{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-04-06T14:01:22.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2026-04-06T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":52427,"title":"ICFP2021 Hole-In-Wall: Solve Problem 4, Score=0, Bonus GLOBALIST assumed","description":"The ICFP held its annual 3-day contest in July 2021 with Hole-In-Wall. Contest Specification.\r\nThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \r\nThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled according to the Specification when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole.  The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\r\nValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u003c= Edges*epsilon/1000000.  Lsqr is length squared.\r\nScore is sum of minimum square distances to the figure from each unique hole vertex. \r\nnpxy=Solve_ICFP004(hxy, pxy, mseg, epsilon)  \r\nThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\r\nThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\r\nThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use Register Team. Anyone can select Problems Page and then click problem numbers to see the puzzles and to download problem files.\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 922px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 461px; transform-origin: 407px 461px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.icfpconference.org/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 148.5px 8px; transform-origin: 148.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e held its annual 3-day contest in July 2021 with \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eHole-In-Wall\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 30.5px 8px; transform-origin: 30.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Contest \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/spec-v4.1.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpecification\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379px 8px; transform-origin: 379px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 168px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 84px; text-align: left; transform-origin: 384px 84px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 381.5px 8px; transform-origin: 381.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled according to the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/spec-v4.1.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpecification\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 332px 8px; transform-origin: 332px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole.  The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u0026lt;= Edges*epsilon/1000000.  Lsqr is length squared.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/52308\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eScore\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 253px 8px; transform-origin: 253px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is sum of minimum square distances to the figure from each unique hole vertex. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 157.5px 8px; transform-origin: 157.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003enpxy=Solve_ICFP004(hxy, pxy, mseg, epsilon)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e  \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 377.5px 8px; transform-origin: 377.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 368.5px 8px; transform-origin: 368.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://poses.live/register\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eRegister Team\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 43.5px 8px; transform-origin: 43.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Anyone can select \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://poses.live/problems\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eProblems Page\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 259px 8px; transform-origin: 259px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and then click problem numbers to see the puzzles and to download problem files.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 358px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 179px; text-align: left; transform-origin: 384px 179px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: top;width: 776px;height: 358px\" 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\" data-image-state=\"image-loaded\" width=\"776\" height=\"358\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function npxy=Solve_ICFP004(hxy,pxy,mseg,epsilon)\r\n% Annealing Solver with Gloabalist Bonus tweak\r\n% Create list of nodes that are placed on hole nodes\r\n% Adjust Segment Max lengths for select segments\r\n% Place hole vertex values into npxy array\r\n% npxy values outside hole are randomly placed inside hole\r\n% as all jiggles are checked for staying in hole\r\n% This routine lacks edge crossing check\r\n% Infinite jiggle traps exist so iterations are limited followed\r\n% by node being randomly placed\r\n% Anneal by moving selected node to/away from goal node with\r\n% directional randomness eg to Bottom Right [1 1;1 0;0 1\r\n% to Top Left the move options would be [-1 -1;-1 0;0 -1]\r\n\r\n npxy=pxy;\r\n np=size(npxy,1); % figure/pose vertices\r\n nhp1=size(hxy,1); % hole vertices\r\n nh=nhp1-1;\r\n nseg=size(mseg,1);\r\n \r\n msegMM=calc_msegMM(pxy,mseg,epsilon,nseg); %Create Min and Max segment integer values\r\n\r\n%By inspection assign these nodes to the hole vertices. See Challenge page Figure \r\n%Revise msegMM for Bonus Stretch of Problem 4\r\n  msegMM([9 46],2)=20*20; % Fit segs 9 and 46 to hole\r\n  msegMM(49,2)=2*msegMM(49,2); %Allow Extend seg 49\r\n  msegMM(50,2)=2*msegMM(50,2); %Allow extend seg 50\r\n%starting at top left\r\n  p2hEdge=[7 27 16 35 42 41 38 23 12 6 5]'; %Assign nodes to hole vertices\r\n  npxy(p2hEdge,:)=hxy(1:nh,:);\r\n \r\n %hplot(hxy,pxy,mseg,nseg,1);\r\n %hplot3(hxy,npxy,mseg,nseg,3,msegMM);\r\n %pause(0.01)\r\n \r\n %Create a simple matrix for indexing in hole positions\r\n %Simple vector calculations determines good/bad node\r\n %May need a -1 check for x or y\r\n xhmax=max(hxy(:,1)); %hole node 0:79 thus 80 47 mod by xchmax\r\n xhmax1=xhmax+1;\r\n yhmax=max(hxy(:,2)); %[x,y]*[1;80]+1 to make in-index\r\n %[x,y]*[1;xhmax+1]+1\r\n dmap=ones(xhmax+1,yhmax+1); % x=column, y=row  hxy(col=x,row=y)\r\n [dx,dy]=find(dmap); %in vector TL2BR across figures L2R,T2B\r\n dxy=[dx dy]-1;\r\n in=inpolygon(dxy(:,1),dxy(:,2),hxy(:,1),hxy(:,2));\r\n hdxy=dxy(:,1)\u003c0; % make logical hdxy of entire board\r\n hdxy(in,:)=1; % [0,0] maps to 1, [1,0] is 2, [79,0] is 80,\r\n %hdxy holds oversized hole map\r\n phdxy=find(hdxy); % Pointer to all in-hole nodes\r\n Lphdxy=nnz(phdxy); % used for outside hole and infinite loops\r\n % new_xy=dxy(phdxy(randi(Lphdxy)),:);\r\n \r\n fprintf('IN-Hole Nodes:%i  HoleV:%i FigV:%i\\n',nnz(hdxy),nh,np);\r\n  \r\n %Problem 4 Solution format in JSON using Bonus from Problem 57\r\n %{\"bonuses\":[{\"bonus\":\"GLOBALIST\",\"problem\":57}],\r\n %\"vertices\": [[0,0],[0,0],[0,0],[0,0],[5,50],[30,70],[0,0],\r\n %...,[73,45]]}\r\n\r\n% ICFP Problem Id 4\r\n% nh 11  np 43 nseg 50\r\n% epsilon=200000;\r\n% hxy=[5 5;35 15;65 15;95 5;95 50;70 70;70 90;50 95;30 90;30 70;\r\n%      5 50;5 5];\r\n% pxy=[10 10;10 25;10 35;15  5;15 30;20 50;30  5;30 30;30 35;30 50;\r\n%      30 55;30 65;35 45;35 60;40  5;40 20;40 30;40 45;40 60;40 80;\r\n%      45 50;45 55;50 95;55 20;55 50;55 55;60  5;60 30;60 35;60 45;\r\n%      60 60;60 80;65 45;65 60;70  5;70 50;70 55;70 65;80 30;80 35;\r\n%      80 50;90 5;90 35];\r\n% mseg=[23 32;32 20;20 23;32 38;38 12;12 20;12 6;38 41;11 10;10 13;13 18;18 21;21 22;22 19;19 14;14 11;34 37;37 36;36 33;33 30;30 25;25 26;26 31;31 34;6 3;43 41;41 36;25 21;10 6;7 4;4 1;1 2;2 5;5 8;15 16;16 17;17 28;28 24;24 27;27 15;16 24;42 39;39 35;8 9;28 29;39 40;43 40;40 29;29 9;9 3];\r\n%Cody mseg cleaned and sorted\r\n%mseg=[1 2;1 4;2 5;3 6;3 9;4 7;5 8;6 10;6 12;8 9;9 29;10 11;10 13;11 14;12 20;12 38;13 18;14 19;15 16;15 27;16 17;16 24;17 28;18 21;19 22;20 23;20 32;21 22;21 25;23 32;24 27;24 28;25 26;25 30;26 31;28 29;29 40;30 33;31 34;32 38;33 36;34 37;35 39;36 37;36 41;38 41;39 40;39 42;40 43;41 43];\r\n  \r\n%  hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%  pause(0.1);\r\n  \r\n  pstatus=zeros(np,1); %Status 0/1/2/3=Final, nseg attached, \r\n  pstatus(p2hEdge,1)=3; % Assign as Fixed node status\r\n  \r\n  %Find all nodes outside hole and randomly place inside\r\n  % should prioitize these but not implemented.\r\n  \r\n  %Find all segments with issues\r\n  %Grab all nodes of problem segments\r\n  %remove nodes that are pstatus==3\r\n  nodechk=inpolygon(npxy(:,1),npxy(:,2),hxy(:,1),hxy(:,2));\r\n  %Find all nodes that are outside hole and randomly place in-hole\r\n  for i=find(~nodechk)'\r\n   npxy(i,:)=[0 0]; %    *****  Line changed from working program  *****\r\n  end\r\n  \r\n  ztic=tic; % while timer\r\n  iter=0; % purely informational\r\n  badnodes=zeros(1,np);\r\n  %Hole intersect check not required for Problem 4 with given Starts\r\n  while toc(ztic)\u003c10 % repeat until anneal solves, should be quick usually \u003c 1.2s\r\n   iter=iter+1; %Tracking number of anneal iterations for info only\r\n   segchk=ones(nseg,1);\r\n   for i=1:nseg % Find bad segments to identify nodes to jiggle\r\n    segchk(i)=prod(msegMM(i,:)-dist2(npxy(mseg(i,1),:),npxy(mseg(i,2),:)) )\u003e0;\r\n   end\r\n   segnodes=mseg(find(segchk),:);\r\n   badnodes(segnodes(:))=1;\r\n   badnodes(pstatus(:,1)==3)=0; %Remove placed nodes from bad list\r\n  \r\n   if nnz(badnodes)==0  % If no badnodes then problem solved\r\n%    hplot3(hxy,npxy,mseg,nseg,4,msegMM); % hplot3 exists below for out of cody usage\r\n   \r\n    vLB2=sum((pxy(mseg(:,1),:)-pxy(mseg(:,2),:)).^2,2); %Method to evaluate GLOBALIST function\r\n    vLN2=sum((npxy(mseg(:,1),:)-npxy(mseg(:,2),:)).^2,2);\r\n    ET=epsilon*nseg/1e6;\r\n    ETseg=[[1:nseg]' mseg vLB2 vLN2 vLN2./vLB2 abs(vLN2./vLB2-1)]; % Info table\r\n%    fprintf('%2i %2i %2i  %4i  %4i   %.3f   %.3f\\n',ETseg')\r\n    fprintf('Cody ET Performance: ET Lim:%.3f  Current ET:%.3f\\n',ET,sum(ETseg(:,end)));\r\n   \r\n    fprintf('Solution found in %i iterations,  %.1fs\\n',iter,toc(ztic));\r\n   \r\n%    pause(0.1);\r\n    return; % No bad nodes , return the solution has been found\r\n   end\r\n  \r\n   setbn=find(badnodes);\r\n   %Perfom jiggle on randomized bad nodes\r\n   for jptr= 0                          % *****  Line changed from working program  *****\r\n    jxy=npxy(jptr,:);\r\n   \r\n    jsegs=[find(mseg(:,1)==jptr);find(mseg(:,2)==jptr)];\r\n    Ljsegs=size(jsegs,1);\r\n    jsegnodes=sum(mseg(jsegs,:)-jptr,2)+jptr;\r\n    jsegxy=npxy(jsegnodes,:);\r\n    \r\n   vjsegs=jsegs*0; %1 is valid\r\n   for i=1:Ljsegs\r\n    vjsegs(i)=prod(msegMM(jsegs(i),:)-sum((jxy-jsegxy(i,:)).^2))\u003c=0;\r\n   end\r\n   if nnz(vjsegs==0)==0\r\n    continue; % can this be reached?  if outside hole placement occurs to good/good\r\n   end % ALL Valid\r\n   \r\n   for i=1:Ljsegs\r\n    if vjsegs(i),continue;end % original length okay \r\n    \r\n     subiter=0; %Break out of jiggle inf loop\r\n    if sum((jxy-jsegxy(i,:)).^2)\u003emsegMM(jsegs(i),2) %too long\r\n     %Perform rand pick of 3 moves until no longer too long\r\n     while sum((jxy-jsegxy(i,:)).^2)\u003emsegMM(jsegs(i),2)\r\n    % Create quadrant directed approach jiggle\r\n      deltaxy=-sign(jxy-jsegxy(i,:)).*[1 1;0 1;1 0];\r\n      if sum(abs(deltaxy(:,1)))==0 % avoid linear inf loop\r\n       deltaxy(:,1)=[0 0 0];  % *****  Line changed from working program  *****\r\n      elseif sum(abs(deltaxy(:,2)))==0\r\n       deltaxy(:,2)=[0 0 0];  % *****  Line changed from working program  *****\r\n      end\r\n      \r\n      % add a random directed deltaxy\r\n      tjxy=jxy+[0 0];          % *****  Line changed from working program  *****\r\n      if ~hdxy(tjxy*[1;xhmax1]+1) %Banging thru wall\r\n       subiter=subiter+1; % Break out of infinite loop       \r\n       if subiter\u003e10\r\n        subiter=0;\r\n        %Place node at random in-hole\r\n        jxy=[0 0];           %  *****  Line changed from working program  *****\r\n        npxy(jptr,:)=jxy;\r\n%       hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%       pause(0.1); \r\n       end\r\n       continue;\r\n      end % stepped out of hole\r\n      jxy=tjxy; % random move in direction goal node\r\n      npxy(jptr,:)=jxy;\r\n       %hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n       %pause(0.1);\r\n     end % while too long\r\n     \r\n    else % must be too short, Push away\r\n     %Perform rand pick of 3 moves until no longer too short\r\n     while sum((jxy-jsegxy(i,:)).^2)\u003cmsegMM(jsegs(i),1)\r\n      deltaxy=sign(jxy-jsegxy(i,:)).*[1 1;0 1;1 0]; %mov deltas\r\n    % Create quadrant directed push away jiggle\r\n      if sum(abs(deltaxy(:,1)))==0 % avoid linear inf loop, no 0 0 move\r\n       deltaxy(:,1)=[0 -1 1];\r\n      elseif sum(abs(deltaxy(:,2)))==0\r\n       deltaxy(:,2)=[0 -1 1];\r\n      end\r\n      \r\n      tjxy=jxy+deltaxy(randi(3),:); % Randomize selection\r\n      if ~hdxy(tjxy*[1;xhmax1]+1) %check jiggle goes outside hole\r\n       subiter=subiter+1; % Break out of locked position\r\n       %Pushing a node into a corner can create inf loop when\r\n       %using directed quadrant push\r\n       if subiter\u003e10\r\n        subiter=0;\r\n        jxy=dxy(phdxy(randi(Lphdxy)),:);  %Place node at random in-hole\r\n        npxy(jptr,:)=jxy;\r\n%       hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%       pause(0.1);\r\n       end\r\n       continue;\r\n      end % stepped out of hole\r\n      jxy=tjxy; % random move in direction goal node\r\n      npxy(jptr,:)=jxy;\r\n       %hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n       %pause(0.1);    \r\n     end % while too short\r\n    \r\n    end %  if Long or Short msegMM\r\n    break; %perform jiggle on only first Lseg  (need to randomize?)\r\n   end % i Ljsegs\r\n  \r\n  end % jptr\r\n   badnodes=badnodes*0; % reset badnodes   \r\n%      hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%      pause(0.1);\r\n  end % while 1\r\n  \r\nend %Solve_ICFP004\r\n\r\nfunction d2=dist2(a,b)\r\n% distance squared a-matrix to b-vector \r\n d2=sum((a-b).^2,2);\r\nend %dist2\r\n\r\nfunction msegMM=calc_msegMM(pxy,mseg,epsilon,nseg)\r\n%determine Min and Max integer value of allowed length squared for each segment\r\n%abs(Lsqr(npxy,seg(i))/Lsqr(pxy,seg(i))-1)\u003c= epsilon/1000000.\r\n%mseg has indices of connected vertices [nseg,2].  The nseg may exceed number of vertices.\r\n msegMM=zeros(nseg,2);\r\n for i=1:nseg\r\n  Lseg=sum((pxy(mseg(i,1),:)-pxy(mseg(i,2),:)).^2);\r\n  delta=floor(epsilon*Lseg/1000000);\r\n  msegMM(i,:)=[-delta delta]+Lseg;\r\n end\r\nend % calc_msegMM\r\n\r\n% function [epsilon,hxy,pxy,mseg]=read_problem(pid)\r\n%  path='D:\\Users\\oglraz\\Documents\\MATLAB\\ICFP\\2021_Hole\\all_problems';\r\n%  fid=fopen([path '\\' num2str(pid) '.problem'],'r');\r\n%   pstr=fgetl(fid);\r\n%  fclose(fid)\r\n%  \r\n%  Lpstr=length(pstr);\r\n%  holidx=findstr('\"hole\":[[',pstr); %starting location match\r\n%  epsidx=findstr('\"epsilon\":',pstr);\r\n%  figidx=findstr(',\"figure\"',pstr);\r\n%  edgidx=findstr('\"edges\":[[',pstr);\r\n%  veridx=findstr('\"vertices\":[[',pstr);\r\n%  epsilon=str2num(pstr(epsidx+10:figidx-1));\r\n%  \r\n%  hxy=reshape(str2num(pstr(holidx+8:epsidx-3)),2,[])';\r\n%  hxy=[hxy;hxy(1,:)]; %repeat row1 to close path\r\n%  \r\n%  pxy=reshape(str2num(pstr(veridx+12:Lpstr-3)),2,[])';\r\n%  \r\n%  mseg=reshape(str2num(pstr(edgidx+9:veridx-3)),2,[])'+1;\r\n% end % read_problem\r\n\r\n%Problem 4 JSON\r\n %{\"bonuses\":[{\"bonus\":\"BREAK_A_LEG\",\"problem\":63,\"position\":[95,26]},\r\n %{\"bonus\":\"BREAK_A_LEG\",\"problem\":92,\"position\":[5,32]}],\r\n %\"hole\":[[5,5],[35,15],[65,15],[95,5],[95,50],[70,70],[70,90],[50,95],\r\n %[30,90],[30,70],[5,50]],\"epsilon\":200000,\"figure\":{\r\n %\"edges\":[[22,31],[31,19],[19,22],[31,37],[37,11],[11,19],[11,5],\r\n %[37,40],[10,9],[9,12],[12,17],[17,20],[20,21],[21,18],[18,13],\r\n %[13,10],[33,36],[36,35],[35,32],[32,29],[29,24],[24,25],[25,30],\r\n %[30,33],[5,2],[42,40],[40,35],[24,20],[9,5],[6,3],[3,0],[0,1],[1,4],\r\n %[4,7],[14,15],[15,16],[16,27],[27,23],[23,26],[26,14],[15,23],[41,38],\r\n %[38,34],[7,8],[27,28],[38,39],[42,39],[39,28],[28,8],[8,2]],\r\n %\"vertices\":[[10,10],[10,25],[10,35],[15,5],[15,30],[20,50],[30,5],\r\n %[30,30],[30,35],[30,50],[30,55],[30,65],[35,45],[35,60],[40,5],[40,20],\r\n %[40,30],[40,45],[40,60],[40,80],[45,50],[45,55],[50,95],[55,20],\r\n %[55,50],[55,55],[60,5],[60,30],[60,35],[60,45],[60,60],[60,80],\r\n %[65,45],[65,60],[70,5],[70,50],[70,55],[70,65],[80,30],[80,35],\r\n %[80,50],[90,5],[90,35]]}}\r\n\r\n% function write_bonus_submission(npxy,pid,bonus_type,bonus_prob)\r\n%  fname=['Solution_' num2str(pid) '_' datestr(now,'yyyymmdd_HHMMSS') '.txt'];\r\n%  %fn=['zH' datestr(now,'yyyymmdd_HHMMSS') '.html'];\r\n%  fid=fopen(fname,'wt'); % t for notepad editing\r\n%  \r\n%  %Create ICFP submission with a bonus\r\n%  fprintf('{\"bonuses\":[{\"bonus\":\"%s\",\"problem\":%s}],',bonus_type,num2str(bonus_prob));\r\n%  fprintf(fid,'{\"bonuses\":[{\"bonus\":\"%s\",\"problem\":%s}],',bonus_type,num2str(bonus_prob));\r\n%  fprintf('\"vertices\": [');\r\n%  fprintf(fid,'\"vertices\": [');\r\n%  for i=1:size(npxy,1)-1 \r\n%   fprintf('[%i,%i],',npxy(i,:));\r\n%   fprintf(fid,'[%i,%i],',npxy(i,:));\r\n%  end \r\n%  fprintf('[%i,%i]]}\\n',npxy(end,:));\r\n%  fprintf(fid,'[%i,%i]]}\\n',npxy(end,:));\r\n%  fclose(fid);\r\n% end %write_submission_bonus\r\n% \r\n% \r\n% \r\n% function hplot3(vxy,qxy,mseg,Lmseg,id,segMM)\r\n%  segMNM=[segMM(:,1) segMM(:,1)+segMM(:,2) segMM(:,2)];\r\n%  [in] = inpolygon(qxy(:,1),qxy(:,2),vxy(:,1),vxy(:,2)); % inside or on edge\r\n%  figure(id)\r\n%   plot(vxy(:,1),vxy(:,2),'k.-') % hole polygon\r\n%   hold on\r\n%   plot(qxy(in,1),qxy(in,2),'b*') % points inside\r\n%   plot(qxy(~in,1),qxy(~in,2),'ro') % points outside\r\n%   for i=1:size(qxy,1) %length(xq)\r\n%    text(qxy(i,1)+.75,qxy(i,2)-1.5,num2str(i));\r\n%   end\r\n%   \r\n%   for i=1:Lmseg\r\n%    d2seg=(qxy(mseg(i,1),1)-qxy(mseg(i,2),1))^2+(qxy(mseg(i,1),2)-qxy(mseg(i,2),2))^2;\r\n%    if d2seg\u003csegMNM(i,1)\r\n%      plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'b-') % segment too short\r\n%    elseif d2seg\u003esegMNM(i,3)\r\n%      plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'r-') % segment too long\r\n%    else\r\n%     plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'g-') \r\n%    end\r\n%    text(sum(qxy(mseg(i,:),1))/2,sum(qxy(mseg(i,:),2))/2,num2str(i),'Color','b');\r\n%   end\r\n%   \r\n%   %o+*.x_|sd^v\u003e\u003cph\r\n%   %colors ymcrgbwk\r\n%   \r\n%   axis tight\r\n%   axis ij\r\n%   hold off\r\n%end % hplot3\r\n\r\n","test_suite":"%%\r\n% ICFP Problem  4  \r\n% Assume that globalist bonus is enabled so the strict edge lengths are not required\r\n%\r\n%Problem 4 Solution format in JSON using Bonus from Problem 57\r\n%{\"bonuses\":[{\"bonus\":\"GLOBALIST\",\"problem\":57}],\r\n%\"vertices\": [[0,0],[0,0],[0,0],[0,0],[5,50],[30,70],[0,0],\r\n%...,[73,45]]}\r\ntic\r\n% ICFP Problem Id 4\r\n% nh 11  np 43 nseg 50\r\nepsilon=200000;\r\nhxy=[5 5;35 15;65 15;95 5;95 50;70 70;70 90;50 95;30 90;30 70;5 50;5 5];\r\npxy=[10 10;10 25;10 35;15 5;15 30;20 50;30 5;30 30;30 35;30 50;30 55;30 65;35 45;35 60;40 5;40 20;40 30;40 45;40 60;40 80;45 50;45 55;50 95;55 20;55 50;55 55;60 5;60 30;60 35;60 45;60 60;60 80;65 45;65 60;70 5;70 50;70 55;70 65;80 30;80 35;80 50;90 5;90 35];\r\nmseg=[1 2;1 4;2 5;3 6;3 9;4 7;5 8;6 10;6 12;8  9;9 29;10 11;10 13;11 14;12 20;12 38;13 18;14 19;15 16;15 27;16 17;16 24;17 28;18 21;19 22;20 23;20 32;21 22;21 25;23 32;24 27;24 28;25 26;25 30;26 31;28 29;29 40;30 33;31 34;32 38;33 36;34 37;35 39;36 37;36 41;38 41;39 40;39 42;40 43;41 43];\r\nnseg=size(mseg,1);\r\nnpxy=Solve_ICFP004(hxy,pxy,mseg,epsilon);\r\nvalid=isequal(npxy,round(npxy));\r\nvalid=valid*isequal(size(npxy),size(pxy));\r\nfor i=1:size(hxy,1) % verify all holes covered\r\n valid=valid*(min(sum(abs(npxy-hxy(i,:)),2))==0);\r\nend\r\nin=inpolygon(npxy(:,1),npxy(:,2),hxy(:,1),hxy(:,2));\r\nvalid=valid*(nnz(in==0)==0);\r\n\r\nvLB2=sum((pxy(mseg(:,1),:)-pxy(mseg(:,2),:)).^2,2); % Base seg d2\r\nvLN2=sum((npxy(mseg(:,1),:)-npxy(mseg(:,2),:)).^2,2); % New seg d2\r\nET=epsilon*nseg/1e6; %Total allowed stretch  10.00\r\nETseg=abs(vLN2./vLB2-1);\r\nETP=sum(ETseg);\r\nvalid=valid*(ETP\u003c=ET);\r\n\r\nfprintf('ET Lim:%.3f  Current ET:%.3f\\n',ET,ETP);\r\nfprintf('%i %i\\n',npxy');\r\ntoc\r\nassert(isequal(valid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2022-09-13T15:37:03.000Z","deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-08-05T03:23:08.000Z","updated_at":"2022-09-13T15:37:03.000Z","published_at":"2021-08-05T04:53:23.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.icfpconference.org/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e held its annual 3-day contest in July 2021 with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eHole-In-Wall\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Contest \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/spec-v4.1.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpecification\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled according to the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/spec-v4.1.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpecification\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole.  The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u0026lt;= Edges*epsilon/1000000.  Lsqr is length squared.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/52308\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eScore\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is sum of minimum square distances to the figure from each unique hole vertex. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enpxy=Solve_ICFP004(hxy, pxy, mseg, epsilon)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e  \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://poses.live/register\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRegister Team\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Anyone can select \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://poses.live/problems\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblems Page\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and then click problem numbers to see the puzzles and to download problem files.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"358\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"776\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"top\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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Hole-In-Wall: Solve Problem 4, Score=0, Bonus GLOBALIST assumed","description":"The ICFP held its annual 3-day contest in July 2021 with Hole-In-Wall. Contest Specification.\r\nThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \r\nThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled according to the Specification when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole.  The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\r\nValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u003c= Edges*epsilon/1000000.  Lsqr is length squared.\r\nScore is sum of minimum square distances to the figure from each unique hole vertex. \r\nnpxy=Solve_ICFP004(hxy, pxy, mseg, epsilon)  \r\nThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\r\nThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\r\nThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use Register Team. Anyone can select Problems Page and then click problem numbers to see the puzzles and to download problem files.\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 922px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 461px; transform-origin: 407px 461px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.5px 8px; transform-origin: 14.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.icfpconference.org/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eICFP\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 148.5px 8px; transform-origin: 148.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e held its annual 3-day contest in July 2021 with \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eHole-In-Wall\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 30.5px 8px; transform-origin: 30.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Contest \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/spec-v4.1.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpecification\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379px 8px; transform-origin: 379px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 168px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 84px; text-align: left; transform-origin: 384px 84px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 381.5px 8px; transform-origin: 381.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled according to the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://icfpcontest2021.github.io/spec-v4.1.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSpecification\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 332px 8px; transform-origin: 332px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole.  The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u0026lt;= Edges*epsilon/1000000.  Lsqr is length squared.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/52308\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eScore\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 253px 8px; transform-origin: 253px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is sum of minimum square distances to the figure from each unique hole vertex. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 157.5px 8px; transform-origin: 157.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003enpxy=Solve_ICFP004(hxy, pxy, mseg, epsilon)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e  \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 377.5px 8px; transform-origin: 377.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 368.5px 8px; transform-origin: 368.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://poses.live/register\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eRegister Team\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 43.5px 8px; transform-origin: 43.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Anyone can select \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://poses.live/problems\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eProblems Page\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 259px 8px; transform-origin: 259px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and then click problem numbers to see the puzzles and to download problem files.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 358px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 179px; text-align: left; transform-origin: 384px 179px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: top;width: 776px;height: 358px\" 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\" data-image-state=\"image-loaded\" width=\"776\" height=\"358\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function npxy=Solve_ICFP004(hxy,pxy,mseg,epsilon)\r\n% Annealing Solver with Gloabalist Bonus tweak\r\n% Create list of nodes that are placed on hole nodes\r\n% Adjust Segment Max lengths for select segments\r\n% Place hole vertex values into npxy array\r\n% npxy values outside hole are randomly placed inside hole\r\n% as all jiggles are checked for staying in hole\r\n% This routine lacks edge crossing check\r\n% Infinite jiggle traps exist so iterations are limited followed\r\n% by node being randomly placed\r\n% Anneal by moving selected node to/away from goal node with\r\n% directional randomness eg to Bottom Right [1 1;1 0;0 1\r\n% to Top Left the move options would be [-1 -1;-1 0;0 -1]\r\n\r\n npxy=pxy;\r\n np=size(npxy,1); % figure/pose vertices\r\n nhp1=size(hxy,1); % hole vertices\r\n nh=nhp1-1;\r\n nseg=size(mseg,1);\r\n \r\n msegMM=calc_msegMM(pxy,mseg,epsilon,nseg); %Create Min and Max segment integer values\r\n\r\n%By inspection assign these nodes to the hole vertices. See Challenge page Figure \r\n%Revise msegMM for Bonus Stretch of Problem 4\r\n  msegMM([9 46],2)=20*20; % Fit segs 9 and 46 to hole\r\n  msegMM(49,2)=2*msegMM(49,2); %Allow Extend seg 49\r\n  msegMM(50,2)=2*msegMM(50,2); %Allow extend seg 50\r\n%starting at top left\r\n  p2hEdge=[7 27 16 35 42 41 38 23 12 6 5]'; %Assign nodes to hole vertices\r\n  npxy(p2hEdge,:)=hxy(1:nh,:);\r\n \r\n %hplot(hxy,pxy,mseg,nseg,1);\r\n %hplot3(hxy,npxy,mseg,nseg,3,msegMM);\r\n %pause(0.01)\r\n \r\n %Create a simple matrix for indexing in hole positions\r\n %Simple vector calculations determines good/bad node\r\n %May need a -1 check for x or y\r\n xhmax=max(hxy(:,1)); %hole node 0:79 thus 80 47 mod by xchmax\r\n xhmax1=xhmax+1;\r\n yhmax=max(hxy(:,2)); %[x,y]*[1;80]+1 to make in-index\r\n %[x,y]*[1;xhmax+1]+1\r\n dmap=ones(xhmax+1,yhmax+1); % x=column, y=row  hxy(col=x,row=y)\r\n [dx,dy]=find(dmap); %in vector TL2BR across figures L2R,T2B\r\n dxy=[dx dy]-1;\r\n in=inpolygon(dxy(:,1),dxy(:,2),hxy(:,1),hxy(:,2));\r\n hdxy=dxy(:,1)\u003c0; % make logical hdxy of entire board\r\n hdxy(in,:)=1; % [0,0] maps to 1, [1,0] is 2, [79,0] is 80,\r\n %hdxy holds oversized hole map\r\n phdxy=find(hdxy); % Pointer to all in-hole nodes\r\n Lphdxy=nnz(phdxy); % used for outside hole and infinite loops\r\n % new_xy=dxy(phdxy(randi(Lphdxy)),:);\r\n \r\n fprintf('IN-Hole Nodes:%i  HoleV:%i FigV:%i\\n',nnz(hdxy),nh,np);\r\n  \r\n %Problem 4 Solution format in JSON using Bonus from Problem 57\r\n %{\"bonuses\":[{\"bonus\":\"GLOBALIST\",\"problem\":57}],\r\n %\"vertices\": [[0,0],[0,0],[0,0],[0,0],[5,50],[30,70],[0,0],\r\n %...,[73,45]]}\r\n\r\n% ICFP Problem Id 4\r\n% nh 11  np 43 nseg 50\r\n% epsilon=200000;\r\n% hxy=[5 5;35 15;65 15;95 5;95 50;70 70;70 90;50 95;30 90;30 70;\r\n%      5 50;5 5];\r\n% pxy=[10 10;10 25;10 35;15  5;15 30;20 50;30  5;30 30;30 35;30 50;\r\n%      30 55;30 65;35 45;35 60;40  5;40 20;40 30;40 45;40 60;40 80;\r\n%      45 50;45 55;50 95;55 20;55 50;55 55;60  5;60 30;60 35;60 45;\r\n%      60 60;60 80;65 45;65 60;70  5;70 50;70 55;70 65;80 30;80 35;\r\n%      80 50;90 5;90 35];\r\n% mseg=[23 32;32 20;20 23;32 38;38 12;12 20;12 6;38 41;11 10;10 13;13 18;18 21;21 22;22 19;19 14;14 11;34 37;37 36;36 33;33 30;30 25;25 26;26 31;31 34;6 3;43 41;41 36;25 21;10 6;7 4;4 1;1 2;2 5;5 8;15 16;16 17;17 28;28 24;24 27;27 15;16 24;42 39;39 35;8 9;28 29;39 40;43 40;40 29;29 9;9 3];\r\n%Cody mseg cleaned and sorted\r\n%mseg=[1 2;1 4;2 5;3 6;3 9;4 7;5 8;6 10;6 12;8 9;9 29;10 11;10 13;11 14;12 20;12 38;13 18;14 19;15 16;15 27;16 17;16 24;17 28;18 21;19 22;20 23;20 32;21 22;21 25;23 32;24 27;24 28;25 26;25 30;26 31;28 29;29 40;30 33;31 34;32 38;33 36;34 37;35 39;36 37;36 41;38 41;39 40;39 42;40 43;41 43];\r\n  \r\n%  hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%  pause(0.1);\r\n  \r\n  pstatus=zeros(np,1); %Status 0/1/2/3=Final, nseg attached, \r\n  pstatus(p2hEdge,1)=3; % Assign as Fixed node status\r\n  \r\n  %Find all nodes outside hole and randomly place inside\r\n  % should prioitize these but not implemented.\r\n  \r\n  %Find all segments with issues\r\n  %Grab all nodes of problem segments\r\n  %remove nodes that are pstatus==3\r\n  nodechk=inpolygon(npxy(:,1),npxy(:,2),hxy(:,1),hxy(:,2));\r\n  %Find all nodes that are outside hole and randomly place in-hole\r\n  for i=find(~nodechk)'\r\n   npxy(i,:)=[0 0]; %    *****  Line changed from working program  *****\r\n  end\r\n  \r\n  ztic=tic; % while timer\r\n  iter=0; % purely informational\r\n  badnodes=zeros(1,np);\r\n  %Hole intersect check not required for Problem 4 with given Starts\r\n  while toc(ztic)\u003c10 % repeat until anneal solves, should be quick usually \u003c 1.2s\r\n   iter=iter+1; %Tracking number of anneal iterations for info only\r\n   segchk=ones(nseg,1);\r\n   for i=1:nseg % Find bad segments to identify nodes to jiggle\r\n    segchk(i)=prod(msegMM(i,:)-dist2(npxy(mseg(i,1),:),npxy(mseg(i,2),:)) )\u003e0;\r\n   end\r\n   segnodes=mseg(find(segchk),:);\r\n   badnodes(segnodes(:))=1;\r\n   badnodes(pstatus(:,1)==3)=0; %Remove placed nodes from bad list\r\n  \r\n   if nnz(badnodes)==0  % If no badnodes then problem solved\r\n%    hplot3(hxy,npxy,mseg,nseg,4,msegMM); % hplot3 exists below for out of cody usage\r\n   \r\n    vLB2=sum((pxy(mseg(:,1),:)-pxy(mseg(:,2),:)).^2,2); %Method to evaluate GLOBALIST function\r\n    vLN2=sum((npxy(mseg(:,1),:)-npxy(mseg(:,2),:)).^2,2);\r\n    ET=epsilon*nseg/1e6;\r\n    ETseg=[[1:nseg]' mseg vLB2 vLN2 vLN2./vLB2 abs(vLN2./vLB2-1)]; % Info table\r\n%    fprintf('%2i %2i %2i  %4i  %4i   %.3f   %.3f\\n',ETseg')\r\n    fprintf('Cody ET Performance: ET Lim:%.3f  Current ET:%.3f\\n',ET,sum(ETseg(:,end)));\r\n   \r\n    fprintf('Solution found in %i iterations,  %.1fs\\n',iter,toc(ztic));\r\n   \r\n%    pause(0.1);\r\n    return; % No bad nodes , return the solution has been found\r\n   end\r\n  \r\n   setbn=find(badnodes);\r\n   %Perfom jiggle on randomized bad nodes\r\n   for jptr= 0                          % *****  Line changed from working program  *****\r\n    jxy=npxy(jptr,:);\r\n   \r\n    jsegs=[find(mseg(:,1)==jptr);find(mseg(:,2)==jptr)];\r\n    Ljsegs=size(jsegs,1);\r\n    jsegnodes=sum(mseg(jsegs,:)-jptr,2)+jptr;\r\n    jsegxy=npxy(jsegnodes,:);\r\n    \r\n   vjsegs=jsegs*0; %1 is valid\r\n   for i=1:Ljsegs\r\n    vjsegs(i)=prod(msegMM(jsegs(i),:)-sum((jxy-jsegxy(i,:)).^2))\u003c=0;\r\n   end\r\n   if nnz(vjsegs==0)==0\r\n    continue; % can this be reached?  if outside hole placement occurs to good/good\r\n   end % ALL Valid\r\n   \r\n   for i=1:Ljsegs\r\n    if vjsegs(i),continue;end % original length okay \r\n    \r\n     subiter=0; %Break out of jiggle inf loop\r\n    if sum((jxy-jsegxy(i,:)).^2)\u003emsegMM(jsegs(i),2) %too long\r\n     %Perform rand pick of 3 moves until no longer too long\r\n     while sum((jxy-jsegxy(i,:)).^2)\u003emsegMM(jsegs(i),2)\r\n    % Create quadrant directed approach jiggle\r\n      deltaxy=-sign(jxy-jsegxy(i,:)).*[1 1;0 1;1 0];\r\n      if sum(abs(deltaxy(:,1)))==0 % avoid linear inf loop\r\n       deltaxy(:,1)=[0 0 0];  % *****  Line changed from working program  *****\r\n      elseif sum(abs(deltaxy(:,2)))==0\r\n       deltaxy(:,2)=[0 0 0];  % *****  Line changed from working program  *****\r\n      end\r\n      \r\n      % add a random directed deltaxy\r\n      tjxy=jxy+[0 0];          % *****  Line changed from working program  *****\r\n      if ~hdxy(tjxy*[1;xhmax1]+1) %Banging thru wall\r\n       subiter=subiter+1; % Break out of infinite loop       \r\n       if subiter\u003e10\r\n        subiter=0;\r\n        %Place node at random in-hole\r\n        jxy=[0 0];           %  *****  Line changed from working program  *****\r\n        npxy(jptr,:)=jxy;\r\n%       hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%       pause(0.1); \r\n       end\r\n       continue;\r\n      end % stepped out of hole\r\n      jxy=tjxy; % random move in direction goal node\r\n      npxy(jptr,:)=jxy;\r\n       %hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n       %pause(0.1);\r\n     end % while too long\r\n     \r\n    else % must be too short, Push away\r\n     %Perform rand pick of 3 moves until no longer too short\r\n     while sum((jxy-jsegxy(i,:)).^2)\u003cmsegMM(jsegs(i),1)\r\n      deltaxy=sign(jxy-jsegxy(i,:)).*[1 1;0 1;1 0]; %mov deltas\r\n    % Create quadrant directed push away jiggle\r\n      if sum(abs(deltaxy(:,1)))==0 % avoid linear inf loop, no 0 0 move\r\n       deltaxy(:,1)=[0 -1 1];\r\n      elseif sum(abs(deltaxy(:,2)))==0\r\n       deltaxy(:,2)=[0 -1 1];\r\n      end\r\n      \r\n      tjxy=jxy+deltaxy(randi(3),:); % Randomize selection\r\n      if ~hdxy(tjxy*[1;xhmax1]+1) %check jiggle goes outside hole\r\n       subiter=subiter+1; % Break out of locked position\r\n       %Pushing a node into a corner can create inf loop when\r\n       %using directed quadrant push\r\n       if subiter\u003e10\r\n        subiter=0;\r\n        jxy=dxy(phdxy(randi(Lphdxy)),:);  %Place node at random in-hole\r\n        npxy(jptr,:)=jxy;\r\n%       hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%       pause(0.1);\r\n       end\r\n       continue;\r\n      end % stepped out of hole\r\n      jxy=tjxy; % random move in direction goal node\r\n      npxy(jptr,:)=jxy;\r\n       %hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n       %pause(0.1);    \r\n     end % while too short\r\n    \r\n    end %  if Long or Short msegMM\r\n    break; %perform jiggle on only first Lseg  (need to randomize?)\r\n   end % i Ljsegs\r\n  \r\n  end % jptr\r\n   badnodes=badnodes*0; % reset badnodes   \r\n%      hplot3(hxy,npxy,mseg,nseg,4,msegMM);\r\n%      pause(0.1);\r\n  end % while 1\r\n  \r\nend %Solve_ICFP004\r\n\r\nfunction d2=dist2(a,b)\r\n% distance squared a-matrix to b-vector \r\n d2=sum((a-b).^2,2);\r\nend %dist2\r\n\r\nfunction msegMM=calc_msegMM(pxy,mseg,epsilon,nseg)\r\n%determine Min and Max integer value of allowed length squared for each segment\r\n%abs(Lsqr(npxy,seg(i))/Lsqr(pxy,seg(i))-1)\u003c= epsilon/1000000.\r\n%mseg has indices of connected vertices [nseg,2].  The nseg may exceed number of vertices.\r\n msegMM=zeros(nseg,2);\r\n for i=1:nseg\r\n  Lseg=sum((pxy(mseg(i,1),:)-pxy(mseg(i,2),:)).^2);\r\n  delta=floor(epsilon*Lseg/1000000);\r\n  msegMM(i,:)=[-delta delta]+Lseg;\r\n end\r\nend % calc_msegMM\r\n\r\n% function [epsilon,hxy,pxy,mseg]=read_problem(pid)\r\n%  path='D:\\Users\\oglraz\\Documents\\MATLAB\\ICFP\\2021_Hole\\all_problems';\r\n%  fid=fopen([path '\\' num2str(pid) '.problem'],'r');\r\n%   pstr=fgetl(fid);\r\n%  fclose(fid)\r\n%  \r\n%  Lpstr=length(pstr);\r\n%  holidx=findstr('\"hole\":[[',pstr); %starting location match\r\n%  epsidx=findstr('\"epsilon\":',pstr);\r\n%  figidx=findstr(',\"figure\"',pstr);\r\n%  edgidx=findstr('\"edges\":[[',pstr);\r\n%  veridx=findstr('\"vertices\":[[',pstr);\r\n%  epsilon=str2num(pstr(epsidx+10:figidx-1));\r\n%  \r\n%  hxy=reshape(str2num(pstr(holidx+8:epsidx-3)),2,[])';\r\n%  hxy=[hxy;hxy(1,:)]; %repeat row1 to close path\r\n%  \r\n%  pxy=reshape(str2num(pstr(veridx+12:Lpstr-3)),2,[])';\r\n%  \r\n%  mseg=reshape(str2num(pstr(edgidx+9:veridx-3)),2,[])'+1;\r\n% end % read_problem\r\n\r\n%Problem 4 JSON\r\n %{\"bonuses\":[{\"bonus\":\"BREAK_A_LEG\",\"problem\":63,\"position\":[95,26]},\r\n %{\"bonus\":\"BREAK_A_LEG\",\"problem\":92,\"position\":[5,32]}],\r\n %\"hole\":[[5,5],[35,15],[65,15],[95,5],[95,50],[70,70],[70,90],[50,95],\r\n %[30,90],[30,70],[5,50]],\"epsilon\":200000,\"figure\":{\r\n %\"edges\":[[22,31],[31,19],[19,22],[31,37],[37,11],[11,19],[11,5],\r\n %[37,40],[10,9],[9,12],[12,17],[17,20],[20,21],[21,18],[18,13],\r\n %[13,10],[33,36],[36,35],[35,32],[32,29],[29,24],[24,25],[25,30],\r\n %[30,33],[5,2],[42,40],[40,35],[24,20],[9,5],[6,3],[3,0],[0,1],[1,4],\r\n %[4,7],[14,15],[15,16],[16,27],[27,23],[23,26],[26,14],[15,23],[41,38],\r\n %[38,34],[7,8],[27,28],[38,39],[42,39],[39,28],[28,8],[8,2]],\r\n %\"vertices\":[[10,10],[10,25],[10,35],[15,5],[15,30],[20,50],[30,5],\r\n %[30,30],[30,35],[30,50],[30,55],[30,65],[35,45],[35,60],[40,5],[40,20],\r\n %[40,30],[40,45],[40,60],[40,80],[45,50],[45,55],[50,95],[55,20],\r\n %[55,50],[55,55],[60,5],[60,30],[60,35],[60,45],[60,60],[60,80],\r\n %[65,45],[65,60],[70,5],[70,50],[70,55],[70,65],[80,30],[80,35],\r\n %[80,50],[90,5],[90,35]]}}\r\n\r\n% function write_bonus_submission(npxy,pid,bonus_type,bonus_prob)\r\n%  fname=['Solution_' num2str(pid) '_' datestr(now,'yyyymmdd_HHMMSS') '.txt'];\r\n%  %fn=['zH' datestr(now,'yyyymmdd_HHMMSS') '.html'];\r\n%  fid=fopen(fname,'wt'); % t for notepad editing\r\n%  \r\n%  %Create ICFP submission with a bonus\r\n%  fprintf('{\"bonuses\":[{\"bonus\":\"%s\",\"problem\":%s}],',bonus_type,num2str(bonus_prob));\r\n%  fprintf(fid,'{\"bonuses\":[{\"bonus\":\"%s\",\"problem\":%s}],',bonus_type,num2str(bonus_prob));\r\n%  fprintf('\"vertices\": [');\r\n%  fprintf(fid,'\"vertices\": [');\r\n%  for i=1:size(npxy,1)-1 \r\n%   fprintf('[%i,%i],',npxy(i,:));\r\n%   fprintf(fid,'[%i,%i],',npxy(i,:));\r\n%  end \r\n%  fprintf('[%i,%i]]}\\n',npxy(end,:));\r\n%  fprintf(fid,'[%i,%i]]}\\n',npxy(end,:));\r\n%  fclose(fid);\r\n% end %write_submission_bonus\r\n% \r\n% \r\n% \r\n% function hplot3(vxy,qxy,mseg,Lmseg,id,segMM)\r\n%  segMNM=[segMM(:,1) segMM(:,1)+segMM(:,2) segMM(:,2)];\r\n%  [in] = inpolygon(qxy(:,1),qxy(:,2),vxy(:,1),vxy(:,2)); % inside or on edge\r\n%  figure(id)\r\n%   plot(vxy(:,1),vxy(:,2),'k.-') % hole polygon\r\n%   hold on\r\n%   plot(qxy(in,1),qxy(in,2),'b*') % points inside\r\n%   plot(qxy(~in,1),qxy(~in,2),'ro') % points outside\r\n%   for i=1:size(qxy,1) %length(xq)\r\n%    text(qxy(i,1)+.75,qxy(i,2)-1.5,num2str(i));\r\n%   end\r\n%   \r\n%   for i=1:Lmseg\r\n%    d2seg=(qxy(mseg(i,1),1)-qxy(mseg(i,2),1))^2+(qxy(mseg(i,1),2)-qxy(mseg(i,2),2))^2;\r\n%    if d2seg\u003csegMNM(i,1)\r\n%      plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'b-') % segment too short\r\n%    elseif d2seg\u003esegMNM(i,3)\r\n%      plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'r-') % segment too long\r\n%    else\r\n%     plot(qxy(mseg(i,:),1),qxy(mseg(i,:),2),'g-') \r\n%    end\r\n%    text(sum(qxy(mseg(i,:),1))/2,sum(qxy(mseg(i,:),2))/2,num2str(i),'Color','b');\r\n%   end\r\n%   \r\n%   %o+*.x_|sd^v\u003e\u003cph\r\n%   %colors ymcrgbwk\r\n%   \r\n%   axis tight\r\n%   axis ij\r\n%   hold off\r\n%end % hplot3\r\n\r\n","test_suite":"%%\r\n% ICFP Problem  4  \r\n% Assume that globalist bonus is enabled so the strict edge lengths are not required\r\n%\r\n%Problem 4 Solution format in JSON using Bonus from Problem 57\r\n%{\"bonuses\":[{\"bonus\":\"GLOBALIST\",\"problem\":57}],\r\n%\"vertices\": [[0,0],[0,0],[0,0],[0,0],[5,50],[30,70],[0,0],\r\n%...,[73,45]]}\r\ntic\r\n% ICFP Problem Id 4\r\n% nh 11  np 43 nseg 50\r\nepsilon=200000;\r\nhxy=[5 5;35 15;65 15;95 5;95 50;70 70;70 90;50 95;30 90;30 70;5 50;5 5];\r\npxy=[10 10;10 25;10 35;15 5;15 30;20 50;30 5;30 30;30 35;30 50;30 55;30 65;35 45;35 60;40 5;40 20;40 30;40 45;40 60;40 80;45 50;45 55;50 95;55 20;55 50;55 55;60 5;60 30;60 35;60 45;60 60;60 80;65 45;65 60;70 5;70 50;70 55;70 65;80 30;80 35;80 50;90 5;90 35];\r\nmseg=[1 2;1 4;2 5;3 6;3 9;4 7;5 8;6 10;6 12;8  9;9 29;10 11;10 13;11 14;12 20;12 38;13 18;14 19;15 16;15 27;16 17;16 24;17 28;18 21;19 22;20 23;20 32;21 22;21 25;23 32;24 27;24 28;25 26;25 30;26 31;28 29;29 40;30 33;31 34;32 38;33 36;34 37;35 39;36 37;36 41;38 41;39 40;39 42;40 43;41 43];\r\nnseg=size(mseg,1);\r\nnpxy=Solve_ICFP004(hxy,pxy,mseg,epsilon);\r\nvalid=isequal(npxy,round(npxy));\r\nvalid=valid*isequal(size(npxy),size(pxy));\r\nfor i=1:size(hxy,1) % verify all holes covered\r\n valid=valid*(min(sum(abs(npxy-hxy(i,:)),2))==0);\r\nend\r\nin=inpolygon(npxy(:,1),npxy(:,2),hxy(:,1),hxy(:,2));\r\nvalid=valid*(nnz(in==0)==0);\r\n\r\nvLB2=sum((pxy(mseg(:,1),:)-pxy(mseg(:,2),:)).^2,2); % Base seg d2\r\nvLN2=sum((npxy(mseg(:,1),:)-npxy(mseg(:,2),:)).^2,2); % New seg d2\r\nET=epsilon*nseg/1e6; %Total allowed stretch  10.00\r\nETseg=abs(vLN2./vLB2-1);\r\nETP=sum(ETseg);\r\nvalid=valid*(ETP\u003c=ET);\r\n\r\nfprintf('ET Lim:%.3f  Current ET:%.3f\\n',ET,ETP);\r\nfprintf('%i %i\\n',npxy');\r\ntoc\r\nassert(isequal(valid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2022-09-13T15:37:03.000Z","deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-08-05T03:23:08.000Z","updated_at":"2022-09-13T15:37:03.000Z","published_at":"2021-08-05T04:53:23.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.icfpconference.org/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eICFP\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e held its annual 3-day contest in July 2021 with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eHole-In-Wall\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Contest \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/spec-v4.1.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpecification\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe contest folds the figure in Red to fit within the hole shown in light grey. The starting node/seg map to show guesses. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to solve ICFP problems 4 assuming the Bonus from Problem 57 of GLOBALIST is enabled according to the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://icfpcontest2021.github.io/spec-v4.1.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSpecification\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e when given the hole vertices in hxy, original figure vertices in pxy, segment matrix mseg, and epsilon. The hxy matrix is [N+1,2] where N is number of hole vertices. A repeat of the first vertex occurs for drawing the hole.  The pxy(original) and npxy(final) matrices are [P,2] where P is the number of figure vertices. The mseg indicates connected vertices that must maintain a length as a function of epsilon from the original length. The final figure vertices must be integer thus the allowed fuzziness of segment lengths. The GLOBALIST bonus allows individual segments to be over stretch/compressed as long as the total stretch delta per the Specification is not excessive.  The next Challenge will be to solve Problem 57 using recursion to unlock GLOBALIST for problem 4.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eValid is 1) all npxy vertices on or inside the hole, hxy 2) GLOBALIST:sum lengths squared of npxy segments normalized are under pxy segments within an allowed epsilon, sum(abs(Lsqr(npxy,seg(i,:))/Lsqr(pxy,seg(i,:))-1))\u0026lt;= Edges*epsilon/1000000.  Lsqr is length squared.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/52308\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eScore\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is sum of minimum square distances to the figure from each unique hole vertex. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enpxy=Solve_ICFP004(hxy, pxy, mseg, epsilon)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e  \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge requires a Score of zero. A starting set of nodes to place on holes is provided along with a suggestion of Segments to stretch.  One method is to anneal the points until lengths match the revised maximums. Annealing employs random point movement until a condition is met.  Protections against INF loops are required as annealing may get stuck.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe function template includes routines to read ICFP problem files, write ICFP solution files using Bonuses, and plots.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe ICFP 2021 Hole In Wall contest site has enabled a public user login to allow submissions. A login must be created to access all the problems and to submit solutions. Solutions are simple text files. Other challenges will show reading files, drawing figures, and producing submission files. To fully access the ICFP/Problems site use \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://poses.live/register\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRegister Team\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. Anyone can select \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://poses.live/problems\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblems Page\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and then click problem numbers to see the puzzles and to download problem files.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"358\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"776\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"top\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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