TOMLAB Base Module

The TOMLAB Base Module includes a large set of optimization solvers. Most of them were originally developed by the Applied Optimization and Modeling group (TOM). Since then they have been improved e.g. to handle Matlab sparse arrays and been further developed. The TOMLAB Base Module also includes the SOL solvers Tlsqr (LSQR), PDCO and PDSCO.
See the complete list of solvers in the TOMLAB Base Module >>

The TOMLAB Base Module can be extended with more powerful solvers. See our product list >>

The TOMLAB Base Module consists of more than 95 000 lines of Matlab m-file code with more than 80 optimization algorithms implemented.

Main features

• TOMLAB Base Module efficiently integrates five Fortran solvers using MEX-file interfaces: Tlsqr, LSEI, Tnnls, QLD and Tfzero. The other solvers are running part of the code in Fortran, giving speed-up.
   
• Using the Tomlab graphical interface (GUI) all solver parameters and features are selectable.
   
• Box-bounded global optimization, solver glbSolve and the Fortran MEX-version glbFast.
   
• Integer and nonlinearly constrained global optimization, solver glcSolve and the Fortran MEX-version glcFast as well as glcCluster.
   
• Nonconvex quadratic programming, solver qpSolve. qld for convex dense QP.
   
• Linear programming, solvers lpSolve, QLD and DualSolve (dual LP problems).
   
• Mixed-Integer Linear Programming, solvers mipSolve and cutplane. Either the QLD MEX-file QP-solver or the DualSolve dual simplex solver may be used for subproblems. With warm starts.
   
• Constrained nonlinear minimization, solvers conSolve and nlpSolve.
   
• Unconstrained nonlinear minimization, solvers ucSolve and sTrustr. The codes for constrained optimization may also be used.
   
• Robust solution of ill-conditioned nonlinear least squares with linear constraints, solver clsSolve.
   
• Sparse linear least squares using Tlsqr, or the nonlinear least squares solver clsSolve.
   
• Dense constrained linear least squares using LSEI.
   
• Nonnegative constrained linear least squares using Tnnls, a fast and robust replacement for the Matlab nnls routine.
   
• Solving non-linear systems of equations, solver clsSolve.
   
• Automatic differentiation using the MAD toolbox.