Author: Stanford University. Department of Operations Research. Systems Optimization LaboratoryPublisher:
ISBN:
Category :
Languages : en
Pages : 142
Book Description
Barrier Methods for Large-scale Quadratic Programming
Author: Stanford University. Department of Operations Research. Systems Optimization LaboratoryPublisher:
ISBN:
Category :
Languages : en
Pages : 142
Book Description
A Penalty/modified Barrier Method for Large-scale Quadratic Programming
Author: S. A. BrooksA Long Step Barrier Method for Convex Quadratic Programming
Author: K. M. AnstreicherLarge-Scale Nonlinear Optimization
Author: Gianni PilloPublisher: Springer Science & Business Media
ISBN: 0387300651
Category : Mathematics
Languages : en
Pages : 297
Book Description
This book reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research. Individual chapters, contributed by eminent authorities, provide an up-to-date overview of the field from different and complementary standpoints, including theoretical analysis, algorithmic development, implementation issues and applications.
Efficient Methods for Large-scale Sparse Linear and Quadratic Programs
Author: Seyoung OhStable Reduction to KKT Systems in Barrier Methods for Linear and Quadratic Programming
Author: Stanford University. Engineering-Economic Systems and Operations Research Department. Systems Optimization LaboratoryPublisher:
ISBN:
Category : Linear programming
Languages : en
Pages : 16
Book Description
Abstract: "We discuss methods for solving the key linear equations within primal-dual barrier methods for linear and quadratic programming. Following Freund and Jarre, we explore methods for reducing the Newton equations to 2 X 2 block systems (KKT systems) in a stable manner. Some methods require partitioning the variables into two or more parts, but a simpler approach is derived and recommended. To justify symmetrizing the KKT systems, we assume the use of a sparse solver whose numerical properties are independent of row and column scaling. In particular, we regularize the problem and use indefinite Cholesky-type factorizations. An implementation within OSL is tested on the larger NETLIB examples."
Large Scale Optimization
Author: William W. HagerPublisher: Springer Science & Business Media
ISBN: 1461336325
Category : Mathematics
Languages : en
Pages : 470
Book Description
On February 15-17, 1993, a conference on Large Scale Optimization, hosted by the Center for Applied Optimization, was held at the University of Florida. The con ference was supported by the National Science Foundation, the U. S. Army Research Office, and the University of Florida, with endorsements from SIAM, MPS, ORSA and IMACS. Forty one invited speakers presented papers on mathematical program ming and optimal control topics with an emphasis on algorithm development, real world applications and numerical results. Participants from Canada, Japan, Sweden, The Netherlands, Germany, Belgium, Greece, and Denmark gave the meeting an important international component. At tendees also included representatives from IBM, American Airlines, US Air, United Parcel Serice, AT & T Bell Labs, Thinking Machines, Army High Performance Com puting Research Center, and Argonne National Laboratory. In addition, the NSF sponsored attendance of thirteen graduate students from universities in the United States and abroad. Accurate modeling of scientific problems often leads to the formulation of large scale optimization problems involving thousands of continuous and/or discrete vari ables. Large scale optimization has seen a dramatic increase in activities in the past decade. This has been a natural consequence of new algorithmic developments and of the increased power of computers. For example, decomposition ideas proposed by G. Dantzig and P. Wolfe in the 1960's, are now implement able in distributed process ing systems, and today many optimization codes have been implemented on parallel machines.
Large-Scale PDE-Constrained Optimization
Author: Lorenz T. BieglerPublisher: Springer Science & Business Media
ISBN: 364255508X
Category : Mathematics
Languages : en
Pages : 347
Book Description
Optimal design, optimal control, and parameter estimation of systems governed by partial differential equations (PDEs) give rise to a class of problems known as PDE-constrained optimization. The size and complexity of the discretized PDEs often pose significant challenges for contemporary optimization methods. With the maturing of technology for PDE simulation, interest has now increased in PDE-based optimization. The chapters in this volume collectively assess the state of the art in PDE-constrained optimization, identify challenges to optimization presented by modern highly parallel PDE simulation codes, and discuss promising algorithmic and software approaches for addressing them. These contributions represent current research of two strong scientific computing communities, in optimization and PDE simulation. This volume merges perspectives in these two different areas and identifies interesting open questions for further research.
Methods for Large-scale Extended Linear-quadratic Programming
Author: Ciyou ZhuPublisher:
ISBN:
Category : Quadratic programming
Languages : en
Pages : 180
Book Description
Linear Programming 2
Author: George B. DantzigPublisher: Springer Science & Business Media
ISBN: 0387215697
Category : Mathematics
Languages : en
Pages : 461
Book Description
George Dantzig is widely regarded as the founder of this subject with his invention of the simplex algorithm in the 1940's. In this second volume, the theory of the items discussed in the first volume is expanded to include such additional advanced topics as variants of the simplex method; interior point methods, GUB, decomposition, integer programming, and game theory. Graduate students in the fields of operations research, industrial engineering and applied mathematics will thus find this volume of particular interest.