## subcategories

**Combinatorial** (5)

## e-books in Applied Mathematics: Optimization category

**Convex Optimization: Algorithms and Complexity**

by

**Sebastien Bubeck**-

**arXiv.org**,

**2015**

This text presents the main complexity theorems in convex optimization and their algorithms. Starting from the fundamental theory of black-box optimization, the material progresses towards recent advances in structural and stochastic optimization.

(

**5177**views)

**Data Assimilation: A Mathematical Introduction**

by

**K.J.H. Law, A.M. Stuart, K.C. Zygalakis**-

**arXiv.org**,

**2015**

This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation. Authors develop a framework in which a Bayesian formulation of the problem provides the bedrock for the derivation and analysis of algorithms.

(

**4880**views)

**An Introduction to Nonlinear Optimization Theory**

by

**Marius Durea, Radu Strugariu**-

**De Gruyter Open**,

**2014**

Starting with the case of differentiable data and the classical results on constrained optimization problems, continuing with the topic of nonsmooth objects involved in optimization, the book concentrates on both theoretical and practical aspects.

(

**6676**views)

**Universal Optimization and Its Application**

by

**Alexander Bolonkin**-

**viXra.org**,

**2017**

This book describes new method of optimization (''Method of Deformation of Functional'') that has the advantages at greater generality and flexibility as well as the ability to solve complex problems which other methods cannot solve.

(

**5119**views)

**Optimization Algorithms: Methods and Applications**

by

**Ozgur Baskan (ed.)**-

**InTech**,

**2016**

This book covers state-of-the-art optimization methods and their applications in wide range especially for researchers and practitioners who wish to improve their knowledge in this field. It covers applications in engineering and various other areas.

(

**6398**views)

**Decision Making and Productivity Measurement**

by

**Dariush Khezrimotlagh**-

**arXiv**,

**2016**

I wrote this book as a self-teaching tool to assist every teacher, student, mathematician or non-mathematician, and to support their understanding of the elementary concepts on assessing the performance of a set of homogenous firms ...

(

**5884**views)

**A Practical Guide to Robust Optimization**

by

**Bram L. Gorissen, Ihsan Yanıkoğlu, Dick den Hertog**-

**arXiv**,

**2015**

The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do's and don'ts for using it in practice.

(

**6658**views)

**Optimization Models For Decision Making**

by

**Katta G. Murty**-

**Springer**,

**2010**

This is a Junior level book on some versatile optimization models for decision making in common use. The aim of this book is to develop skills in mathematical modeling, and in algorithms and computational methods to solve and analyze these models.

(

**10307**views)

**Linear Programming**

by

**Jim Burke**-

**University of Washington**,

**2012**

These are notes for an introductory course in linear programming. The four basic components of the course are modeling, solution methodology, duality theory, and sensitivity analysis. We focus on the simplex algorithm due to George Dantzig.

(

**7276**views)

**Discrete Optimization**

by

**Guido Schaefer**-

**Utrecht University**,

**2012**

From the table of contents: Preliminaries (Optimization Problems); Minimum Spanning Trees; Matroids; Shortest Paths; Maximum Flows; Minimum Cost Flows; Matchings; Integrality of Polyhedra; Complexity Theory; Approximation Algorithms.

(

**8187**views)

**Robust Optimization**

by

**A. Ben-Tal, L. El Ghaoui, A. Nemirovski**-

**Princeton University Press**,

**2009**

Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of this relatively new approach to optimization.

(

**9803**views)

**Lectures on Optimization: Theory and Algorithms**

by

**John Cea**-

**Tata Institute of Fundamental Research**,

**1978**

Contents: Differential Calculus in Normed Linear Spaces; Minimization of Functionals; Minimization Without Constraints; Minimization with Constraints; Duality and Its Applications; Elements of the Theory of Control and Elements of Optimal Design.

(

**10117**views)

**Iterative Methods for Optimization**

by

**C.T. Kelley**-

**Society for Industrial Mathematics**,

**1987**

This book presents a carefully selected group of methods for unconstrained and bound constrained optimization problems and analyzes them in depth both theoretically and algorithmically. It focuses on clarity in algorithmic description and analysis.

(

**10002**views)

**Applied Mathematical Programming Using Algebraic Systems**

by

**Bruce A. McCarl, Thomas H. Spreen**-

**Texas A&M University**,

**2011**

This book is intended to both serve as a reference guide and a text for a course on Applied Mathematical Programming. The text concentrates upon conceptual issues, problem formulation, computerized problem solution, and results interpretation.

(

**11282**views)

**Optimal Stopping and Applications**

by

**Thomas S. Ferguson**-

**UCLA**,

**2008**

From the table of contents: Stopping Rule Problems; Finite Horizon Problems; The Existence of Optimal Rules; Applications. Markov Models; Monotone Stopping Rule Problems; Maximizing the Rate of Return; Bandit Problems; Solutions to the Exercises.

(

**11899**views)

**The Design of Approximation Algorithms**

by

**D. P. Williamson, D. B. Shmoys**-

**Cambridge University Press**,

**2010**

This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. It is organized around techniques for designing approximation algorithms, including greedy and local search algorithms.

(

**14865**views)

**Applied Mathematical Programming**

by

**S. Bradley, A. Hax, T. Magnanti**-

**Addison-Wesley**,

**1977**

This book shows you how to model a wide array of problems. Covered are topics such as linear programming, duality theory, sensitivity analysis, network/dynamic programming, integer programming, non-linear programming, and my favorite, etc.

(

**17941**views)

**Linear Complementarity, Linear and Nonlinear Programming**

by

**Katta G. Murty**,

**1997**

This book provides an in-depth and clear treatment of all the important practical, technical, computational, geometric, and mathematical aspects of the Linear Complementarity Problem, Quadratic Programming, and their various applications.

(

**10682**views)

**Optimization and Dynamical Systems**

by

**U. Helmke, J. B. Moore**-

**Springer**,

**1996**

Aimed at mathematics and engineering graduate students and researchers in the areas of optimization, dynamical systems, control systems, signal processing, and linear algebra. The problems solved are those of linear algebra and linear systems theory.

(

**13287**views)

**Notes on Optimization**

by

**Pravin Varaiya**-

**Van Nostrand**,

**1972**

The author presents the main concepts mathematical programming and optimal control to students having diverse technical backgrounds. A reasonable knowledge of advanced calculus, linear algebra, and linear differential equations is required.

(

**11198**views)

**Optimization Algorithms on Matrix Manifolds**

by

**P.-A. Absil, R. Mahony, R. Sepulchre**-

**Princeton University Press**,

**2007**

Many science and engineering problems can be rephrased as optimization problems on matrix search spaces endowed with a manifold structure. This book shows how to exploit the structure of such problems to develop efficient numerical algorithms.

(

**16952**views)

**Convex Optimization**

by

**Stephen Boyd, Lieven Vandenberghe**-

**Cambridge University Press**,

**2004**

A comprehensive introduction to the subject for students and practitioners in engineering, computer science, mathematics, statistics, finance, etc. The book shows in detail how optimization problems can be solved numerically with great efficiency.

(

**18148**views)