What Is Optimization Toolbox?

Constrained OptimizationConstrained Optimization• “Introduction” on page 3-29• “Sequential Quadratic Programming (SQP)” on page 3-30• “Quadratic Programming (QP) Subproblem” on page 3-31• “SQP Implementation” on page 3-32• “Simplex Algorithm” on page 3-39IntroductionIn constrained optimization, the general aim is to transform the probleminto an easier subproblem that can then be solved and used as the basis ofan iterative process. A characteristic of a large class of early methods is thetranslation of the constrained problem to a basic unconstrained problem byusing a penalty function for constraints that are near or beyond the constraintboundary. In this way the constrained problem is solved using a sequenceof parameterized unconstrained optimizations, which in the limit (of thesequence) converge to the constrained problem. These methods are nowconsidered relatively inefficient and have been replaced by methods that havefocused on the solution of the Kuhn-Tucker (KT) equations. The KT equationsare necessary conditions for optimality for a constrained optimizationproblem. If the problem is a so-called convex programming problem, that is,and, are convex functions, then the KT equations areboth necessary and sufficient for a global solution point.Referring to GP (Equation 3-1), the Kuhn-Tucker equations can be stated asin addition to the original constraints in Equation 3-1.(3-23)The first equation describes a canceling of the gradients between the objectivefunction and the active constraints at the solution point. For the gradients to3-29