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Subjects / keywords: LOOMBA, NARENDRA PAUL; MACMILLAN PUBLISHING CO; 658.4033 L863l2

Subjects / keywords: GASS, SAUL I; McGRAW-HILL BOOK COMPANY; 519.72 G251l4

A basic text in linear programming, the solution of systems of linear equalities. The subjects covered include the concepts, origins and formulations of linear programs, and the simplex method of solution as applied to t...

Subjects / keywords: KOLMAN, BERNARD; ACADEMIC PRESS; 519.72 K81e

This study aims to understand how students acquire the ability to model linear programming (LP) problems. Our motivation is to improve LP teaching through the employment of better teaching methods and modeling languages....

This paper considers a natural error correcting problem with real valued input/output. We wish to recover an input vector f/spl isin/R/sup n/ from corrupted measurements y=Af+e. Here, A is an m by n (coding) matrix and e...

Subjects / keywords: Karush–Kuhn–Tucker conditions; Nonlinear programming; Nonlinear system; Saddle point; Mathematical optimization; Duality (order theory); Mathematics; Linear programming; Function (biology); Convex optimization; Conve...

Introduction and Preliminaries. Problems, Algorithms, and Complexity. LINEAR ALGEBRA. Linear Algebra and Complexity. LATTICES AND LINEAR DIOPHANTINE EQUATIONS. Theory of Lattices and Linear Diophantine Equations. Algorit...

Operations Research, Management Science or Business Analytics is a technology that helps improve business decision making. This book is focused on linear, integer, nonlinear and multiobjective programming, as well as mul...

Previous article Next article An Algorithm for Least-Squares Estimation of Nonlinear ParametersDonald W. MarquardtDonald W. Marquardthttps://doi.org/10.1137/0111030PDFPDF PLUSBibTexSections ToolsAdd to favoritesExport Ci...

This clearly written , mathematically rigorous text includes a novel algorithmic exposition of the simplex method and also discusses the Soviet ellipsoid algorithm for linear programming; efficient algorithms for network...

A nonlinear (nonconvex) programming model provides a new definition of efficiency for use in evaluating activities of not-for-profit entities participating in public programs. A scalar measure of the efficiency of each p...

A new approach to optimizing or hedging a portfolio of financial instruments to reduce risk is presented and tested on applications. It focuses on minimizing Conditional Value-at-Risk (CVaR) rather than minimizing Value-...

FOUNDATIONS. The Scope of Integer and Combinatorial Optimization. Linear Programming. Graphs and Networks. Polyhedral Theory. Computational Complexity. Polynomial-Time Algorithms for Linear Programming. Integer Lattices....

Many optimization problems in various fields have been solved using diverse optimization al gorithms. Traditional optimization techniques such as linear programming (LP), non-linear programming (NLP), and dynamic program...

The time-frequency and time-scale communities have recently developed a large number of overcomplete waveform dictionaries --- stationary wavelets, wavelet packets, cosine packets, chirplets, and warplets, to name a few....

Abstract A new method for non‐linear programming in general and structural optimization in particular is presented. In each step of the iterative process, a strictly convex approximating subproblem is generated and sol...

We describe the implementation of a first-order linearly typed assembly language, HBAL, that allows the safe reuse of heap space for elements of different types Linear typing ensures the single pointer property, disallow...

The N-body problem is often characterized by the necessity to interact each of the N bodies with every other one. Its principal problem is the time spent in force computation. Computing the force among a set of N bodies...

The brms package implements Bayesian multilevel models in R using the probabilistic programming language Stan. A wide range of distributions and link functions are supported, allowing users to fit - among others - linear...

One of the problems which has plagued those attempting to predict the behavior of capital markets is the absence of a body of positive microeconomic theory dealing with conditions of risk. Although many useful insights c...

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression an...

This chapter describes a new algorithm for training Support Vector Machines: Sequential Minimal Optimization, or SMO. Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (Q...

Subjects / keywords: Linear programming; Algebras, Linear; Industrial management; Electronic books