A unique feature of the implementation is that it is possible to solve exact/extended precision problems. The Wolfram Language's implementation of these algorithms uses dense linear algebra. The simplex and revised simplex algorithms solve a linear optimization problem by moving along the edges of the polytope defined by the constraints, from vertices to vertices with successively smaller values of the objective function, until the minimum is reached. Examples Difference between Interior Point and Simplex and/or Revised Simplex Solution properties for LinearOptimization. The linear equality constraint matrix and vector The linear inequality constraint matrix and vector There are a number of solution properties that can be accessed through the LinearOptimization function: With WorkingPrecision-> Automatic, the precision is taken automatically from the precision of the input arguments unless a method is specified that only works with machine precision, in which case machine precision is used. The Tolerance option specifies the convergence tolerance. The default is Automatic, which automatically chooses from the other methods based on the problem size and precision. Possible values are Automatic, "Simplex", "RevisedSimplex", "InteriorPoint", "CLP", "MOSEK" and "Gurobi". ![]() The Method option specifies the algorithm used to solve the linear optimization problem. Precision to be used in internal computations Method used to solve the linear optimization problemĪspects of performance to try to optimize
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