This is documentation for Mathematica 4, which was
based on an earlier version of the Wolfram Language.
View current documentation (Version 11.1)

JordanDecompositionLatticeReduce

LinearProgramming

FilledSmallSquareLinearProgramming[c, m, b] finds a vector x which minimizes the quantity c.x subject to the constraints and .

FilledSmallSquareLinearProgramming[c, m, , , , , ... ] finds a vector x which minimizes c.x subject to and linear constraints specified by the matrix m and the pairs , . For each row of m, the corresponding constraint is if , or == if == 0, or if .

FilledSmallSquareLinearProgramming[c, m, b, l] minimizes c.x subject to the constraints specified by m and b and .

FilledSmallSquareLinearProgramming[c, m, b, , , ... ] minimizes c.x subject to the constraints specified by m and b and .

FilledSmallSquareLinearProgramming[c, m, b, , , , , ... ] minimizes c.x subject to the constraints specified by m and b and .

FilledSmallSquare All entries in the vectors c and b and the matrix m must be real numbers.

FilledSmallSquare The bounds and must be real numbers or Infinity or -Infinity.

FilledSmallSquareLinearProgramming gives exact rational number results if its input is exact.

FilledSmallSquareLinearProgramming returns unevaluated if no solution can be found.

FilledSmallSquareLinearProgramming accepts the same Tolerance option as ConstrainedMax.

FilledSmallSquare See The Mathematica Book: Section 3.9.9.

FilledSmallSquare See also: ConstrainedMax.

Further Examples

JordanDecompositionLatticeReduce