Topic
Discrete optimization
About: Discrete optimization is a research topic. Over the lifetime, 4598 publications have been published within this topic receiving 158297 citations. The topic is also known as: discrete optimisation.
Papers published on a yearly basis
Papers
More filters
Book•
01 Jan 1994
TL;DR: General Optimality Conditions via a Separation Scheme F. Giannessi, G. Di Pillo, V.G. Evtushenko, M. Potapov, and M.W. Dixon.
Abstract: General Optimality Conditions via a Separation Scheme F. Giannessi. Linear Equations in Optimization C.G. Broyden. Generalized and Sparse Least Squares Problems A. Bjoerck. Algorithms for Solving Nonlinear Systems of Equations J.M. Martinez. An Overview of Unconstrained Optimization R. Fletcher. Nonquadratic Model Methods in Unconstrained Optimization Naiyang Deng, Zhengfeng Li. Algorithms for General Constrained Nonlinear Optimization M.C. Bartholomew-Biggs. Exact Penalty Methods G. Di Pillo. Stable Barrier-projection and Barrier-Newton Methods for Linear and Nonlinear Programming Y.G. Evtushenko, V.G. Zhadan. Large-Scale Nonlinear Constrained Optimization - a Current Survey A.R. Conn, N. Gould, P.L. Toint. ABS Methods for Nonlinear Optimization E. Spedicato, Zunquan Xia. A Condensed Introduction to Bundle Methods in Nonsmooth Optimization C. Lemarechal, J. Zowe. Computational Methods for Linear Programming D.F. Shanno. Infeasible Interior Point Methods for Solving Linear Programs J. Stoer. Algorithms for Linear Complementarity Problems J.J. Judice. A Homework Exercise - the "Big M" Problem R.W.H. Sargent. Deterministic Global Optimization Y.G. Evtushenko, M.A. Potapov. On Automatic Differentiation and Continuous Optimization L.C.W. Dixon. Neural Networks and Unconstrained Optimization L.C.W. Dixon. Parallel Nonlinear Optimization - Limitations, Challenges and Opportunities R.B. Schnabel.
80 citations
01 Dec 2002
TL;DR: This work proposes an optimization-via-simulation algorithm for use when the performance measure is estimated via a stochastic, discrete-event simulation, and the decision variables may be subject to deterministic linear integer constraints.
Abstract: We propose an optimization-via-simulation algorithm for use when the performance measure is estimated via a stochastic, discrete-event simulation, and the decision variables may be subject to deterministic linear integer constraints. Our approach-which consists of a global guidance system, a selection-of-the-best procedure, and local improvement-is globally convergent under very mild conditions.
80 citations
TL;DR: This paper presents a survey of the literature on discrete-event simulation optimization published in recent years, with a particular focus on discrete input parameter optimization.
Abstract: Discrete-event simulation optimization is a problem of significant interest to practitioners interested in extracting useful information about an actual (or yet to be designed) system that can be modeled using discrete-event simulation. This paper presents a survey of the literature on discrete-event simulation optimization published in recent years (1988 to the present), with a particular focus on discrete input parameter optimization. The discrete input parameter case differentiates techniques appropriate for small and for large numbers of feasible input parameter values. Examples of applications that illustrate these methods are also discussed.
79 citations
11 Oct 1999
TL;DR: A new vector-based definition of descent directions in discrete space is proposed and it is shown that the new definition does not obey the rules of calculus in continuous space, but provides a strong mathematical foundation for solving general nonlinear discrete optimization problems.
Abstract: In this paper we present a Lagrange-multiplier formulation of discrete constrained optimization problems, the associated discrete-space first-order necessary and sufficient conditions for saddle points, and an efficient first-order search procedure that looks for saddle points in discrete space. Our new theory provides a strong mathematical foundation for solving general nonlinear discrete optimization problems. Specifically, we propose a new vector-based definition of descent directions in discrete space and show that the new definition does not obey the rules of calculus in continuous space. Starting from the concept of saddle points and using only vector calculus, we then prove the discrete-space first-order necessary and sufficient conditions for saddle points. Using well-defined transformations on the constraint functions, we further prove that the set of discrete-space saddle points is the same as the set of constrained local minima, leading to the first-order necessary and sufficient conditions for constrained local minima. Based on the first-order conditions, we propose a local-search method to look for saddle points that satisfy the first-order conditions.
79 citations
TL;DR: In this paper, the authors present stationary NLP type models of gas networks that are primarily designed to include detailed nonlinear physics in the final optimization steps for mid-term planning problems after fixing discrete decisions with coarsely approximated physics.
Abstract: Economic reasons and the regulation of gas markets create a growing need for mathematical optimization of natural gas networks. Real life planning tasks often lead to highly complex and extremely challenging optimization problems whose numerical treatment requires a breakdown into several simplified problems to be solved by carefully chosen hierarchies of models and algorithms. This paper presents stationary NLP type models of gas networks that are primarily designed to include detailed nonlinear physics in the final optimization steps for mid term planning problems after fixing discrete decisions with coarsely approximated physics.
79 citations