Difference between revisions of "ApCoCoA-1:GLPK.IPCSolve"
(New page: <command> <title>GLPK.IPCSolve</title> <short_description>Solve a system of polynomial equations over <tt>F_2</tt> for one solution in <tt>F_2^n</tt>.</short_description> <syntax> GLPK.IPC...) |
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-- The result will be the following: | -- The result will be the following: | ||
+ | |||
+ | Modelling the system as a mixed integer programming problem. | ||
+ | QStrategy: LinearPartner, CStrategy: Standard. | ||
+ | Model is ready to solve with GLPK... | ||
+ | Solution Status: INTEGER OPTIMAL | ||
+ | Value of objective function: 4 | ||
[1, 1, 1, 1, 0] | [1, 1, 1, 1, 0] | ||
Line 84: | Line 90: | ||
-- The result will be the following: | -- The result will be the following: | ||
+ | |||
+ | Modelling the system as a mixed integer programming problem. | ||
+ | QStrategy: Standard, CStrategy: CubicParnterDegree2. | ||
+ | Model is ready to solve with GLPK... | ||
+ | |||
+ | Solution Status: INTEGER OPTIMAL | ||
+ | Value of objective function: 1 | ||
[0, 0, 1] | [0, 0, 1] |
Revision as of 14:06, 27 April 2011
GLPK.IPCSolve
Solve a system of polynomial equations over F_2 for one solution in F_2^n.
Syntax
GLPK.IPCSolve(F:LIST, QStrategy:INT, CStrategy:INT, MinMax:STRING)
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 finds one solution in F_2^n of a system of polynomial equations over the field F_2. It uses Integer Polynomial Conversion (IPC) along with some strategies from propositional logic to model a mixed integer linear programming problem. Then the modeled mixed integer linear programming problem is solved using glpk.
@param F: A List containing the polynomials of the given system.
@param QStrategy: Strategy for quadratic substitution. 0 - Standard; 1 - Linear Partner; 2 - Double Linear Partner; 3 - Quadratic Partner;
@param CStrategy: Strategy for cubic substitution. 0 - Standard; and 1 - Quadratic Partner;
@param MinMax: Optimization direction i.e. minimization ("Min") or maximization ("Max").
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 ]; QStrategy:=0; CStrategy:=0; MinMax:=<quotes>Max</quotes>; -- Then we compute the solution with GLPK.IPCSolve(F, QStrategy, CStrategy, MinMax); -- The result will be the following: [0, 1, 0, 1] -------------------------------
Example
Use S::=Z/(2)[x[1..5]]; F:=[ x[1]x[5] + x[3]x[5] + x[4]x[5] + x[1] + x[4], x[1]x[2] + x[1]x[4] + x[3]x[4] + x[1]x[5] + x[2]x[5] + x[3]x[5] + x[1] + x[4] + x[5] + 1, x[1]x[2] + x[4]x[5] + x[1] + x[2] + x[4], x[1]x[4] + x[3]x[4] + x[2]x[5] + x[1] + x[2] + x[4] + x[5] + 1, x[1]x[4] + x[2]x[4] + x[3]x[4] + x[2]x[5] + x[4]x[5] + x[1] + x[2] + x[4] + x[5] ]; QStrategy:=1; CStrategy:=0; MinMax:=<quotes>Max</quotes>; -- Then we compute the solution with GLPK.IPCSolve(F, QStrategy, CStrategy, MinMax); -- The result will be the following: Modelling the system as a mixed integer programming problem. QStrategy: LinearPartner, CStrategy: Standard. Model is ready to solve with GLPK... Solution Status: INTEGER OPTIMAL Value of objective function: 4 [1, 1, 1, 1, 0] -------------------------------
Example
Use ZZ/(2)[x[1..3]]; F := [ x[1]x[2]x[3] + x[1]x[2] + x[2]x[3] + x[1] + x[3] +1, x[1]x[2]x[3] + x[1]x[2] + x[2]x[3] + x[1] + x[2], x[1]x[2] + x[2]x[3] + x[2] ]; QStrategy:=0; CStrategy:=1; MinMax:=<quotes>Max</quotes>; -- Then we compute the solution with GLPK.IPCSolve(F, QStrategy, CStrategy, MinMax); -- The result will be the following: Modelling the system as a mixed integer programming problem. QStrategy: Standard, CStrategy: CubicParnterDegree2. Model is ready to solve with GLPK... Solution Status: INTEGER OPTIMAL Value of objective function: 1 [0, 0, 1] -------------------------------