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©2000 - 2002 Dipl.-Ing. Tobias Jockenhövel. All rights reserved. |
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[Dyn. Optimization] [DAE 2 NLP] [OCOMA] [NLP Solvers] [Flexitime] |
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Powerful NLP solvers are needed to solve the discretized dynamic optimization problem. Currently, the solvers SNOPT, IPOPT (both full space and reduced space) as well as a Trust region SQP solver (for system identification) are integrated in the OptControlCentre:
SNOPT SNOPT (invented by Philip Gill, Walter Murray and Michael Saunders) is a software package for solving large-scale optimization problems (linear and nonlinear programs). It is especially effective for nonlinear problems whose functions and gradients are expensive to evaluate for nonlinear problems, SNOPT employs a sparse SQP algorithm with limited-memory quasi-Newton approximations to the Hessian of Lagrangian. For more information, please visit the SNOPT Homepage.
IPOPT IPOPT implements a interior point method for nonlinear programming. Search directions (coming from a linearization of the optimality conditions) can be computed in a full-space version by solving a large symmetric linear system. IPOPT can employ second derivative information, if available, or otherwise approximate it by means of a quasi-Newton approach (BFGS and SR1). Global convergence of the method is ensured by a line search procedure, where one can choose from among several merit functions and a novel filter method. For more information you might want to visit http://dynopt.cheme.cmu.edu/andreasw and check out the "papers" section (particularly the Ph.D. thesis). IPOPT was developed by Andreas Wächter and Lorenz T. Biegler at the Carnegie Mellon University, Pittsburgh, U.S.A.
Trust Region SQP solver This is a large-scale NLP solver specially designed for system identification. Development by Nikhil Arora and Lorenz T. Biegler at the Carnegie Mellon University, Pittsburgh, U.S.A. |