Optimization Toolbox

Quadratic Programming

Quadratic programming problems involve minimizing a multivariate quadratic function subject to bounds, linear equality, and inequality constraints. Optimization Toolbox includes three algorithms for solving quadratic programs:

• The interior-point-convex algorithm solves convex problems with any combination of constraints.
• The trust-region-reflective algorithm solves bound constrained problems or linear equality constrained problems.
• The active-set algorithm solves problems with any combination of constraints.

Both the interior-point-convex and trust-region-reflective algorithms are large-scale, meaning they can handle large, sparse problems. Furthermore, the interior-point-convex algorithm has optimized internal linear algebra routines and a new presolve module that can improve speed, numerical stability, and the detection of infeasibility.

Quadratic programming used to perform a returns-based style analysis for three mutual funds.