Problem 916. Rubik's Cube : Solve Ten Face Move - Speed Scoring

Created by Richard Zapor

Solve Later

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The Challenge is to Solve all Cubes in 10 Moves or Less. Score will be by cumulative time of cubes 2 thru 11. The first cube may be used to load arrays but is still required to solve the cube. Ten moves relates to worst case 18^10 configs - 3.6e12. In actuality there are only 2.4e11 cubes at 10 turns. Brute force might not work.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId1\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId2\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId3\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Input:</w:t></w:r><w:r><w:t> Cube vector (1x48](see Figure) with values 0:5, eight of each</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Output:</w:t></w:r><w:r><w:t> Rotation Vector of 10 or less values. Values range from 1:18</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>Values 1:18 represent the standard cube moves:ufdlbru'f'd'l'b'r'u2f2d2l2b2r2</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Example:</w:t></w:r><w:r><w:t> Move of FL[2 4] requires L'F' solv_vec=[10 8] at a minimum to solve</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>Cube=[00303003 44410110 11022222 22253533 53344444 11155555] as integer array</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>solv_vec=[10 8]</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>Actual solutions will be 8 to 10 values.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>The Cube sites claim a capability of solving 10 moves in only 40 hrs. Not sure if they have Matlab, which can achieve 10 moves in &lt;0.15 sec. Matlab with a 16GB/i5 can solve 12 moves in &lt; 1.8 seconds. Unfortunately, Cody appears to have a memory/processing speed issue which makes the 11 and 12 move Challenges impossible.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:hyperlink w:docLocation=\"http://kociemba.org/cube.htm\"><w:r><w:t>Cube Org</w:t></w:r></w:hyperlink><w:r><w:t> and</w:t></w:r><w:r><w:t> </w:t></w:r><w:hyperlink w:docLocation=\"http://www.speedcubing.com/CubeSolver/CubeSolver.html\"><w:r><w:t>Speed Cubing</w:t></w:r></w:hyperlink></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>The next Cube Challenge will be to solve a fully randomized cube in as few moves as possible. With this next challenge I'll post my 3D-Cube Viewer Tool used for algorithm development.</w:t></w:r></w:p></w:body></w:document>"},{"partUri":"/matlab/output.xml","contentType":"text/xml","content":"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\" ?><embeddedOutputs><metaData><evaluationState>manual</evaluationState><layoutState>code</layoutState><outputStatus>ready</outputStatus></metaData><outputArray type=\"array\"/><regionArray 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"},{"partUri":"/media/image2.gif","contentType":"image/gif","content":"data:image/gif;base64,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"}]}

A set of cubes will be scrambled with 10 Face moves.
The Challenge is to Solve all Cubes in 10 Moves or Less.
Score will be by cumulative time of cubes 2 thru 11. The first cube may be used to load arrays but is still required to solve the cube.
Ten moves relates to worst case 18^10 configs - 3.6e12. In actuality there are only 2.4e11 cubes at 10 turns. Brute force might not work.<img src="https://sites.google.com/site/razapor/matlab_cody/cube_small.gif"><img src="https://sites.google.com/site/razapor/matlab_cody/Cube_Map48_200.png"><img src="http://mathworks.com/matlabcentral/images/surf.gif">.Input: Cube vector (1x48](see Figure) with values 0:5, eight of eachOutput: Rotation Vector of 10 or less values. Values range from 1:18Values 1:18 represent the standard cube moves:ufdlbru'f'd'l'b'r'u2f2d2l2b2r2Example: Move of FL[2 4] requires L'F' solv_vec=[10 8] at a minimum to solveCube=[00303003 44410110 11022222 22253533 53344444 11155555] as integer arraysolv_vec=[10 8]Actual solutions will be 8 to 10 values.The Cube sites claim a capability of solving 10 moves in only 40 hrs.
Not sure if they have Matlab, which can achieve 10 moves in &lt;0.15 sec. Matlab with a 16GB/i5 can solve 12 moves in &lt; 1.8 seconds. Unfortunately, Cody appears to have a memory/processing speed issue which makes the 11 and 12 move Challenges impossible.<a href="http://kociemba.org/cube.htm">Cube Org</a> and <a href="http://www.speedcubing.com/CubeSolver/CubeSolver.html">Speed Cubing</a>The next Cube Challenge will be to solve a fully randomized cube in as few moves as possible. With this next challenge I'll post my 3D-Cube Viewer Tool used for algorithm development.

A set of cubes will be scrambled with 10 Face moves.
The Challenge is to Solve all Cubes in 10 Moves or Less.
Score will be by cumulative time of cubes 2 thru 11. The first cube may be used to load arrays but is still required to solve the cube.
Ten moves relates to worst case 18^10 configs - 3.6e12. In actuality there are only 2.4e11 cubes at 10 turns. Brute force might not work.

<<https://sites.google.com/site/razapor/matlab_cody/cube_small.gif>>

<<https://sites.google.com/site/razapor/matlab_cody/Cube_Map48_200.png>>

<<http://mathworks.com/matlabcentral/images/surf.gif>>

*Input:* Cube vector (1x48](see Figure) with values 0:5, eight of each

*Output:* Rotation Vector of 10 or less values. Values range from 1:18

Values 1:18 represent the standard cube moves:ufdlbru'f'd'l'b'r'u2f2d2l2b2r2

*Example:* Move of FL[2 4]  requires L'F' solv_vec=[10 8] at a minimum to solve

Cube=[00303003 44410110 11022222 22253533 53344444 11155555] as integer array

solv_vec=[10 8]

Actual solutions will be 8 to 10 values.

The Cube sites claim a capability of solving 10 moves in only 40 hrs.
Not sure if they have Matlab, which can achieve 10 moves in <0.15 sec. Matlab with a 16GB/i5 can solve 12 moves in < 1.8 seconds. Unfortunately, Cody appears to have a memory/processing speed issue which makes the 11 and 12 move Challenges impossible.

<http://kociemba.org/cube.htm Cube Org> and <http://www.speedcubing.com/CubeSolver/CubeSolver.html Speed Cubing>

The next Cube Challenge will be to solve a fully randomized cube in as few moves as possible. With this next challenge I'll post my 3D-Cube Viewer Tool used for algorithm development.

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