　　《线性和非线性规划（第3版）》这部研究运筹学的经典教材，在原来版本的基本上做了大量的修订补充，涵盖了这个运算领域的大量的理论洞见，是各行各业分析学者和运筹学研究人员所必需的。书中将运筹问题的纯分析特性和解决其的算术行为联系起来，将新鲜的第一手运筹学方法包括其中。目次：导论；（线性规划）：线性规划的基本性质；单纯型方法；对偶；内部点方法；运输和网络流问题；（无条件问题）解的基本特性和运算；基本下降方法；共轭方向法；拟牛顿法；原始方法；惩罚和柱式开采法；对偶和割平面方法；原始对偶方法；附录A：数学回顾；凸集合；高斯估计。

　　《线性和非线性规划（第3版）》读者对象：数学、特别是运筹学专业的高年级本科生、研究生和工程人员。

内容简介

　　This book is intended as a text covering the central concepts of practical optimization techniques. It is designed for either self-study by professionals or classroom work at the undergraduate or graduate level for students who have a technical background in engineering mathematics or science. Like the field of optimization itself which involves many classicaf disciplines the book should be useful to system analysts operations researchers numerical analysts management scientists and other specialists from the host ofdisciplines from which practicaloptimization appli- cations are drawn. The prerequisites for convenient use of the book are relatively modest; the prime requirement being some familiarity with introductory elements of linear algebra. Certain sections and developments do assume some knowledge of more advanced concepts of linear algebra such as eigenvector analysisk_or some background in sets of real numbers but the text is structured so that the mainstrean/ of the development can be faithfully pursued without reliance on trus more.advanced background material.

作者简介

　　David G. Luenberger（D.G.吕恩博格，美国），是国际知名学者，在数学和物理学界享有盛誉。本书凝聚了作者多年科研和教学成果，适用于科研工作者、高校教师和研究生。

内页插图

Chapter l.Introdriction

PART I Linear Programming
Chapter 2.Basic Properties of Linear Programs
Chapter 3.The Simplex Method
Chapter 4.Dunlity
Chapter 5,Interior-Point Methods
Chapter 6. Transportation and Network Flow Problems

PART II Unconstrained Problems
Chapter 7.Basic Properhes of Solutions and Algorithms
Chapter 8.Basic Descent Methods
Chapter 9.Conjugate Direction Methods
Chapter 10.Quasi-Newton Methods

PART III Constrained Minimization
Chapter 11. Constrained Minimization Conditions
Chapter 12. Primal Methods
Appendix B. Convex Sets
Appendix C.Gaussian Elimination
Bibliography
Index