Top Research Papers on Optimization Techniques

Some common analytical and numerical techniques of finding the maximum or a minimum of multivariable functions are presented, including Lagrange's method of undetermined multipliers, linear and dynamic programming.

Mathematical optimization deals with the problem of finding (or approximating) a point that gives an optimal (minimal or maximal) value to some function (called the objective function), subject to some additional conditions (called constraints).

Examples of how the newer techniques have contributed to the improvement in technological processes are presented, and the principles of constraint and uncertainty programming outlined.

This chapter proposes to use the low-polynomial cost approximation method presented in (Bar-Yehuda, 2001) to solve the DBW minimization problem and proposes to use L 2 to approximate g(L 2) (as defined in Section 3.1 of Chapter 3) to solve the AT Data Sep optimization problem.

Optimization techniques have been successfully applied to the solution of electric utility system operating and planning problems, and significant system improvements have been achieved.

A basic overview of optimization techniques is provided, and the standard form of the general non-linear, constrained optimization problem is presented, and various techniques for solving the resulting optimization problem are discussed.

The particle swirl algorithm (PSA) utilizes multiple solutions ("particles") that rotate or "swirl" around a center point based on the principle of water spiraling toward a drain, so the center point is known as the vortex.

This paper discusses the popular evolutionary optimization technique, Genetic Algorithm (GA) and Teaching-learningbased Optimization (TLBO) algorithm and the definitions of various parameters used by these algorithms.

Researchers tend to come back to genetic and evolutionary algorithms recently as they are suited for parallel processing, finding global optima, and are reported to be suitable for a large number of design variables.

The papers collected in this volume were presented at the Symposium on Mathematical Optimization Techniques held in the Santa Monica Civic Auditorium, Santa Monica, California, on October 18-20, 1960. The objective of the symposium was to bring together, for the purpose of mutual education, mathematicians, scientists, and engineers interested in modern optimization techniques. Some 250 persons attended. The techniques discussed included recent developments in linear, integer, convex, and dynamic programming as well as the variational processes surrounding optimal guidance, flight trajectories,...

Synopsis: Classical Optimisation Techniques, Optimisation and Inequalities, Numerical Methods of Optimisation, Linear Programming Techniques, Non-linear Programming Techniques, Dynamic Programming Methods, Variational Methods, Stochastic Approximation Procedures, Optimisation in Simulation, Optimisation in Function Spaces Classical Optimisation Techniques: Preliminaries, Necessary and Sufficient Conditions for an Extremum, Constrained Optimisation - Lagrange Multipliers, Statistical Applications Optimisation and Inequalities: Classical Inequalities, Matrix Inequalities, Applications Numerical ...

This paper outlines the steps taken to reduce the processing time of a Reed-Solomon encoding and the insight into modern optimization techniques gained from the experience.

The aim of this book is to provide a Discussion of Constrained Optimization and its Applications to Linear Programming and Other Optimization Problems.

An overview of various kinds of optimization problems will be given with examples in the world of applications, and various techniques of handling such problems to get rough, workable solutions will be discussed.

There are some tasks that nature manages to perform very easily but which algorithms designed by human beings cannot complete, and these tasks can find these tasks in complicated and variable environments.

Optimization issue formulation, optimization methodologies, and solution approaches are discussed and optimization utilizing constraints in terms of dependability is shown to be the optimal choice.

A very simple method to find the extremum of a n‐variables function leads to the optimization of a one‐variable function and can be applied to two kinds of cybernetic problems: resolution of equations with several variables and identification of the black box.

It is demonstrated how simple phantoms may be used to improve image quality so that high-quality images can be attained consistently and is provided to enable the reader to develop their own practical diffusion-weighted imaging protocols in the body.

This study was conducted to provide the experimental results of the existing compiler technique as a reference for the development of multibank memory architecture.

In Gordon’s executive routine the priority of a task is calculated as a simple, usually linear, function of the time elapsed since the task was last called, with the initial priority of each task preassigned.