includes two solvers for
nonconvex, convex or pseudo-convex mixed-integer nonlinear programming (MINLP)
Features and capabilities
solves convex or pseudo-convex mixed-integer
nonlinear programming problems using an extended cutting plane algorithm
with cuts regulated by a parameter-vector alpha. Cuts and linearizations
are added to MIP subproblem which is then solved by a subsolver in each
The solver algorithm is mainly based on the paper
Solving Pseudo-Convex Mixed Integer Optimization Problems by Cutting Plane Techniques'
Optimization and Engineering 3,
by Tapio Westerlund and Ray Pörn,
but with several modifications.
is using a Single-search Tree Outer Approximation algorithm
to solve Mixed-Integer NonLinear Programming (MINLP) problems.
Handles both convex or nonconvex problems, but is best suited for solving
If the nonlinear subproblems are known to be convex,
setting an input parameter will make the solver will run much faster.
TOMLAB /MIPNLP is integrated with the TOMLAB optimization environment.
The TOMLAB /MIPNLP solvers may be used as subproblem solvers in the TOMLAB