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
TL;DR: It is shown that, for a discrete problem, GMRFs, a type ofGaussian process defined on a graph, provides better inference on the remaining optimality gap than the typical choice of continuous Gaussian process and thereby enables the algorithm to search efficiently and stop correctly when the remaining Optimality gap is below a predefined threshold.
Abstract: This paper lays the foundation for employing Gaussian Markov random fields (GMRFs) for discrete decision–variable optimization via simulation; that is, optimizing the performance of a simulated sys...
46 citations
TL;DR: This paper investigates ways to derive semidefinite programs from discrete optimization problems and deals with the approximation of integer problems both in a theoretical setting, and from a computational point of view.
46 citations
TL;DR: The present method has insensitive performance to variations of the design variables within the selected discrete values enhancing the feasibility of constraints, and ranking the estimators of the characteristic function is developed to enhance feasibility.
Abstract: Robust design in discrete design space is defined as a discrete design that is insensitive to external uncertainties or variations. The application of robust discrete design is not prevalent yet due to high computational cost. A relatively simple method is proposed to select discrete and robust optimum. At first, the discrete design is achieved as the postprocess of conventional optimization. An orthogonal array is established around a conventional optimum, and the characteristic functions are evaluated. The characteristic function is defined by considering the robustness of the objective and constraints. The parameter design of the Taguchi method is introduced to obtain the robust solution in discrete space. The present method has insensitive performance to variations of the design variables within the selected discrete values enhancing the feasibility of constraints. To enhance feasibility, ranking the estimators of the characteristic function is developed. Several structural problems are solved to show the usefulness of the present method.
46 citations
TL;DR: Experimental results show that DTSA is another qualified and competitive solver on discrete optimization in nature inspired population-based iterative search algorithm.
46 citations
TL;DR: A novel class of methods for solving path and end point constrained dynamic optimization problems is proposed, based on the reformulation of the dynamic model constraints into a higher order differential model representation, in which state variable derivatives are eliminated.
46 citations