bqpd
The solver BQPD solves large, sparse or dense linear and quadratic programming problems.
Main features

The BQPD code
solves quadratic programming (minimization of a quadratic function
subject to linear constraints) and linear programming problems. If the
Hessian matrix Q is positive definite, then a global solution is found.
A global solution is also found in the case of linear programming (Q=0).
When Q is indefinite,
a KuhnTucker point that is usually a local solution is found.

The code implements a nullspace active set method with a technique for
resolving degeneracy that guarantees that cycling does not occur even when
roundoff errors are present. Feasibility is obtained by minimizing a sum
of constraint violations. The Devex method for avoiding nearzero pivots is
used to promote stability. The matrix algebra is implemented so that the
algorithm can take advantage of sparse factors of the basis matrix.
Factors of the reduced Hessian matrix are stored in a dense format,
an approach that is most effective when the number of free variables
is relatively small. The user must supply a subroutine to evaluate the
Hessian matrix Q, so that sparsity in Q can be exploited.
An extreme case occurs when Q=0 and the QP reduces to a linear program.
The code is written to take maximum advantage of this situation,
so that it also provides an efficient method for linear programming.

The dense and the sparse version of
BQPD
are compiled in two different MEX binaries,
making the two versions optimally efficient.

BQPD
is integrated with the TOMLAB driver routines.

BQPD
may be used as subproblem solver in the TOMLAB
environment.
