现货 Statistical Inference via Convex Optimization (Princeton 进口原版图书,现货速发
¥592.00
预订 Statistical Inference Via Convex Optimization 97806911972 海外仓库发货,通常付款后4-9周到货!
图书信息 书号: 9780691197296 作者: Juditsky, Anatoli;Nemirovski, Arkadi 装帧: 精装 页数: 632页 出版社: Princeton University Press 尺寸: 25.4 x 18.0 x 0.2 cm 出版日期: 1800-01-01 重量: 1270g 语种: 其它(含多语)
¥911.00
【预订】Introduction to Linear Optimization 9789811278730 国外库房发货,通常付款后3-5周到货!
Product Details 基本信息 ISBN-13 书号 9789811278730 Author 作者 Nemirovski Format 版本 平装-胶订 Pages Number 页数 648 pp页 Publisher 出版社 World Scientific Publishing Publication Date 出版日期 2024-01-29 Language 语种 其它(含多语) Book Contents 内容简介 The book presents a graduate level, rigorous, and self-contained introduction to linear optimization (LO), the presented topics being expressive abilities of LO; geometry of LO — structure of polyhedral sets, LO duality and its applications; traditional LO algorithms — primal and dual simplex methods, and network simplex method; polynomial time solvability of LO via ellipsoid algorithm; conic programming with emphasis on expressing abilities of second order and semidefinite optimization, and polynomial time primal-dual interior point algorithms for linear and semidefinite optimization.Key Features?Linear optimization has wide
¥805.00
【预订】Introduction to Linear Optimization 9789811277900 国外库房发货,通常付款后3-5周到货!
Product Details 基本信息 ISBN-13 书号 9789811277900 Author 作者 Nemirovski Format 版本 精装 Pages Number 页数 570 pp页 Publisher 出版社 World Scientific Publishing Publication Date 出版日期 2024-02-28 Language 语种 其它(含多语) Book Contents 内容简介 The book presents a graduate level, rigorous, and self-contained introduction to linear optimization (LO), the presented topics being expressive abilities of LO; geometry of LO — structure of polyhedral sets, LO duality and its applications; traditional LO algorithms — primal and dual simplex methods, and network simplex method; polynomial time solvability of LO via ellipsoid algorithm; conic programming with emphasis on expressing abilities of second order and semidefinite optimization, and polynomial time primal-dual interior point algorithms for linear and semidefinite optimization.Key Features?Linear optimization has wide applica
¥1613