Optimization

IBM ILOG CPLEX Optimization Studio uses decision optimization technology to optimize your business decisions, develop and deploy optimization models quickly, and create real-world applications that can significantly improve business outcomes.

Mastering Multi-Objective Optimization and Decision-Making with pymoo: Balancing Objectives, Finding Solutions

Guide for building optimization probelm (operation research) in Pyomo Jupyter and solve it using CPLEX, Gurobi and IPOPT

IBM ILOG CPLEX Optimization Studio uses decision optimization technology to optimize your business decisions, develop and deploy optimization models quickly, and create real-world applications that can significantly improve business outcomes.

IBM® ILOG® CPLEX® Optimization Studio uses decision optimization technology to optimize your business decisions, develop and deploy optimization models quickly, and create real-world applications that can significantly improve business outcomes.

An accessible introduction to metaheuristics and optimization, featuring powerful and modern algorithms for application across engineering and the sciences From engineering and computer science to economics and management science, optimization is a core component for problem solving. Highlighting the latest developments that have evolved in recent years, Engineering Optimization: An Introduction with Metaheuristic Applications outlines popular metaheuristic algorithms and equips readers with the skills needed to apply these techniques to their own optimization problems. With insightful examples from various fields of study, the author highlights key concepts and techniques for the successful application of commonly-used metaheuristc algorithms, including simulated annealing, particle swarm optimization, harmony search, and genetic algorithms.

Semidefinite and conic optimization is a major and thriving research area within the optimization community. Although semidefinite optimization has been studied (under different names) since at least the 1940s, its importance grew immensely during the 1990s after polynomial-time interior-point methods for linear optimization were extended to solve semidefinite optimization problems.

Semidefinite and conic optimization is a major and thriving research area within the optimization community. Although semidefinite optimization has been studied (under different names) since at least the 1940s, its importance grew immensely during the 1990s after polynomial-time interior-point methods for linear optimization were extended to solve semidefinite optimization problems.

This book systematically discusses nonlinear interval optimization design theory and methods. Firstly, adopting a mathematical programming theory perspective, it develops an innovative mathematical transformation model to deal with general nonlinear interval uncertain optimization problems, which is able to equivalently convert complex interval uncertain optimization problems to simple deterministic optimization problems.