# Difference between revisions of "ApCoCoA-1:CharP.MNLASolve"

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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 Mutant NLA-Algorithm to find the unique zero. The Mutant NLA-Algorithm generates a sequence of linear systems to solve the given system. The Mutant NLA-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 Mutant NLA-Algorithm is the NLA-Algorithm with 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 Mutant NLA-Algorithm to find the unique zero. The Mutant NLA-Algorithm generates a sequence of linear systems to solve the given system. The Mutant NLA-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 Mutant NLA-Algorithm is the NLA-Algorithm with mutant strategy. It uses <ref>ApCoCoA-1:LinAlg.EF|LinAlg.EF</ref> for gaussian elimination. |

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− | <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.IMNLASolve</see> | + | <see>ApCoCoA-1:CharP.IMNLASolve|CharP.IMNLASolve</see> |

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<key>finite field</key> | <key>finite field</key> | ||

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## Latest revision as of 09:56, 7 October 2020

This article is about a function from ApCoCoA-1. |

## CharP.MNLASolve

Computes the unique `F_2-`rational zero of a given polynomial system over `F_2`.

### Syntax

CharP.MNLASolve(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 Mutant NLA-Algorithm to find the unique zero. The Mutant NLA-Algorithm generates a sequence of linear systems to solve the given system. The 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 Mutant NLA-Algorithm is the NLA-Algorithm with mutant strategy. It uses LinAlg.EF for gaussian elimination.

@param

*F:*List of polynomials of given system.@return 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.MNLASolve(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 finding Muatants... 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 finding Muatants... Gaussian Elimination Compeleted The No. of 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 finding Muatants... Gaussian Elimination Compeleted The No. of Mutants found = 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.MNLASolve(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 finding Muatants... 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 finding Muatants... Gaussian Elimination Compeleted The No. of Mutants found = 0 The size of Matrix is: No. of Rows=14 No. of Columns=16 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=14 No. of Columns=16 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=15 No. of Columns=14 Applying Gaussian Elimination finding Muatants... Gaussian Elimination Compeleted The No. of Mutants found = 4 The size of Matrix is: No. of Rows=14 No. of Columns=28 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=14 No. of Columns=28 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=27 No. of Columns=14 Applying Gaussian Elimination finding Muatants... Gaussian Elimination Compeleted The No. of Mutants found = 0 The size of Matrix is: No. of Rows=14 No. of Columns=13 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=14 No. of Columns=13 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=12 No. of Columns=14 Applying Gaussian Elimination finding Muatants... Gaussian Elimination Compeleted The No. of Mutants found = 0 The size of Matrix is: No. of Rows=15 No. of Columns=20 Applying Gaussian Elimination to check solution coordinate... Gaussian Elimination Completed. The size of Matrix is: No. of Rows=15 No. of Columns=20 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