Download or read book User s Guide for SOL QPSOL a FORTRAN Package for Quadratec Programming written by Stanford University. Systems Optimization Laboratory and published by . This book was released on 1983 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report forms the user's guide of SOL/NPSOL, a set of Fortran subroutines designed to minimize an arbitrary smooth function subject to constraints, which may include simple bounds on the variables, linear constraints and smooth nonlinear constraints (NPSOL may also be used for uncontrained, bound-constrained and linearly constrained optimization.) The user must provide subroutines that define the objective and constraint functions and their gradients. All matrices are treated as dense, and hence NPSOL is not intended for large sparse problems. NPSOL uses a sequential quadratic programming (SQP) algorithm, in which the search direction is the solution of a quadratic programming (QP) subproblem. The algorithm treats bounds, linear constraints and nonlinear constraints separately. The Hessian of each QP subproblem is a positive-definite quasi-Newton approximation to the Hessian of an augmented Lagrangian function. The steplength at each iteration is required to produce a sufficient decrease in an augmented Lagrangian merit function. Each QP subproblem is solved using a quadratic programming package with several features that improve the efficiency of an SQP algorithm.

Download or read book User s Guide for SOL QPSOL written by and published by . This book was released on 1983 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This report forms the user's guide for Version 3.1 of SOL/QPSOL, a set of Fortran subroutines designed to locate the minimum value of an arbitrary quadratic function subject to linear constraints and simple upper and lower bounds. If the quadratic function is convex, a global minimum is found; otherwise, a local minimum is found. The method used is most efficient when many constraints or bounds are active at the solution. QPSOL treats the Hessian and general constraints as dense matrices, and hence is not intended for large sparse problems. This document replaces the previous user's guide of June 1982.

Download or read book User s Guide for SOL QPSOL a FORTRAN Package for Quadratic Programming written by Stanford University. Systems Optimization Laboratory and published by . This book was released on 1982 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book User s Guide for QPSOL Version 3 2 A Fortran Package for Quadratic Programming Revision written by P. E. Gill and published by . This book was released on 1984 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report forms the user's guide for Version 3.2 of QPSOL, a set of Fortran subroutines designed to locate the minimum value of a quadratic function subject to linear constraints and simple upper and lower bounds. If the quadratic function is convex, a global minimum is found; otherwise, a local minimum is found. The method used is most efficient when many constraints or bounds are active at the solution. QPSOL treats the Hessian and general constraints as dense matrices, and hence is not intended for large sparse problems. This document replaces he previous user's guide of July 1983.

Download or read book LSSOL Version 1 0 a Fortran Package for Constrained Linear Least Squares and Convex Quadratic Programming User s Guide written by Philip E. Gill and published by . This book was released on 1986 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report forms the user's guide for Version 1.0 of LSSOL, a set of Fortran 77 subroutines for linearly constrained linear least-squares and convex quadratic programming. The method of LSSOL is of the two-phase, active-set type, and is related to the method used in the package SOL/QPSOL. Two main features of LSSOL are its exploitation of convexity and treatment of singularity. LSSOL may also be used for linear programming, and to find a feasible point with respect to a set of linear inequality constraints. LSSOL treats all matrices as dense, and hence is not intended for large sparse problems. Keywords: Algorithms; Parameters; Optimization; Linear programming; Mathematical software. (Author).