Optimization Toolbox |
Linear Equality Constraints
The general linear equality constrained minimization problem can be written
(4-5) |
where is an m-by-n matrix (). The Optimization Toolbox preprocesses to remove strict linear dependencies using a technique based on the LU-factorization of [6]. Here is assumed to be of rank m.
The method used to solve Eq. 4-5 differs from the unconstrained approach in two significant ways. First, an initial feasible point is computed, using a sparse least-squares step, so that . Second, Algorithm PCG is replaced with Reduced Preconditioned Conjugate Gradients (RPCG), see [6], in order to compute an approximate reduced Newton step (or a direction of negative curvature in the null space of ). The key linear algebra step involves solving systems of the form
(4-6) |
where approximates (small nonzeros of are set to zero provided rank is not lost) and is a sparse symmetric positive-definite approximation to H, i.e., . See [6] for more details.
Linearly Constrained Problems | Box Constraints |