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Reduced Hessian Sequential Quadratic Programming Methods For Multidiscipline Design Optimization
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Book Synopsis Reduced Hessian Sequential Quadratic Programming Methods for Multidiscipline Design Optimization by : James P. Masciarelli
Download or read book Reduced Hessian Sequential Quadratic Programming Methods for Multidiscipline Design Optimization written by James P. Masciarelli and published by . This book was released on 1997 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multidisciplinary Design Optimization by : Natalia M. Alexandrov
Download or read book Multidisciplinary Design Optimization written by Natalia M. Alexandrov and published by SIAM. This book was released on 1997-01-01 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multidisciplinary design optimization (MDO) has recently emerged as a field of research and practice that brings together many previously disjointed disciplines and tools of engineering and mathematics. MDO can be described as a technology, environment, or methodology for the design of complex, coupled engineering systems, such as aircraft, automobiles, and other mechanisms, the behavior of which is determined by interacting subsystems.
Book Synopsis Quadratic Programming Methods for Tailored Reduced Hessian SQP by : Claudia Schmid
Download or read book Quadratic Programming Methods for Tailored Reduced Hessian SQP written by Claudia Schmid and published by . This book was released on 1993 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Reduced Hessian Successive Quadratic Programming for Realtime Optimization by : Claudia Schmid
Download or read book Reduced Hessian Successive Quadratic Programming for Realtime Optimization written by Claudia Schmid and published by . This book was released on 1994 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Reduced Hessian Successive Quadratic Programming (SQP) is well suited for the solution of large-scale process optimization problems with many variables and constraints but few degrees of freedom. The reduced space method involves four major steps: an initial preprocessing phase followed by an iterative procedure which requires the solution of a set of nonlinear equations, a QP subproblem and a line search. The overall performance of the algorithm depends directly on the robustness and computational efficiency of the techniques used to handle each of these sub-tasks. Here, we discuss improvements to all of these steps in order to specialize this approach to real-time optimization. A numerical comparison of reduced Hessian SQP with MINOS (Murtagh and Saunders, 1982, 1987) is provided for the optimization of the Sunoco Hydrocracker Fractionation Plant (Bailey et al., 1992). The case study consists of about 3000 variables and constraints and includes several scenarios related to parameter estimation and on-line process-wide optimization. A study of the effect of optimizing the DIB distillation column which constitutes a subproblem of the Sunoco example is also included. The results indicate that our algorithm is at least as robust and an order of magnitude faster than MINOS for this set of problems."
Author :Stanford University. Department of Operations Research. Systems Optimization Laboratory Publisher : ISBN 13 : Total Pages :98 pages Book Rating :4.F/5 ( download)
Book Synopsis Large-scale Sequential Quadratic Programming Algorithms by : Stanford University. Department of Operations Research. Systems Optimization Laboratory
Download or read book Large-scale Sequential Quadratic Programming Algorithms written by Stanford University. Department of Operations Research. Systems Optimization Laboratory and published by . This book was released on 1992 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Optimal Quadratic Programming Algorithms by : Zdenek Dostál
Download or read book Optimal Quadratic Programming Algorithms written by Zdenek Dostál and published by Springer Science & Business Media. This book was released on 2009-04-03 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quadratic programming (QP) is one advanced mathematical technique that allows for the optimization of a quadratic function in several variables in the presence of linear constraints. This book presents recently developed algorithms for solving large QP problems and focuses on algorithms which are, in a sense optimal, i.e., they can solve important classes of problems at a cost proportional to the number of unknowns. For each algorithm presented, the book details its classical predecessor, describes its drawbacks, introduces modifications that improve its performance, and demonstrates these improvements through numerical experiments. This self-contained monograph can serve as an introductory text on quadratic programming for graduate students and researchers. Additionally, since the solution of many nonlinear problems can be reduced to the solution of a sequence of QP problems, it can also be used as a convenient introduction to nonlinear programming.
Book Synopsis Large-scale Sequential Quadratic Programming Algorithms by :
Download or read book Large-scale Sequential Quadratic Programming Algorithms written by and published by . This book was released on 1992 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem addressed is the general nonlinear programming problem: finding a local minimizer for a nonlinear function subject to a mixture of nonlinear equality and inequality constraints. The methods studied are in the class of sequential quadratic programming (SQP) algorithms, which have previously proved successful for problems of moderate size. Our goal is to devise an SQP algorithm that is applicable to large-scale optimization problems, using sparse data structures and storing less curvature information but maintaining the property of superlinear convergence. The main features are: 1. The use of a quasi-Newton approximation to the reduced Hessian of the Lagrangian function. Only an estimate of the reduced Hessian matrix is required by our algorithm. The impact of not having available the full Hessian approximation is studied and alternative estimates are constructed. 2. The use of a transformation matrix Q. This allows the QP gradient to be computed easily when only the reduced Hessian approximation is maintained. 3. The use of a reduced-gradient form of the basis for the null space of the working set. This choice of basis is more practical than an orthogonal null-space basis for large-scale problems. The continuity condition for this choice is proven. 4. The use of incomplete solutions of quadratic programming subproblems. Certain iterates generated by an active-set method for the QP subproblem are used in place of the QP minimizer to define the search direction for the nonlinear problem. An implementation of the new algorithm has been obtained by modifying the code MINOS. Results and comparisons with MINOS and NPSOL are given for the new algorithm on a set of 92 test problems.
Book Synopsis New Approaches to a Reduced Hessian Successive Quadratic Programming Method for Large-scale Process Optimization by : David J. Ternet
Download or read book New Approaches to a Reduced Hessian Successive Quadratic Programming Method for Large-scale Process Optimization written by David J. Ternet and published by . This book was released on 1998 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Sequential Quadratic Programming Methods Based on Approximating a Projected Hessian Matrix (Classic Reprint) by : Chaya Bleich Gurwitz
Download or read book Sequential Quadratic Programming Methods Based on Approximating a Projected Hessian Matrix (Classic Reprint) written by Chaya Bleich Gurwitz and published by . This book was released on 2015-08-05 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Excerpt from Sequential Quadratic Programming Methods Based on Approximating a Projected Hessian Matrix We consider the nonlinear programming problem, namely minimizing a nonlinear function subject to a set of nonlinear equality and inequality constraints. Sequential quadratic programming (SQP) methods are particularly effective for solving problems of this nature. It is assumed that first derivatives of the objective and constraint functions are available, but that second derivatives may be too expensive to compute. Instead, the methods typically update a suitable matrix which approximates second derivative information at each iteration. We are interested in developing SQP methods which maintain an approximation to second derivative information projected onto the tangent space of the constraints. The main motivation for our work is that only the projected matrix enters into the optimality conditions for the nonlinear problem. Updating projected second derivative information reduces the dimension of the matrix to be recurred; we avoid the necessity of introducing an augmenting term which can lead to ill-conditioned matrices; and we are able to make use of standard quasi-Newton updates which maintain hereditary positive definiteness. We discuss four possible formulations of the quadratic programming subproblem and present numerical results which indicate that our methods may be useful in practice. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Book Synopsis Sequential Quadratic Programming Algorithms for Optimization by : Francisco Javier Prieto
Download or read book Sequential Quadratic Programming Algorithms for Optimization written by Francisco Javier Prieto and published by . This book was released on 1989 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Sequential Quadratic Programming Methods Based on Approximating a Projected Hessian Matrix by : Chaya Bleich Gurwitz
Download or read book Sequential Quadratic Programming Methods Based on Approximating a Projected Hessian Matrix written by Chaya Bleich Gurwitz and published by . This book was released on 1986 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Sequential Quadratic Programming Methods Based on Approximating a Projected Hessian Matrix by : Chaya Bleich Gurwitz
Download or read book Sequential Quadratic Programming Methods Based on Approximating a Projected Hessian Matrix written by Chaya Bleich Gurwitz and published by Franklin Classics Trade Press. This book was released on 2018-11-11 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work has been selected by scholars as being culturally important and is part of the knowledge base of civilization as we know it. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. To ensure a quality reading experience, this work has been proofread and republished using a format that seamlessly blends the original graphical elements with text in an easy-to-read typeface. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Book Synopsis A Single-phase Method for Quadratic Programming by : Stanford University. Systems Optimization Laboratory
Download or read book A Single-phase Method for Quadratic Programming written by Stanford University. Systems Optimization Laboratory and published by . This book was released on 1986 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report describes a single-phase quadratic programming method, an active-set method which solves a sequence of equality-constraint quadratic programs.
Book Synopsis Parallel Computational Fluid Dynamics '99 by : D. Keyes
Download or read book Parallel Computational Fluid Dynamics '99 written by D. Keyes and published by Elsevier. This book was released on 2000-10-18 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contributed presentations were given by over 50 researchers representing the state of parallel CFD art and architecture from Asia, Europe, and North America. Major developments at the 1999 meeting were: (1) the effective use of as many as 2048 processors in implicit computations in CFD, (2) the acceptance that parallelism is now the 'easy part' of large-scale CFD compared to the difficulty of getting good per-node performance on the latest fast-clocked commodity processors with cache-based memory systems, (3) favorable prospects for Lattice-Boltzmann computations in CFD (especially for problems that Eulerian and even Lagrangian techniques do not handle well, such as two-phase flows and flows with exceedingly multiple-connected demains with a lot of holes in them, but even for conventional flows already handled well with the continuum-based approaches of PDEs), and (4) the nascent integration of optimization and very large-scale CFD. Further details of Parallel CFD'99, as well as other conferences in this series, are available at http://www.parcfd.org
Book Synopsis Acceleration of Reduced Hessian Methods for Large-scale Nonlinear Programming by : Claudia Schmid
Download or read book Acceleration of Reduced Hessian Methods for Large-scale Nonlinear Programming written by Claudia Schmid and published by . This book was released on 1992 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Process optimization problems are frequently characterized by large models, with many variables and constraints but relatively few degrees of freedom. Thus, reduced Hessian decomposition methods applied to Successive Quadratic Programming (SQP) exploit the low dimensionality of the subspace of the decision variables, and have been very successful for a wide variety of process application. However, further development is needed for improving the efficient large-scale use of these tools. In this study we develop an improved SQP algorithm decomposition with coordinate bases that includes an inexpensive second order correction term. The resulting algorithm is 1-step Q-superlinearly convergent. More importantly, though, the resulting algorithm is largely independent of the specific decomposition steps. Thus, the inexpensive factorization of the coordinate decomposition, which lends itself very well to tailoring, can be applied in a reliable and efficient manner. With this efficient and easy-to-implement NLP strategy, we continue to improve the efficiency of the optimization algorithm by exploiting the mathematical structure of existing process engineering models. Here we consider the tailoring of a reduced Hessian method for the block tridiagonal structure of the model equations for distillation columns. This approach is applied to the Naphthali-Sandholm algorithm implemented within the UNIDIST and programs. Our reduced Hessian SQP strategy is incorporated within the package with only minor changes in the program's interface and data structures. Through this integration, reductions of 20% to 80% in the total CPU time are obtained compared to general reduced space optimization; an order of magnitude reduction is obtained when compared to conventional sequential strategies. Consequently, this approach shows considerable potential for efficient and reliable large-scale process optimization, particularly when complex Newton-based process models are already available."
Book Synopsis A Regularized Active-Set method For Sparse Convex Quadratic Programming by :
Download or read book A Regularized Active-Set method For Sparse Convex Quadratic Programming written by and published by Stanford University. This book was released on with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis New Approaches to a Reduced Hessian Successive Quadratic Programming Algorithm for Large-scale Process Optimization by : David J. Ternet
Download or read book New Approaches to a Reduced Hessian Successive Quadratic Programming Algorithm for Large-scale Process Optimization written by David J. Ternet and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
