Difference between revisions of "ApCoCoA-1:CharP.IMNLASolve"
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− | This function computes the unique zero in <tt>F_2^n</tt> of a polynomial system over <tt>F_2</tt>. It uses <tt>I</tt>mproved <tt>M</tt>utant <tt>NLA</tt>-Algorithm to find the unique zero. The Improved Mutant <tt>NLA</tt>-Algorithm generates a sequence of linear systems to solve the given system. The Improved Mutant <tt>NLA</tt>-Algorithm can find the unique zero only. If the given polynomial system has more than one zeros in <tt>F_2^n </tt> then this function does not find any zero. In this case a massage for non-uniqueness will be displayed to the screen after reaching the maximum degree bound. In fact Improved Mutant NLA-Algorithm is the NLA-Algorithm with improved mutant strategy. It uses <ref>LinAlg.EF</ref> for gaussian elimination. | + | This function computes the unique zero in <tt>F_2^n</tt> of a polynomial system over <tt>F_2</tt>. It uses <tt>I</tt>mproved <tt>M</tt>utant <tt>NLA</tt>-Algorithm to find the unique zero. The Improved Mutant <tt>NLA</tt>-Algorithm generates a sequence of linear systems to solve the given system. The Improved Mutant <tt>NLA</tt>-Algorithm can find the unique zero only. If the given polynomial system has more than one zeros in <tt>F_2^n </tt> then this function does not find any zero. In this case a massage for non-uniqueness will be displayed to the screen after reaching the maximum degree bound. In fact Improved Mutant NLA-Algorithm is the NLA-Algorithm with improved mutant strategy. It uses <ref>ApCoCoA-1:LinAlg.EF|LinAlg.EF</ref> for gaussian elimination. |
<itemize> | <itemize> | ||
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</description> | </description> | ||
<seealso> | <seealso> | ||
− | <see>CharP.MXLSolve</see> | + | <see>ApCoCoA-1:CharP.MXLSolve|CharP.MXLSolve</see> |
− | <see>Introduction to CoCoAServer</see> | + | <see>ApCoCoA-1:Introduction to CoCoAServer|Introduction to CoCoAServer</see> |
− | <see>Introduction to Groebner Basis in CoCoA</see> | + | <see>ApCoCoA-1:Introduction to Groebner Basis in CoCoA|Introduction to Groebner Basis in CoCoA</see> |
− | <see>CharP.GBasisF2</see> | + | <see>ApCoCoA-1:CharP.GBasisF2|CharP.GBasisF2</see> |
− | <see>CharP.XLSolve</see> | + | <see>ApCoCoA-1:CharP.XLSolve|CharP.XLSolve</see> |
− | <see>CharP.IMXLSolve</see> | + | <see>ApCoCoA-1:CharP.IMXLSolve|CharP.IMXLSolve</see> |
− | <see>CharP.MNLASolve</see> | + | <see>ApCoCoA-1:CharP.MNLASolve|CharP.MNLASolve</see> |
</seealso> | </seealso> | ||
Revision as of 08:08, 7 October 2020
CharP.IMNLASolve
Computes the unique F_2-rational zero of a given polynomial system over F_2.
Syntax
CharP.IMNLASolve(F:LIST):LIST
Description
Please note: The function(s) explained on this page is/are using the ApCoCoAServer. You will have to start the ApCoCoAServer in order to use it/them.
This function computes the unique zero in F_2^n of a polynomial system over F_2. It uses Improved Mutant NLA-Algorithm to find the unique zero. The Improved Mutant NLA-Algorithm generates a sequence of linear systems to solve the given system. The Improved Mutant NLA-Algorithm can find the unique zero only. If the given polynomial system has more than one zeros in F_2^n then this function does not find any zero. In this case a massage for non-uniqueness will be displayed to the screen after reaching the maximum degree bound. In fact Improved Mutant NLA-Algorithm is the NLA-Algorithm with improved mutant strategy. It uses LinAlg.EF for gaussian elimination.
@param F: List of polynomials of given system.
@return Possibly the unique solution of the given system in F_2^n.
Example
Use Z/(2)[x[1..4]]; F:=[ x[1]x[2] + x[2]x[3] + x[2]x[4] + x[3]x[4] + x[1] + x[3] + 1, x[1]x[2] + x[1]x[3] + x[1]x[4] + x[3]x[4] + x[2] + x[3] + 1, x[1]x[2] + x[1]x[3] + x[2]x[3] + x[3]x[4] + x[1] + x[4] + 1, x[1]x[3] + x[2]x[3] + x[1]x[4] + x[2]x[4] + 1 ]; -- Then we compute the solution with CharP.IMNLASolve(F); -- And we achieve the following information on the screen together with the solution at the end. ---------------------------------------- The size of Matrix is: No. of Rows=4 No. of Columns=11 Applying Gaussian Elimination for finding Mutants... Gaussian Elimination Compeleted. Finding Variable: x[4] The size of Matrix is: No. of Rows=11 No. of Columns=5 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=11 No. of Columns=5 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=4 No. of Columns=11 Applying Gaussian Elimination for finding Mutants... Gaussian Elimination Compeleted. No. of New Mutants found = 0 The size of Matrix is: No. of Rows=11 No. of Columns=9 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=11 No. of Columns=9 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=8 No. of Columns=11 Applying Gaussian Elimination for finding Mutants... Gaussian Elimination Compeleted. No. of New Mutants found = 1 The total No. of Mutants found are = 1 The No. of Mutants of Minimum degree (Mutants used) are = 1 The size of Matrix is: No. of Rows=11 No. of Columns=12 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=11 No. of Columns=12 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. x[4] = 1 Finding Variable: x[3] The size of Matrix is: No. of Rows=7 No. of Columns=10 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. x[3] = 0 Finding Variable: x[2] The size of Matrix is: No. of Rows=4 No. of Columns=5 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=4 No. of Columns=5 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. x[2] = 1 [0, 1, 0, 1]
Example
Use Z/(2)[x[1..4]]; F:=[ x[2]x[3] + x[1]x[4] + x[2]x[4] + x[3]x[4] + x[1] + x[2] + x[3] + x[4], x[2]x[3] + x[2]x[4] + x[3]x[4] + x[2] + x[3] + x[4], x[1]x[2] + x[2]x[3] + x[2]x[4] + x[3]x[4] + x[1] + x[2], x[1]x[2] + x[2]x[3] + x[2]x[4] + x[3]x[4] + x[1] + x[2] ]; -- Solution is not unique i.e. [0, 1, 1, 1], [0, 0, 0, 0], and [1, 1, 1, 1] are solutions -- Then we compute the solution with CharP.IMNLASolve(F); -- And we achieve the following information on the screen. ---------------------------------------- The size of Matrix is: No. of Rows=4 No. of Columns=9 Applying Gaussian Elimination for finding Mutants... Gaussian Elimination Compeleted. Finding Variable: x[4] The size of Matrix is: No. of Rows=9 No. of Columns=4 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=9 No. of Columns=4 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=3 No. of Columns=9 Applying Gaussian Elimination for finding Mutants... Gaussian Elimination Compeleted. No. of New Mutants found = 0 The size of Matrix is: No. of Rows=14 No. of Columns=8 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=14 No. of Columns=8 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=7 No. of Columns=14 Applying Gaussian Elimination for finding Mutants... Gaussian Elimination Compeleted. No. of New Mutants found = 2 The total No. of Mutants found are = 2 The No. of Mutants of Minimum degree (Mutants used) are = 2 The size of Matrix is: No. of Rows=10 No. of Columns=14 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=10 No. of Columns=14 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=13 No. of Columns=10 Applying Gaussian Elimination for finding Mutants... Gaussian Elimination Compeleted. No. of New Mutants found = 0 The size of Matrix is: No. of Rows=10 No. of Columns=9 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=10 No. of Columns=9 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=8 No. of Columns=10 Applying Gaussian Elimination for finding Mutants... Gaussian Elimination Compeleted. No. of New Mutants found = 0 The size of Matrix is: No. of Rows=14 No. of Columns=24 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=14 No. of Columns=24 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. x[4] = NA Please Check the uniqueness of solution. The Given system of polynomials does not seem to have a unique solution or it has no solution over the finite field F2.
See also
Introduction to Groebner Basis in CoCoA