{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2025-12-14T01:33:56.000Z","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":"2025-12-14T00:00:00.000Z","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":59511,"title":"Count the ones in a divisibility matrix","description":"Cody Problem 59506 asked solvers to compute the determinant of matrix consisting of ones in the first column and anywhere the row index divides the column index.\r\nWrite a function to count the ones in the matrix. For example, the 4x4 matrix given in the description of Cody Problem 59506 has 11 ones, and the 1000x1000 matrix has 8068. ","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: 93px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 46.5px; transform-origin: 407px 46.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; 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 21px; text-align: left; transform-origin: 384px 21px; 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/59506\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003eCody Problem 59506\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: 290.933px 8px; transform-origin: 290.933px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e asked solvers to compute the determinant of matrix consisting of ones in the first column and anywhere the row index divides the column index.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; 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 21px; text-align: left; transform-origin: 384px 21px; 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: 318.025px 8px; transform-origin: 318.025px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to count the ones in the matrix. For example, the 4x4 matrix given in the description of \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/59506\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003eCody Problem 59506\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: 0.225px 8px; transform-origin: 0.225px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e has 11 ones, and the 1000x1000 matrix has 8068. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = divMatrixOnes(n)\r\n  y = length(find(divMatrix));\r\nend","test_suite":"%%\r\nn = 4;\r\ny = divMatrixOnes(n);\r\ny_correct = 11;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 18;\r\ny = divMatrixOnes(n);\r\ny_correct = 75;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 58;\r\ny = divMatrixOnes(n);\r\ny_correct = 304;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 411;\r\ny = divMatrixOnes(n);\r\ny_correct = 2950;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 1563;\r\ny = divMatrixOnes(n);\r\ny_correct = 13303;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 5851;\r\ny = divMatrixOnes(n);\r\ny_correct = 57516;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 99991;\r\ny = divMatrixOnes(n);\r\ny_correct = 1266618;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 4;\r\ny = divMatrixOnes(n);\r\ny_correct = 11;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 168732;\r\ny = divMatrixOnes(n);\r\ny_correct = 2225685;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 452111;\r\ny = divMatrixOnes(n);\r\ny_correct = 6409180;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 1534232;\r\ny = divMatrixOnes(n);\r\ny_correct = 23624033;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 84173652;\r\ny = divMatrixOnes(n);\r\ny_correct = 1633206463;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 2e7;\r\ny = divMatrixOnes(n);\r\ny_correct = 359313639;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 3e8;\r\ny = divMatrixOnes(n);\r\ny_correct = 6202117673;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 3678;\r\nyyyy = divMatrixOnes(divMatrixOnes(divMatrixOnes(divMatrixOnes(n))));\r\nyyyy_correct = 93760150;\r\nassert(isequal(yyyy,yyyy_correct))\r\n\r\n%%\r\nfiletext = fileread('divMatrixOnes.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext,'regexp'); \r\nassert(~illegal)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2023-12-30T01:17:12.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-12-30T01:17:04.000Z","updated_at":"2026-01-18T12:21:54.000Z","published_at":"2023-12-30T01:17:12.000Z","restored_at":null,"restored_by":null,"spam":false,"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:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/59506\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCody Problem 59506\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e asked solvers to compute the determinant of matrix consisting of ones in the first column and anywhere the row index divides the column index.\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\u003eWrite a function to count the ones in the matrix. For example, the 4x4 matrix given in the description of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/59506\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCody Problem 59506\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e has 11 ones, and the 1000x1000 matrix has 8068. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":59506,"title":"Compute the determinant of a divisibility matrix","description":"Consider a matrix consisting of ones in the first column and anywhere the row index divides the column index. The 4x4 matrix is [1 1 1 1; 1 1 0 1; 1 0 1 0; 1 0 0 1]. The 1000x1000 matrix is depicted below.\r\nWrite a function to compute the determinant of these matrices, given an array of sizes. \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: 649.7px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 324.85px; transform-origin: 407px 324.85px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; 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 21px; text-align: left; transform-origin: 384px 21px; 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: 368.758px 8px; transform-origin: 368.758px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eConsider a matrix consisting of ones in the first column and anywhere the row index divides the column index. The 4x4 matrix is [1 1 1 1; 1 1 0 1; 1 0 1 0; 1 0 0 1]. The 1000x1000 matrix is depicted below.\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: 268.625px 8px; transform-origin: 268.625px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to compute the determinant of these matrices, given an array of sizes. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 568.7px; 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 284.35px; text-align: left; transform-origin: 384px 284.35px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"561\" height=\"563\" style=\"vertical-align: baseline;width: 561px;height: 563px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = detDivMatrix(n)\r\n  v = 1:n;\r\n  y = det(mod(v,v')==0);\r\nend","test_suite":"%%\r\nn = 1;\r\ny = detDivMatrix(n);\r\ny_correct = 1;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = [65 39 2 40 101];\r\ny = detDivMatrix(n);\r\nassert(all(y==0))\r\n\r\n%%\r\nn = [439 382 766 796 187];\r\ny = detDivMatrix(n);\r\ny_correct = [-7 -1 -3 0 -3];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = [5408 5011 6817 7385 7793 3485 7118 6896 2464 2071];\r\ny = detDivMatrix(n);\r\ny_correct = [10 3 -13 8 -2 9 -11 -18 -8 6];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 11000:1000:20000;\r\ny = detDivMatrix(n);\r\ny_correct = [-9 15 -9 13 -10 -28 -21 -11 39 26];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 111111:111111:999999;\r\ny = detDivMatrix(n);\r\ny_correct = [2 -75 -109 -37 34 180 28 -140 212];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 25000:50000;\r\ny = detDivMatrix(n);\r\nu = unique(y);\r\n[s,indx] = sort(histcounts(y,u),'descend');\r\nassert(isequal(s(1:5),[526 512 505 503 501]))\r\nassert(isequal(u(indx(1:5)),[-2 -1 -4 -3 -5]))\r\n\r\n%%\r\nfiletext = fileread('detDivMatrix.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext,'regexp'); \r\nassert(~illegal)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-12-29T03:24:09.000Z","updated_at":"2023-12-29T03:24:09.000Z","published_at":"2023-12-29T03:24:09.000Z","restored_at":null,"restored_by":null,"spam":false,"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\u003eConsider a matrix consisting of ones in the first column and anywhere the row index divides the column index. The 4x4 matrix is [1 1 1 1; 1 1 0 1; 1 0 1 0; 1 0 0 1]. The 1000x1000 matrix is depicted below.\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\u003eWrite a function to compute the determinant of these matrices, given an array of sizes. \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=\\\"563\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"561\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":59511,"title":"Count the ones in a divisibility matrix","description":"Cody Problem 59506 asked solvers to compute the determinant of matrix consisting of ones in the first column and anywhere the row index divides the column index.\r\nWrite a function to count the ones in the matrix. For example, the 4x4 matrix given in the description of Cody Problem 59506 has 11 ones, and the 1000x1000 matrix has 8068. ","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: 93px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 46.5px; transform-origin: 407px 46.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; 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 21px; text-align: left; transform-origin: 384px 21px; 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/59506\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003eCody Problem 59506\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: 290.933px 8px; transform-origin: 290.933px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e asked solvers to compute the determinant of matrix consisting of ones in the first column and anywhere the row index divides the column index.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; 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 21px; text-align: left; transform-origin: 384px 21px; 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: 318.025px 8px; transform-origin: 318.025px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to count the ones in the matrix. For example, the 4x4 matrix given in the description of \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/59506\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003eCody Problem 59506\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: 0.225px 8px; transform-origin: 0.225px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e has 11 ones, and the 1000x1000 matrix has 8068. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = divMatrixOnes(n)\r\n  y = length(find(divMatrix));\r\nend","test_suite":"%%\r\nn = 4;\r\ny = divMatrixOnes(n);\r\ny_correct = 11;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 18;\r\ny = divMatrixOnes(n);\r\ny_correct = 75;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 58;\r\ny = divMatrixOnes(n);\r\ny_correct = 304;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 411;\r\ny = divMatrixOnes(n);\r\ny_correct = 2950;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 1563;\r\ny = divMatrixOnes(n);\r\ny_correct = 13303;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 5851;\r\ny = divMatrixOnes(n);\r\ny_correct = 57516;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 99991;\r\ny = divMatrixOnes(n);\r\ny_correct = 1266618;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 4;\r\ny = divMatrixOnes(n);\r\ny_correct = 11;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 168732;\r\ny = divMatrixOnes(n);\r\ny_correct = 2225685;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 452111;\r\ny = divMatrixOnes(n);\r\ny_correct = 6409180;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 1534232;\r\ny = divMatrixOnes(n);\r\ny_correct = 23624033;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 84173652;\r\ny = divMatrixOnes(n);\r\ny_correct = 1633206463;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 2e7;\r\ny = divMatrixOnes(n);\r\ny_correct = 359313639;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 3e8;\r\ny = divMatrixOnes(n);\r\ny_correct = 6202117673;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 3678;\r\nyyyy = divMatrixOnes(divMatrixOnes(divMatrixOnes(divMatrixOnes(n))));\r\nyyyy_correct = 93760150;\r\nassert(isequal(yyyy,yyyy_correct))\r\n\r\n%%\r\nfiletext = fileread('divMatrixOnes.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext,'regexp'); \r\nassert(~illegal)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2023-12-30T01:17:12.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-12-30T01:17:04.000Z","updated_at":"2026-01-18T12:21:54.000Z","published_at":"2023-12-30T01:17:12.000Z","restored_at":null,"restored_by":null,"spam":false,"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:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/59506\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCody Problem 59506\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e asked solvers to compute the determinant of matrix consisting of ones in the first column and anywhere the row index divides the column index.\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\u003eWrite a function to count the ones in the matrix. For example, the 4x4 matrix given in the description of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/59506\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCody Problem 59506\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e has 11 ones, and the 1000x1000 matrix has 8068. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":59506,"title":"Compute the determinant of a divisibility matrix","description":"Consider a matrix consisting of ones in the first column and anywhere the row index divides the column index. The 4x4 matrix is [1 1 1 1; 1 1 0 1; 1 0 1 0; 1 0 0 1]. The 1000x1000 matrix is depicted below.\r\nWrite a function to compute the determinant of these matrices, given an array of sizes. \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: 649.7px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 324.85px; transform-origin: 407px 324.85px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; 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 21px; text-align: left; transform-origin: 384px 21px; 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: 368.758px 8px; transform-origin: 368.758px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eConsider a matrix consisting of ones in the first column and anywhere the row index divides the column index. The 4x4 matrix is [1 1 1 1; 1 1 0 1; 1 0 1 0; 1 0 0 1]. The 1000x1000 matrix is depicted below.\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: 268.625px 8px; transform-origin: 268.625px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to compute the determinant of these matrices, given an array of sizes. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 568.7px; 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 284.35px; text-align: left; transform-origin: 384px 284.35px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"561\" height=\"563\" style=\"vertical-align: baseline;width: 561px;height: 563px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = detDivMatrix(n)\r\n  v = 1:n;\r\n  y = det(mod(v,v')==0);\r\nend","test_suite":"%%\r\nn = 1;\r\ny = detDivMatrix(n);\r\ny_correct = 1;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = [65 39 2 40 101];\r\ny = detDivMatrix(n);\r\nassert(all(y==0))\r\n\r\n%%\r\nn = [439 382 766 796 187];\r\ny = detDivMatrix(n);\r\ny_correct = [-7 -1 -3 0 -3];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = [5408 5011 6817 7385 7793 3485 7118 6896 2464 2071];\r\ny = detDivMatrix(n);\r\ny_correct = [10 3 -13 8 -2 9 -11 -18 -8 6];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 11000:1000:20000;\r\ny = detDivMatrix(n);\r\ny_correct = [-9 15 -9 13 -10 -28 -21 -11 39 26];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 111111:111111:999999;\r\ny = detDivMatrix(n);\r\ny_correct = [2 -75 -109 -37 34 180 28 -140 212];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 25000:50000;\r\ny = detDivMatrix(n);\r\nu = unique(y);\r\n[s,indx] = sort(histcounts(y,u),'descend');\r\nassert(isequal(s(1:5),[526 512 505 503 501]))\r\nassert(isequal(u(indx(1:5)),[-2 -1 -4 -3 -5]))\r\n\r\n%%\r\nfiletext = fileread('detDivMatrix.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext,'regexp'); \r\nassert(~illegal)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-12-29T03:24:09.000Z","updated_at":"2023-12-29T03:24:09.000Z","published_at":"2023-12-29T03:24:09.000Z","restored_at":null,"restored_by":null,"spam":false,"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\u003eConsider a matrix consisting of ones in the first column and anywhere the row index divides the column index. The 4x4 matrix is [1 1 1 1; 1 1 0 1; 1 0 1 0; 1 0 0 1]. The 1000x1000 matrix is depicted below.\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\u003eWrite a function to compute the determinant of these matrices, given an array of sizes. \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=\\\"563\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"561\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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