User's Guide For SNOPT 5.3: A Fortran Package For Large-Scale Nonlinear Programming

User's Guide For SNOPT 5.3: A Fortran Package For Large-Scale Nonlinear Programming (1999)

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by Philip E. Gill , Walter Murray , Michael A. Saunders

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Datum	Value	Source
TITLE	User's Guide For SNOPT 5.3: A Fortran Package For Large-Scale Nonlinear Programming	user correction - Legacy Corrections
AUTHOR NAME	Philip E. Gill	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Mathematics; University of California, San Diego	user correction - Legacy Corrections
AUTHOR ADDR	La Jolla, California 92093-0112	user correction - Legacy Corrections
AUTHOR NAME	Walter Murray	user correction - Legacy Corrections
AUTHOR AFFIL	Systems Optimization Laboratory; Department of EESOR; Stanford University	user correction - Legacy Corrections
AUTHOR ADDR	Stanford, California 94305-4023	user correction - Legacy Corrections
AUTHOR NAME	Michael A. Saunders	user correction - Legacy Corrections
AUTHOR AFFIL	Systems Optimization Laboratory; Department of EESOR; Stanford University	user correction - Legacy Corrections
AUTHOR ADDR	Stanford, California 94305-4023	user correction - Legacy Corrections
ABSTRACT	SNOPT is a general-purpose system for solving optimization problems involving many variables and constraints. It minimizes a linear or nonlinear function subject to bounds on the variables and sparse linear or nonlinear constraints. It is suitable for large-scale linear and quadratic programming and for linearly constrained optimization, as well as for general nonlinear programs. SNOPT finds solutions that are locally optimal , and ideally any nonlinear functions should be smooth and users should provide gradients. It is often more widely useful. For example, local optima are often global solutions, and discontinuities in the function gradients can often be tolerated if they are not too close to an optimum. Unknown gradients are estimated by finite differences. SNOPT uses a sequential quadratic programming (SQP) algorithm that obtains search directions from a sequence of quadratic programming subproblems. Each QP subproblem minimizes a quadratic model of a certain Lagrangian function subject to a linearization of the constraints. An augmented Lagrangian merit function is reduced along each search direction to ensure convergence from any starting point. SNOPT is most efficient if only some of the variables enter nonlinearly, or if the number of active constraints (including simple bounds) is nearly as large as the number of variables. SNOPT requires relatively few evaluations of the problem functions. Hence it is especially effective if the objective or constraint functions (and their gradients) are expensive to evaluate. The source code for SNOPT is suitable for any machine with a Fortran compiler. SNOPT may be called from a driver program (typically in Fortran, C or MATLAB). SNOPT can also be used as a stand-alone package, reading data in the MPS ...	user correction - Legacy Corrections
YEAR	1999	user correction - Legacy Corrections
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Technical report	INFERENCE
CITATIONS	13 found	ParsCit 1.0