Procedure GMP::Instance::FindApproximatelyFeasibleSolution(GMP, solution1, solution2, nrIter, maxIter, feasTol, moveTol, imprTol, maxTime, useSum, augIter, useBest)

GMP::Instance::FindApproximatelyFeasibleSolution

The procedure GMP::Instance::FindApproximatelyFeasibleSolution tries to find an approximately feasible solution of a generated mathematical program. It uses the column level values of the first solution as a starting point. The approximately feasible solution is stored in the second solution.
The algorithm used to find the approximately feasible solution is based on the constraint consensus method as developed by John W. Chinneck. The constraint consensus method is an iterative projection algorithm. In each iteration a new point (i.e., a vector of column values) is constructed in such a way that it is likely that it is closer to the feasible region (as defined by the generated mathematical program) then the previous point.
GMP::Instance::FindApproximatelyFeasibleSolution(
     GMP,            ! (input) a generated mathematical program
     solution1,      ! (input) a solution
     solution2,      ! (input) a solution
     nrIter,         ! (output) a scalar numerical parameter
     [maxIter],      ! (optional) a scalar value
     [feasTol],      ! (optional) a scalar value
     [moveTol],      ! (optional) a scalar value
     [imprTol],      ! (optional) a scalar value,
     [maxTime],      ! (optional) a scalar value
     [useSum],       ! (optional) a scalar value
     [augIter],      ! (optional) a scalar value
     [useBest]       ! (optional) a scalar value
)

Arguments

GMP
An element in AllGeneratedMathematicalPrograms.
solution1
An integer scalar reference to a solution.
solution2
An integer scalar reference to a solution.
nrIter
The number of iterations used by the algorithm.
maxIter
The maximal number of iterations that can be used by the algorithm. If its value is 0 (the default) then there is no iteration limit.
feasTol
The feasibility distance tolerance. The default is 1e-5.
moveTol
The movement tolerance. The default is 1e-5.
imprTol
The improvement tolerance. The default is 0.01.
maxTime
The maximum time (in seconds) that can be used by the algorithm. If its value is 0 (the default) then there is no time limit.
useSum
A scalar binary value to indicate whether the SUM constraint consensus method should be used (value 1) or not (value 0; the default).
augIter
An integer scalar reference that specifies the frequency of iterations in which augumentation should be applied. At the default value of 0 no augumentation is applied.
useBest
A scalar binary value to indicate whether the best point found (value 1) or the last point found should be returnd (value 0; the default).

Return Value

The procedure returns 1 on success, or 0 otherwise.

Note

  • The (basic) constraint consensus method is described in: John W. Chinneck, The Constraint Consensus Method for Finding Approximately Feasible Points in Nonlinear Programs, INFORMS Journal on Computing 16(3) (2004), pp. 255-265.
  • The SUM constraint consensus method and a constraint consensus method using augumentation are described in: Laurence Smith, John Chinneck, Victor Aitken, Improved constraint consensus methods for seeking feasibility in nonlinear programs, Computational Optimization and Applications 54(3) (2013), pp. 555-578.
  • The algorithm terminates if:
    • The iteration limit maxIter is exceeded.
    • The time limit maxTime is exceeded.
    • The feasibility distance of each row is smaller than the feasibility distance tolerance feasTol. The feasibility distance of a row at a point is defined as the row violation normalized by the length of the gradient of the row at that point.
    • The length of the movement vector is smaller than the movement tolerance moveTol. The movement vector is the vector along which the point moves from one iteration to another.
    • The relative improvement was smaller than the improvement tolerance imprTol for 10 successive iterations. The improvement is defined as the difference between the length of the movement vector of the current iteration and that of the previous iteration.
  • The procedure GMP::Solution::Check can be used to get the sum and number of infeasibilies before and after calling the procedure GMP::Instance::FindApproximatelyFeasibleSolution.