Journal ArticleDOI
An interior-point method for efficient solution of block-structured NLP problems using an implicit Schur-complement decomposition
TLDR
This paper shows that this bottleneck can be overcome by solving the Schur-complement equations implicitly, using a quasi-Newton preconditioned conjugate gradient method and dramatically reduces the computational cost for problems with many coupling variables.About:
This article is published in Computers & Chemical Engineering.The article was published on 2014-12-04. It has received 48 citations till now. The article focuses on the topics: Conjugate gradient method & Interior point method.read more
Citations
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Journal ArticleDOI
FORCES NLP: an efficient implementation of interior-point methods for multistage nonlinear nonconvex programs
TL;DR: The purpose of this paper is to demonstrate that, using simple standard building blocks from nonlinear programming, combined with a structure-exploiting linear system solver, it is possible to achieve computation times in the range typical of solvers for QPs, while retaining nonlinearities and solving the nonlinear programs (NLP) to local optimality.
Journal ArticleDOI
Reprint of: Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection
TL;DR: This work proposes to use community detection in network representations of optimization problems as a systematic method of partitioning the optimization variables into groups, such that the variables in the same groups generally share more constraints than variables between different groups.
Journal ArticleDOI
Minimization of energy consumption in multi-stage evaporator system of Kraft recovery process using Interior-Point Method
TL;DR: In this paper, a nonlinear mathematical model of heptads' effect backward feed flow with various energy saving schemes namely, steam split, feed-split, feed preheating and their hybrid operations have been developed.
Journal ArticleDOI
GPU-Accelerated Stochastic Predictive Control of Drinking Water Networks
TL;DR: In this paper, a proximal gradient algorithm is proposed to parallelize the involved operations of scenario-based stochastic model predictive control for the operational control of water networks, which is applied and validated on a case study: the water network of the city of Barcelona.
Journal ArticleDOI
Clustering-based preconditioning for stochastic programs
TL;DR: A clustering-based preconditioning strategy for KKT systems arising in stochastic programming within an interior-point framework that can avoid scalability issues of Schur decomposition in problems with large first-stage dimensionality.
References
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Book
Numerical Optimization
Jorge Nocedal,Stephen J. Wright +1 more
TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
Book
Iterative Methods for Sparse Linear Systems
TL;DR: This chapter discusses methods related to the normal equations of linear algebra, and some of the techniques used in this chapter were derived from previous chapters of this book.
Journal ArticleDOI
On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
TL;DR: A comprehensive description of the primal-dual interior-point algorithm with a filter line-search method for nonlinear programming is provided, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix.
Book
AMPL: A Modeling Language for Mathematical Programming
Robert Fourer,Brian W. Kernighan +1 more
TL;DR: An efficient translator is implemented that takes as input a linear AMPL model and associated data, and produces output suitable for standard linear programming optimizers.
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
TL;DR: The Conjugate Gradient Method as discussed by the authors is the most prominent iterative method for solving sparse systems of linear equations and is a composite of simple, elegant ideas that almost anyone can understand.