%PDF-1.3
1 0 obj
<< /Type /Catalog
/Outlines 2 0 R
/Pages 3 0 R >>
endobj
2 0 obj
<< /Type /Outlines /Count 0 >>
endobj
3 0 obj
<< /Type /Pages
/Kids [6 0 R
9 0 R
11 0 R
13 0 R
15 0 R
17 0 R
19 0 R
21 0 R
23 0 R
25 0 R
27 0 R
29 0 R
31 0 R
33 0 R
35 0 R
37 0 R
39 0 R
41 0 R
43 0 R
45 0 R
47 0 R
49 0 R
51 0 R
53 0 R
55 0 R
57 0 R
]
/Count 26
/Resources <<
/ProcSet 4 0 R
/Font <<
/F1 8 0 R
>>
>>
/MediaBox [0.000 0.000 595.280 419.530]
>>
endobj
4 0 obj
[/PDF /Text ]
endobj
5 0 obj
<<
/Producer (Collabora Online Version: 9.1.0)
/CreationDate (D:20220926022639+00'00')
/ModDate (D:20220926022639+00'00')
/Title (Read Book Convex Optimization Stephen Boyd [PDF] - jason.wells.me)
/Subject (jason.wells.me)
/Author (Leaf Books)
/Keywords (Read Online Read Book Convex Optimization Stephen Boyd [PDF] - jason.wells.me)
>>
endobj
6 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 7 0 R
>>
endobj
7 0 obj
<<
/Length 1463 >>
stream
0.000 0.000 0.000 rg
BT 34.016 338.580 Td /F1 28.5 Tf [(Convex Optimization Stephen Boyd)] TJ ET
BT 34.016 284.356 Td /F1 14.2 Tf [(Getting the books )] TJ ET
BT 149.669 284.356 Td /F1 14.2 Tf [(Convex Optimization Stephen Boyd)] TJ ET
BT 374.605 284.356 Td /F1 14.2 Tf [( now is not type of inspiring )] TJ ET
BT 34.016 266.957 Td /F1 14.2 Tf [(means. You could not abandoned going subsequently book stock or library or )] TJ ET
BT 34.016 249.558 Td /F1 14.2 Tf [(borrowing from your contacts to contact them. This is an unconditionally simple )] TJ ET
BT 34.016 232.159 Td /F1 14.2 Tf [(means to specifically get lead by on-line. This online statement Convex )] TJ ET
BT 34.016 214.759 Td /F1 14.2 Tf [(Optimization Stephen Boyd can be one of the options to accompany you similar to )] TJ ET
BT 34.016 197.360 Td /F1 14.2 Tf [(having additional time.)] TJ ET
BT 34.016 162.861 Td /F1 14.2 Tf [(It will not waste your time. recognize me, the e-book will entirely space you )] TJ ET
BT 34.016 145.462 Td /F1 14.2 Tf [(supplementary business to read. Just invest little period to edit this on-line )] TJ ET
BT 34.016 128.062 Td /F1 14.2 Tf [(pronouncement )] TJ ET
BT 136.986 128.062 Td /F1 14.2 Tf [(Convex Optimization Stephen Boyd)] TJ ET
BT 361.922 128.062 Td /F1 14.2 Tf [( as capably as review them )] TJ ET
BT 34.016 110.663 Td /F1 14.2 Tf [(wherever you are now.)] TJ ET
BT 34.016 47.664 Td /F1 14.2 Tf [(Convex Optimization Theory)] TJ ET
endstream
endobj
8 0 obj
<< /Type /Font
/Subtype /Type1
/Name /F1
/BaseFont /Helvetica
/Encoding /WinAnsiEncoding
>>
endobj
9 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 10 0 R
>>
endobj
10 0 obj
<<
/Length 2435 >>
stream
0.000 0.000 0.000 rg
BT 34.016 371.595 Td /F1 14.2 Tf [(Dimitri Bertsekas 2009-06-01 An insightful, concise, )] TJ ET
BT 364.259 371.595 Td /F1 14.2 Tf [(and rigorous treatment of the )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(basic theory of convex sets and functions in finite dimensions, and the )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(analytical/geometrical foundations of convex optimization and duality theory. )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(Convexity theory is first developed in a simple accessible manner, using easily )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(visualized proofs. Then the focus shifts to a transparent geometrical line of )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(analysis to develop the fundamental duality between descriptions of convex )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(functions in terms of points, and in terms of hyperplanes. Finally, convexity theory )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(and abstract duality are applied to problems of constrained optimization, Fenchel )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(and conic duality, and game theory to develop the sharpest possible duality results )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(within a highly visual geometric framework. This on-line version of the book, )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(includes an extensive set of theoretical problems with detailed high-quality )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(solutions, which significantly extend the range and value of the book. The book )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(may be used as a text for a theoretical convex optimization course; the author has )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(taught several variants of such a course at MIT and elsewhere over the last ten )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(years. It may also be used as a supplementary source for nonlinear programming )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(classes, and as a theoretical foundation for classes focused on convex )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(optimization models \(rather than theory\). It is an excellent supplement to several of )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(our books: Convex Optimization Algorithms \(Athena Scientific, 2015\), Nonlinear )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(Programming \(Athena Scientific, 2017\), Network Optimization\(Athena Scientific, )] TJ ET
endstream
endobj
11 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 12 0 R
>>
endobj
12 0 obj
<<
/Length 2395 >>
stream
0.000 0.000 0.000 rg
BT 34.016 371.595 Td /F1 14.2 Tf [(1998\), Introduction to Linear Optimization \(Athena Scientific, 1997\), and Network )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(Flows and Monotropic Optimization \(Athena Scientific, 1998\).)] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(Problem Complexity and Method Efficiency in Optimization)] TJ ET
BT 403.846 336.796 Td /F1 14.2 Tf [( Arkadi? Semenovich )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(Nemirovski? 1983 )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(Convex Analysis and Optimization)] TJ ET
BT 250.217 301.998 Td /F1 14.2 Tf [( Dimitri Bertsekas 2003-03-01 A uniquely )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(pedagogical, insightful, and rigorous treatment of the analytical/geometrical )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(foundations of optimization. The book provides a comprehensive development of )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(convexity theory, and its rich applications in optimization, including duality, )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(minimax/saddle point theory, Lagrange multipliers, and Lagrangian )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(relaxation/nondifferentiable optimization. It is an excellent supplement to several of )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(our books: Convex Optimization Theory \(Athena Scientific, 2009\), Convex )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(Optimization Algorithms \(Athena Scientific, 2015\), Nonlinear Programming \(Athena )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(Scientific, 2016\), Network Optimization \(Athena Scientific, 1998\), and Introduction )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(to Linear Optimization \(Athena Scientific, 1997\). Aside from a thorough account of )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(convex analysis and optimization, the book aims to restructure the theory of the )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(subject, by introducing several novel unifying lines of analysis, including: 1\) A )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(unified development of minimax theory and constrained optimization duality as )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(special cases of duality between two simple geometrical problems. 2\) A unified )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(development of conditions for existence of solutions of convex optimization )] TJ ET
endstream
endobj
13 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 14 0 R
>>
endobj
14 0 obj
<<
/Length 2392 >>
stream
0.000 0.000 0.000 rg
BT 34.016 371.595 Td /F1 14.2 Tf [(problems, conditions for the minimax equality to hold, and conditions for the )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(absence of a duality gap in constrained optimization. 3\) A unification of the major )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(constraint qualifications allowing the use of Lagrange multipliers for nonconvex )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(constrained optimization, using the notion of constraint pseudonormality and an )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(enhanced form of the Fritz John necessary optimality conditions. Among its )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(features the book: a\) Develops rigorously and comprehensively the theory of )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(convex sets and functions, in the classical tradition of Fenchel and Rockafellar b\) )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(Provides a geometric, highly visual treatment of convex and nonconvex )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(optimization problems, including existence of solutions, optimality conditions, )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(Lagrange multipliers, and duality c\) Includes an insightful and comprehensive )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(presentation of minimax theory and zero sum games, and its connection with )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(duality d\) Describes dual optimization, the associated computational methods, )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(including the novel incremental subgradient methods, and applications in linear, )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(quadratic, and integer programming e\) Contains many examples, illustrations, and )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(exercises with complete solutions \(about 200 pages\) posted at the publisher's web )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(site http://www.athenasc.com/convexity.html)] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(First-Order Methods in Optimization)] TJ ET
BT 259.693 93.207 Td /F1 14.2 Tf [( Amir Beck 2017-10-02 The primary goal of )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(this book is to provide a self-contained, comprehensive study of the main ?rst-)] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(order methods that are frequently used in solving large-scale problems. First-order )] TJ ET
endstream
endobj
15 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 16 0 R
>>
endobj
16 0 obj
<<
/Length 2426 >>
stream
0.000 0.000 0.000 rg
BT 34.016 371.595 Td /F1 14.2 Tf [(methods exploit information on values and gradients/subgradients \(but not )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(Hessians\) of the functions composing the model under consideration. With the )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(increase in the number of applications that can be modeled as large or even huge-)] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(scale optimization problems, there has been a revived interest in using simple )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(methods that require low iteration cost as well as low memory storage. The author )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(has gathered, reorganized, and synthesized \(in a unified manner\) many results )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(that are currently scattered throughout the literature, many of which cannot be )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(typically found in optimization books. First-Order Methods in Optimization offers )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(comprehensive study of first-order methods with the theoretical foundations; )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(provides plentiful examples and illustrations; emphasizes rates of convergence )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(and complexity analysis of the main first-order methods used to solve large-scale )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(problems; and covers both variables and functional decomposition methods.)] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(Introduction to Applied Linear Algebra)] TJ ET
BT 272.432 162.804 Td /F1 14.2 Tf [( Stephen Boyd 2018-06-07 A )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(groundbreaking introduction to vectors, matrices, and least squares for )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(engineering applications, offering a wealth of practical examples.)] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(Lectures on Modern Convex Optimization)] TJ ET
BT 296.159 110.606 Td /F1 14.2 Tf [( Aharon Ben-Tal 2001-01-01 Here is a )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(book devoted to well-structured and thus efficiently solvable convex optimization )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(problems, with emphasis on conic quadratic and semidefinite programming. The )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(authors present the basic theory underlying these problems as well as their )] TJ ET
endstream
endobj
17 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 18 0 R
>>
endobj
18 0 obj
<<
/Length 2428 >>
stream
0.000 0.000 0.000 rg
BT 34.016 371.595 Td /F1 14.2 Tf [(numerous applications in engineering, including synthesis of filters, Lyapunov )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(stability analysis, and structural design. The authors also discuss the complexity )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(issues and provide an overview of the basic theory of state-of-the-art polynomial )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(time interior point methods for linear, conic quadratic, and semidefinite )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(programming. The book's focus on well-structured convex problems in conic form )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(allows for unified theoretical and algorithmical treatment of a wide spectrum of )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(important optimization problems arising in applications.)] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(Lectures on Convex Optimization)] TJ ET
BT 243.890 249.800 Td /F1 14.2 Tf [( Yurii Nesterov 2018-11-19 This book provides a )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(comprehensive, modern introduction to convex optimization, a field that is )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(becoming increasingly important in applied mathematics, economics and finance, )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(engineering, and computer science, notably in data science and machine learning. )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(Written by a leading expert in the field, this book includes recent advances in the )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(algorithmic theory of convex optimization, naturally complementing the existing )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(literature. It contains a unified and rigorous presentation of the acceleration )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(techniques for minimization schemes of first- and second-order. It provides readers )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(with a full treatment of the smoothing technique, which has tremendously extended )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(the abilities of gradient-type methods. Several powerful approaches in structural )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(optimization, including optimization in relative scale and polynomial-time interior-)] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(point methods, are also discussed in detail. Researchers in theoretical optimization )] TJ ET
endstream
endobj
19 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 20 0 R
>>
endobj
20 0 obj
<<
/Length 2333 >>
stream
0.000 0.000 0.000 rg
BT 34.016 371.595 Td /F1 14.2 Tf [(as well as professionals working on optimization problems will find this book very )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(useful. It presents many successful examples of how to develop very fast )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(specialized minimization algorithms. Based on the author’s lectures, it can )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(naturally serve as the basis for introductory and advanced courses in convex )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(optimization for students in engineering, economics, computer science and )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(mathematics.)] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(Algorithms for Optimization)] TJ ET
BT 205.058 267.199 Td /F1 14.2 Tf [( Mykel J. Kochenderfer 2019-03-12 A comprehensive )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(introduction to optimization with a focus on practical algorithms for the design of )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(engineering systems. This book offers a comprehensive introduction to )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(optimization with a focus on practical algorithms. The book approaches )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(optimization from an engineering perspective, where the objective is to design a )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(system that optimizes a set of metrics subject to constraints. Readers will learn )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(about computational approaches for a range of challenges, including searching )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(high-dimensional spaces, handling problems where there are multiple competing )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(objectives, and accommodating uncertainty in the metrics. Figures, examples, and )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(exercises convey the intuition behind the mathematical approaches. The text )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(provides concrete implementations in the Julia programming language. Topics )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(covered include derivatives and their generalization to multiple dimensions; local )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(descent and first- and second-order methods that inform local descent; stochastic )] TJ ET
endstream
endobj
21 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 22 0 R
>>
endobj
22 0 obj
<<
/Length 2264 >>
stream
0.000 0.000 0.000 rg
BT 34.016 371.595 Td /F1 14.2 Tf [(methods, which introduce randomness into the optimization process; linear )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(constrained optimization, when both the objective function and the constraints are )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(linear; surrogate models, probabilistic surrogate models, and using probabilistic )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(surrogate models to guide optimization; optimization under uncertainty; uncertainty )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(propagation; expression optimization; and multidisciplinary design optimization. )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(Appendixes offer an introduction to the Julia language, test functions for evaluating )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(algorithm performance, and mathematical concepts used in the derivation and )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(analysis of the optimization methods discussed in the text. The book can be used )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(by advanced undergraduates and graduate students in mathematics, statistics, )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(computer science, any engineering field, \(including electrical engineering and )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(aerospace engineering\), and operations research, and as a reference for )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(professionals.)] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(Convex Optimization)] TJ ET
BT 165.472 162.804 Td /F1 14.2 Tf [( Stephen P. Boyd 2004 )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(Distributed Optimization and Statistical Learning Via the Alternating Direction )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(Method of Multipliers)] TJ ET
BT 165.472 128.005 Td /F1 14.2 Tf [( Stephen Boyd 2011 Surveys the theory and history of the )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(alternating direction method of multipliers, and discusses its applications to a wide )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(variety of statistical and machine learning problems of recent interest, including the )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(lasso, sparse logistic regression, basis pursuit, covariance selection, support )] TJ ET
endstream
endobj
23 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 24 0 R
>>
endobj
24 0 obj
<<
/Length 2379 >>
stream
0.000 0.000 0.000 rg
BT 34.016 371.595 Td /F1 14.2 Tf [(vector machines, and many others.)] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(Numerical Optimization)] TJ ET
BT 181.289 354.196 Td /F1 14.2 Tf [( Jorge Nocedal 2006-06-06 The new edition of this book )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(presents a comprehensive and up-to-date description of the most effective )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(methods in continuous optimization. It responds to the growing interest in )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(optimization in engineering, science, and business by focusing on methods best )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(suited to practical problems. This edition has been thoroughly updated throughout. )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(There are new chapters on nonlinear interior methods and derivative-free methods )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(for optimization, both of which are widely used in practice and are the focus of )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(much current research. Because of the emphasis on practical methods, as well as )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(the extensive illustrations and exercises, the book is accessible to a wide audience.)] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(Convex Optimization of Power Systems)] TJ ET
BT 284.260 197.602 Td /F1 14.2 Tf [( Joshua Adam Taylor 2015-02-12 A )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(mathematically rigorous guide to convex optimization for power systems )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(engineering.)] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(Convex Analysis and Nonlinear Optimization)] TJ ET
BT 315.154 145.405 Td /F1 14.2 Tf [( Jonathan Borwein 2010-05-05 )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(Optimization is a rich and thriving mathematical discipline, and the underlying )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(theory of current computational optimization techniques grows ever more )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(sophisticated. This book aims to provide a concise, accessible account of convex )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(analysis and its applications and extensions, for a broad audience. Each section )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(concludes with an often extensive set of optional exercises. This new edition adds )] TJ ET
endstream
endobj
25 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 26 0 R
>>
endobj
26 0 obj
<<
/Length 2548 >>
stream
0.000 0.000 0.000 rg
BT 34.016 371.595 Td /F1 14.2 Tf [(material on semismooth optimization, as well as several new proofs.)] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(Primal-dual Interior-Point Methods)] TJ ET
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
34.016 351.844 m 250.202 351.844 l S
BT 250.202 354.196 Td /F1 14.2 Tf [( Stephen J. Wright 1997-01-01 In the past )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(decade, primal-dual algorithms have emerged as the most important and useful )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(algorithms from the interior-point class. This book presents the major primal-dual )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(algorithms for linear programming in straightforward terms. A thorough description )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(of the theoretical properties of these methods is given, as are a discussion of )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(practical and computational aspects and a summary of current software. This is an )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(excellent, timely, and well-written work. The major primal-dual algorithms covered )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(in this book are path-following algorithms \(short- and long-step, predictor-)] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(corrector\), potential-reduction algorithms, and infeasible-interior-point algorithms. A )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(unified treatment of superlinear convergence, finite termination, and detection of )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(infeasible problems is presented. Issues relevant to practical implementation are )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(also discussed, including sparse linear algebra and a complete specification of )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(Mehrotra's predictor-corrector algorithm. Also treated are extensions of primal-dual )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(algorithms to more general problems such as monotone complementarity, )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(semidefinite programming, and general convex programming problems.)] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(Linear Matrix Inequalities in System and Control Theory)] TJ ET
BT 384.851 93.207 Td /F1 14.2 Tf [( Stephen Boyd 1994-01-01 )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(In this book the authors reduce a wide variety of problems arising in system and )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(control theory to a handful of convex and quasiconvex optimization problems that )] TJ ET
endstream
endobj
27 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 28 0 R
>>
endobj
28 0 obj
<<
/Length 2549 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(involve linear matrix inequalities. These optimization problems can be solved using )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(recently developed numerical algorithms that not only are polynomial-time but also )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(work very well in practice; the reduction therefore can be considered a solution to )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(the original problems. This book opens up an important new research area in )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(which convex optimization is combined with system and control theory, resulting in )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(the solution of a large number of previously unsolved problems.)] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(Selected Applications of Convex Optimization)] TJ ET
BT 321.509 267.199 Td /F1 14.2 Tf [( Li Li 2015-03-26 This book focuses )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(on the applications of convex optimization and highlights several topics, including )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(support vector machines, parameter estimation, norm approximation and )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(regularization, semi-definite programming problems, convex relaxation, and )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(geometric problems. All derivation processes are presented in detail to aid in )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(comprehension. The book offers concrete guidance, helping readers recognize )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(and formulate convex optimization problems they might encounter in practice.)] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(Optimization in Practice with MATLAB)] TJ ET
0.285 w 0 J [ ] 0 d
34.016 143.053 m 273.957 143.053 l S
BT 273.957 145.405 Td /F1 14.2 Tf [( Achille Messac 2015-03-19 This textbook is )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(designed for students and industry practitioners for a first course in optimization )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(integrating MATLAB® software.)] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(Recent Advances in Learning and Control)] TJ ET
BT 297.769 93.207 Td /F1 14.2 Tf [( Vincent D. Blondel 2008-01-11 This )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(volume is composed of invited papers on learning and control. The contents form )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(the proceedings of a workshop held in January 2008, in Hyderabad that honored )] TJ ET
endstream
endobj
29 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 30 0 R
>>
endobj
30 0 obj
<<
/Length 2490 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(the 60th birthday of Doctor Mathukumalli Vidyasagar. The 14 papers, written by )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(international specialists in the field, cover a variety of interests within the broader )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(field of learning and control. The diversity of the research provides a )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(comprehensive overview of a field of great interest to control and system theorists.)] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(Convex Optimization)] TJ ET
BT 165.472 301.998 Td /F1 14.2 Tf [( Stephen Boyd 2004-03-25 Convex optimization problems )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(arise frequently in many different fields. This book provides a comprehensive )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(introduction to the subject, and shows in detail how such problems can be solved )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(numerically with great efficiency. The book begins with the basic elements of )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(convex sets and functions, and then describes various classes of convex )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(optimization problems. Duality and approximation techniques are then covered, as )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(are statistical estimation techniques. Various geometrical problems are then )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(presented, and there is detailed discussion of unconstrained and constrained )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(minimization problems, and interior-point methods. The focus of the book is on )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(recognizing convex optimization problems and then finding the most appropriate )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(technique for solving them. It contains many worked examples and homework )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(exercises and will appeal to students, researchers and practitioners in fields such )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(as engineering, computer science, mathematics, statistics, finance and economics.)] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(Global Optimization)] TJ ET
BT 158.347 75.808 Td /F1 14.2 Tf [( Leo Liberti 2006-02-21 Most global optimization literature )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(focuses on theory. This book, however, contains descriptions of new )] TJ ET
endstream
endobj
31 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 32 0 R
>>
endobj
32 0 obj
<<
/Length 2420 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(implementations of general-purpose or problem-specific global optimization )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(algorithms. It discusses existing software packages from which the entire )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(community can learn. The contributors are experts in the discipline of actually )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(getting global optimization to work, and the book provides a source of ideas for )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(people needing to implement global optimization software.)] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(Semidefinite Optimization and Convex Algebraic Geometry)] TJ ET
BT 405.442 284.599 Td /F1 14.2 Tf [( Grigoriy Blekherman )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(2013-03-21 An accessible introduction to convex algebraic geometry and )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(semidefinite optimization. For graduate students and researchers in mathematics )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(and computer science.)] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(Convex Optimization)] TJ ET
BT 165.472 215.002 Td /F1 14.2 Tf [( Sébastien Bubeck 2015-11-12 This monograph presents the )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(main complexity theorems in convex optimization and their corresponding )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(algorithms. It begins with the fundamental theory of black-box optimization and )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(proceeds to guide the reader through recent advances in structural optimization )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(and stochastic optimization. The presentation of black-box optimization, strongly )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(influenced by the seminal book by Nesterov, includes the analysis of cutting plane )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(methods, as well as \(accelerated\) gradient descent schemes. Special attention is )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(also given to non-Euclidean settings \(relevant algorithms include Frank-Wolfe, )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(mirror descent, and dual averaging\), and discussing their relevance in machine )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(learning. The text provides a gentle introduction to structural optimization with )] TJ ET
endstream
endobj
33 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 34 0 R
>>
endobj
34 0 obj
<<
/Length 2433 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(FISTA \(to optimize a sum of a smooth and a simple non-smooth term\), saddle-)] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(point mirror prox \(Nemirovski's alternative to Nesterov's smoothing\), and a concise )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(description of interior point methods. In stochastic optimization it discusses )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(stochastic gradient descent, mini-batches, random coordinate descent, and )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(sublinear algorithms. It also briefly touches upon convex relaxation of )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(combinatorial problems and the use of randomness to round solutions, as well as )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(random walks based methods.)] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(Integer Programming)] TJ ET
0.285 w 0 J [ ] 0 d
34.016 247.449 m 167.852 247.449 l S
BT 167.852 249.800 Td /F1 14.2 Tf [( Laurence A. Wolsey 2020-10-20 A PRACTICAL GUIDE TO )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(OPTIMIZATION PROBLEMS WITH DISCRETE OR INTEGER VARIABLES, )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(REVISED AND UPDATED The revised second edition of Integer Programming )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(explains in clear and simple terms how to construct custom-made algorithms or )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(use existing commercial software to obtain optimal or near-optimal solutions for a )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(variety of real-world problems. The second edition also includes information on the )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(remarkable progress in the development of mixed integer programming solvers in )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(the 22 years since the first edition of the book appeared. The updated text includes )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(information on the most recent developments in the field such as the much )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(improved preprocessing/presolving and the many new ideas for primal heuristics )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(included in the solvers. The result has been a speed-up of several orders of )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(magnitude. The other major change reflected in the text is the widespread use of )] TJ ET
endstream
endobj
35 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 36 0 R
>>
endobj
36 0 obj
<<
/Length 2479 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(decomposition algorithms, in particular column generation \(branch-\(cut\)-and-price\) )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(and Benders’ decomposition. The revised second edition: Contains new )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(developments on column generation Offers a new chapter on Benders’ algorithm )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(Includes expanded information on preprocessing, heuristics, and branch-and-cut )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(Presents several basic and extended formulations, for example for fixed cost )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(network flows Also touches on and briefly introduces topics such as non-bipartite )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(matching, the complexity of extended formulations or a good linear program for the )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(implementation of lift-and-project Written for students of integer/mathematical )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(programming in operations research, mathematics, engineering, or computer )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(science, Integer Programming offers an updated edition of the basic text that )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(reflects the most recent developments in the field.)] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(Linear Controller Design)] TJ ET
BT 187.645 180.203 Td /F1 14.2 Tf [( Stephen P. Boyd 1991 )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(Optimization Theory for Large Systems)] TJ ET
BT 281.096 162.804 Td /F1 14.2 Tf [( Leon S. Lasdon 2013-01-17 Important text )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(examines most significant algorithms for optimizing large systems and clarifying )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(relations between optimization procedures. Initial chapter on linear and nonlinear )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(programming provide the foundation for the rest of the book. Appendixes.)] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(Earth's Legacy)] TJ ET
BT 127.823 93.207 Td /F1 14.2 Tf [( Stephen Boyd 2021-03-24 A new habitable world was discovered )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(in the universe. The people of Earth could get there but only as a space )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(colony.The real question was who would go?The atheists of Earth agreed to go to )] TJ ET
endstream
endobj
37 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 38 0 R
>>
endobj
38 0 obj
<<
/Length 2461 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(explore the new planet but only if they could form a new society, a society free )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(from all religious beliefs. The untold secret goal of the group was even darker than )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(they told their everyone, darker than anyone could have imagined.As soon as the )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(space colony ship went past the point of no return, the leaders announced a )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(harsher system that was originally proposed. Anyone caught worshipping anything )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(was immediately put to death.Unbeknown to the leaders of the original colony, a )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(small group of Believers imbedded themselves into the colony. They were visited )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(by an angel of God who told them to go. They were told that they didn't have to go )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(but if they didn't billions of souls would be lost for all of eternity.Some of the group )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(that were visited by the angel went on the mission, some did not.The new planet )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(was named PIA, an abbreviation for Planet of Intergalactic Atheists. The story )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(picks up several generations after the original colonists landed. The grandson of )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(the founder of the athestist group was in charge of the planet just as his father had )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(been and his father's father before him. Stephen Steele was the grandson of one )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(the original Believers that had imbedded themselves onto the space colony. The )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(Believers spread out across the planet when they first arrived so as to keep safe. )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(They formed small cell groups as well. That way if one group got caught the others )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(would stay safe.His Grandparents migrated to the mountains. They lived in a small )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(isolated village they named named Ash, Ash was very far away from the Darwin, )] TJ ET
endstream
endobj
39 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 40 0 R
>>
endobj
40 0 obj
<<
/Length 2415 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(the capital of the planet.Kristin Knope was born to be a prostitute just as her )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(mother and her mother's mother was. Kristin never met her father as even her )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(mother didn't know who he was. Even if she did know it would not have mattered )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(much. Most children on PIA were raised in state run orphanages. If either of their )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(parents stayed around it was usually the mother. Child were considered throw )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(aways, especially children born into the lower class and Kristin was the lowest of )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(the lowest class. She was born into the Hopeless class.On Pia the manual )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(laborers were necessary at first, not so much later on. The laborers soon became )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(useless. As more and more machinery arrived from Earth they were tossed aside. )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(Soon an entire class of people moved into the gutters of Pia. They became the )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(Hopeless class.Stephen Steele's parents and grandparents were part of the )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(original team of Believers that developed their computer cyber systems. They were )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(necessary to run the planet. They were considered valuable and given much )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(freedom in the early days of Pia. They also helped create the Pianet, their world )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(wide web. Fresh out of law school, Stephen Steele came across Kristin Knope on )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(the streets of downtown Darwin. He had barely heard of a prostitution when he met )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(Kristin due to his isolated upbringing.When he recognized what she was he )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(determine to help her. He sent her to a charity that helped prostitutes reeducate )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(themselves.It was years before they met again. By that time Kristin had a degree )] TJ ET
endstream
endobj
41 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 42 0 R
>>
endobj
42 0 obj
<<
/Length 2485 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(as a teacher and was teaching Kindergartners at Simpka elementary.Due to )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(unfortunate circumstances Kristin lost her job and had to get work where ever she )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(could. She also had a degree in paralegal studies and got a job in Stephen )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(Steele's law office. They didn't recognise each other at first from their long ago )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(chance encounter but they soon remembered each other.Stephen's real mission in )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(life was was the same as his parents and grandparents, to tell people about God )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(and let them decide for themselves whether or not to follow him.The Supreme )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(Chancellor of Pia says that he is against all religions but in truth he is not. In fact )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(he is being controlled by one. A mysterious being that suddenly appears.)] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(Linear Programming)] TJ ET
0.285 w 0 J [ ] 0 d
34.016 212.650 m 163.092 212.650 l S
BT 163.092 215.002 Td /F1 14.2 Tf [( Robert J Vanderbei 2013-07-16 This Fourth Edition )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(introduces the latest theory and applications in optimization. It emphasizes )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(constrained optimization, beginning with a substantial treatment of linear )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(programming and then proceeding to convex analysis, network flows, integer )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(programming, quadratic programming, and convex optimization. Readers will )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(discover a host of practical business applications as well as non-business )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(applications. Topics are clearly developed with many numerical examples worked )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(out in detail. Specific examples and concrete algorithms precede more abstract )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(topics. With its focus on solving practical problems, the book features free C )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(programs to implement the major algorithms covered, including the two-phase )] TJ ET
endstream
endobj
43 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 44 0 R
>>
endobj
44 0 obj
<<
/Length 2452 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(simplex method, primal-dual simplex method, path-following interior-point method, )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(and homogeneous self-dual methods. In addition, the author provides online JAVA )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(applets that illustrate various pivot rules and variants of the simplex method, both )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(for linear programming and for network flows. These C programs and JAVA tools )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(can be found on the book's website. The website also includes new online )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(instructional tools and exercises.)] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(Convex Optimization)] TJ ET
BT 165.472 267.199 Td /F1 14.2 Tf [( Stephen Boyd 2004-03-08 A comprehensive introduction to )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(the tools, techniques and applications of convex optimization.)] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(Introduction to Nonlinear Optimization)] TJ ET
BT 272.404 232.401 Td /F1 14.2 Tf [( Amir Beck 2014-10-27 This book provides )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(the foundations of the theory of nonlinear optimization as well as some related )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(algorithms and presents a variety of applications from diverse areas of applied )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(sciences. The author combines three pillars of optimization?theoretical and )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(algorithmic foundation, familiarity with various applications, and the ability to apply )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(the theory and algorithms on actual problems?and rigorously and gradually builds )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(the connection between theory, algorithms, applications, and implementation. )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(Readers will find more than 170 theoretical, algorithmic, and numerical exercises )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(that deepen and enhance the reader's understanding of the topics. The author )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(includes offers several subjects not typically found in optimization books?for )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(example, optimality conditions in sparsity-constrained optimization, hidden )] TJ ET
endstream
endobj
45 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 46 0 R
>>
endobj
46 0 obj
<<
/Length 2513 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(convexity, and total least squares. The book also offers a large number of )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(applications discussed theoretically and algorithmically, such as circle fitting, )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(Chebyshev center, the Fermat?Weber problem, denoising, clustering, total least )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(squares, and orthogonal regression and theoretical and algorithmic topics )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(demonstrated by the MATLAB? toolbox CVX and a package of m-files that is )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(posted on the book?s web site.)] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(Convex Optimization China Edition)] TJ ET
BT 254.178 267.199 Td /F1 14.2 Tf [( Stephen Boyd 2013-12-12 )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(Convex Analysis)] TJ ET
0.285 w 0 J [ ] 0 d
34.016 247.449 m 139.337 247.449 l S
BT 139.337 249.800 Td /F1 14.2 Tf [( Ralph Tyrell Rockafellar 2015-04-29 Available for the first time in )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(paperback, R. Tyrrell Rockafellar's classic study presents readers with a coherent )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(branch of nonlinear mathematical analysis that is especially suited to the study of )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(optimization problems. Rockafellar's theory differs from classical analysis in that )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(differentiability assumptions are replaced by convexity assumptions. The topics )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(treated in this volume include: systems of inequalities, the minimum or maximum )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(of a convex function over a convex set, Lagrange multipliers, minimax theorems )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(and duality, as well as basic results about the structure of convex sets and the )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(continuity and differentiability of convex functions and saddle- functions. This book )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(has firmly established a new and vital area not only for pure mathematics but also )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(for applications to economics and engineering. A sound knowledge of linear )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(algebra and introductory real analysis should provide readers with sufficient )] TJ ET
endstream
endobj
47 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 48 0 R
>>
endobj
48 0 obj
<<
/Length 2466 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(background for this book. There is also a guide for the reader who may be using )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(the book as an introduction, indicating which parts are essential and which may be )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(skipped on a first reading.)] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(A Course in Convexity)] TJ ET
BT 174.977 319.397 Td /F1 14.2 Tf [( Alexander Barvinok 2002-11-19 Convexity is a simple idea )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(that manifests itself in a surprising variety of places. This fertile field has an )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(immensely rich structure and numerous applications. Barvinok demonstrates that )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(simplicity, intuitive appeal, and the universality of applications make teaching \(and )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(learning\) convexity a gratifying experience. The book will benefit both teacher and )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(student: It is easy to understand, entertaining to the reader, and includes many )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(exercises that vary in degree of difficulty. Overall, the author demonstrates the )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(power of a few simple unifying principles in a variety of pure and applied problems. )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(The prerequisites are minimal amounts of linear algebra, analysis, and elementary )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(topology, plus basic computational skills. Portions of the book could be used by )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(advanced undergraduates. As a whole, it is designed for graduate students )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(interested in mathematical methods, computer science, electrical engineering, and )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(operations research. The book will also be of interest to research mathematicians, )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(who will find some results that are recent, some that are new, and many known )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(results that are discussed from a new perspective.)] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(Statistical Inference Via Convex Optimization)] TJ ET
BT 318.332 58.408 Td /F1 14.2 Tf [( Anatoli Juditsky 2020-04-07 This )] TJ ET
endstream
endobj
49 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 50 0 R
>>
endobj
50 0 obj
<<
/Length 2433 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(authoritative book draws on the latest research to explore the interplay of high-)] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(dimensional statistics with optimization. Through an accessible analysis of )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(and Arkadi Nemirovski show how convex optimization theory can be used to )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(devise and analyze near-optimal statistical inferences. Statistical Inference via )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(Convex Optimization is an essential resource for optimization specialists who are )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(new to statistics and its applications, and for data scientists who want to improve )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(their optimization methods. Juditsky and Nemirovski provide the first systematic )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(treatment of the statistical techniques that have arisen from advances in the theory )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(of optimization. They focus on four well-known statistical problems—sparse )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(recovery, hypothesis testing, and recovery from indirect observations of both )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(signals and functions of signals—demonstrating how they can be solved more )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(efficiently as convex optimization problems. The emphasis throughout is on )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(achieving the best possible statistical performance. The construction of inference )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(routines and the quantification of their statistical performance are given by efficient )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(computation rather than by analytical derivation typical of more conventional )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(statistical approaches. In addition to being computation-friendly, the methods )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(described in this book enable practitioners to handle numerous situations too )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(difficult for closed analytical form analysis, such as composite hypothesis testing )] TJ ET
endstream
endobj
51 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 52 0 R
>>
endobj
52 0 obj
<<
/Length 2541 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(and signal recovery in inverse problems. Statistical Inference via Convex )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(Optimization features exercises with solutions along with extensive appendixes, )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(making it ideal for use as a graduate text.)] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(Introductory Lectures on Convex Optimization)] TJ ET
BT 323.091 319.397 Td /F1 14.2 Tf [( I?U?. E. Nesterov 2004 )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(Convex Optimization South Asia Edition)] TJ ET
0.285 w 0 J [ ] 0 d
34.016 299.647 m 285.870 299.647 l S
BT 285.870 301.998 Td /F1 14.2 Tf [( Stephen Boyd 2016-12-01 )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(Convex Optimization in Signal Processing and Communications)] TJ ET
BT 436.322 284.599 Td /F1 14.2 Tf [( Daniel P. Palomar )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(2010 Leading experts provide the theoretical underpinnings of the subject plus )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(tutorials on a wide range of applications, from automatic code generation to robust )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(broadband beamforming. Emphasis on cutting-edge research and formulating )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(problems in convex form make this an ideal textbook for advanced graduate )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(courses and a useful self-study guide.)] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(Optimization for Machine Learning)] TJ ET
BT 251.015 180.203 Td /F1 14.2 Tf [( Suvrit Sra 2012 An up-to-date account of the )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(interplay between optimization and machine learning, accessible to students and )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(researchers in both communities. The interplay between optimization and machine )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(learning is one of the most important developments in modern computational )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(science. Optimization formulations and methods are proving to be vital in )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(designing algorithms to extract essential knowledge from huge volumes of data. )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(Machine learning, however, is not simply a consumer of optimization technology )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(but a rapidly evolving field that is itself generating new optimization ideas. This )] TJ ET
endstream
endobj
53 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 54 0 R
>>
endobj
54 0 obj
<<
/Length 2506 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(book captures the state of the art of the interaction between optimization and )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(machine learning in a way that is accessible to researchers in both fields. )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(Optimization approaches have enjoyed prominence in machine learning because )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(of their wide applicability and attractive theoretical properties. The increasing )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(complexity, size, and variety of today's machine learning models call for the )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(reassessment of existing assumptions. This book starts the process of )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(reassessment. It describes the resurgence in novel contexts of established )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(frameworks such as first-order methods, stochastic approximations, convex )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(relaxations, interior-point methods, and proximal methods. It also devotes attention )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(to newer themes such as regularized optimization, robust optimization, gradient )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(and subgradient methods, splitting techniques, and second-order methods. Many )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(of these techniques draw inspiration from other fields, including operations )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(research, theoretical computer science, and subfields of optimization. The book )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(will enrich the ongoing cross-fertilization between the machine learning community )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(and these other fields, and within the broader optimization community.)] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(Optimization Models)] TJ ET
BT 163.092 110.606 Td /F1 14.2 Tf [( Giuseppe C. Calafiore 2014-10-31 This accessible textbook )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(demonstrates how to recognize, simplify, model and solve optimization problems - )] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(and apply these principles to new projects.)] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(Proximal Algorithms)] TJ ET
0.285 w 0 J [ ] 0 d
34.016 56.057 m 160.698 56.057 l S
BT 160.698 58.408 Td /F1 14.2 Tf [( Neal Parikh 2013-11 Proximal Algorithms discusses proximal )] TJ ET
endstream
endobj
55 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Contents 56 0 R
>>
endobj
56 0 obj
<<
/Length 2459 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(operators and proximal algorithms, and illustrates their applicability to standard )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(and distributed convex optimization in general and many applications of recent )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(interest in particular. Much like Newton's method is a standard tool for solving )] TJ ET
BT 34.016 319.397 Td /F1 14.2 Tf [(unconstrained smooth optimization problems of modest size, proximal algorithms )] TJ ET
BT 34.016 301.998 Td /F1 14.2 Tf [(can be viewed as an analogous tool for nonsmooth, constrained, large-scale, or )] TJ ET
BT 34.016 284.599 Td /F1 14.2 Tf [(distributed versions of these problems. They are very generally applicable, but are )] TJ ET
BT 34.016 267.199 Td /F1 14.2 Tf [(especially well-suited to problems of substantial recent interest involving large or )] TJ ET
BT 34.016 249.800 Td /F1 14.2 Tf [(high-dimensional datasets. Proximal methods sit at a higher level of abstraction )] TJ ET
BT 34.016 232.401 Td /F1 14.2 Tf [(than classical algorithms like Newton's method: the base operation is evaluating )] TJ ET
BT 34.016 215.002 Td /F1 14.2 Tf [(the proximal operator of a function, which itself involves solving a small convex )] TJ ET
BT 34.016 197.602 Td /F1 14.2 Tf [(optimization problem. These subproblems, which generalize the problem of )] TJ ET
BT 34.016 180.203 Td /F1 14.2 Tf [(projecting a point onto a convex set, often admit closed-form solutions or can be )] TJ ET
BT 34.016 162.804 Td /F1 14.2 Tf [(solved very quickly with standard or simple specialized methods. Proximal )] TJ ET
BT 34.016 145.405 Td /F1 14.2 Tf [(Algorithms discusses different interpretations of proximal operators and algorithms, )] TJ ET
BT 34.016 128.005 Td /F1 14.2 Tf [(looks at their connections to many other topics in optimization and applied )] TJ ET
BT 34.016 110.606 Td /F1 14.2 Tf [(mathematics, surveys some popular algorithms, and provides a large number of )] TJ ET
BT 34.016 93.207 Td /F1 14.2 Tf [(examples of proximal operators that commonly arise in practice.)] TJ ET
BT 34.016 75.808 Td /F1 14.2 Tf [(Introduction to Online Convex Optimization)] TJ ET
BT 304.880 75.808 Td /F1 14.2 Tf [( Elad Hazan 2016-08-10 This book )] TJ ET
BT 34.016 58.408 Td /F1 14.2 Tf [(serves as a reference for a self-contained course on online convex optimization )] TJ ET
endstream
endobj
57 0 obj
<< /Type /Page
/MediaBox [0.000 0.000 595.280 419.530]
/Parent 3 0 R
/Annots [ 59 0 R ]
/Contents 58 0 R
>>
endobj
58 0 obj
<<
/Length 675 >>
stream
0.000 0.000 0.000 rg
0.000 0.000 0.000 RG
0.285 w 0 J [ ] 0 d
BT 34.016 371.595 Td /F1 14.2 Tf [(and the convex optimization approach to machine learning for the educated )] TJ ET
BT 34.016 354.196 Td /F1 14.2 Tf [(graduate student in computer science/electrical engineering/ operations )] TJ ET
BT 34.016 336.796 Td /F1 14.2 Tf [(research/statistics and related fields. An ideal reference.)] TJ ET
BT 36.266 291.902 Td /F1 8.0 Tf [(convex-optimization-stephen-boyd)] TJ ET
BT 323.350 292.109 Td /F1 8.0 Tf [(Downloaded from )] TJ ET
BT 388.262 291.902 Td /F1 8.0 Tf [(jason.wells.me)] TJ ET
BT 440.718 292.109 Td /F1 8.0 Tf [( on September 26, 2022 by guest)] TJ ET
endstream
endobj
59 0 obj
<< /Type /Annot
/Subtype /Link
/A 60 0 R
/Border [0 0 0]
/H /I
/Rect [ 388.2623 291.1621 440.7183 299.3021 ]
>>
endobj
60 0 obj
<< /Type /Action
/S /URI
/URI (https://jason.wells.me)
>>
endobj
xref
0 61
0000000000 65535 f
0000000009 00000 n
0000000074 00000 n
0000000120 00000 n
0000000449 00000 n
0000000478 00000 n
0000000830 00000 n
0000000933 00000 n
0000002448 00000 n
0000002555 00000 n
0000002659 00000 n
0000005147 00000 n
0000005252 00000 n
0000007700 00000 n
0000007805 00000 n
0000010250 00000 n
0000010355 00000 n
0000012834 00000 n
0000012939 00000 n
0000015420 00000 n
0000015525 00000 n
0000017911 00000 n
0000018016 00000 n
0000020333 00000 n
0000020438 00000 n
0000022870 00000 n
0000022975 00000 n
0000025576 00000 n
0000025681 00000 n
0000028283 00000 n
0000028388 00000 n
0000030931 00000 n
0000031036 00000 n
0000033509 00000 n
0000033614 00000 n
0000036100 00000 n
0000036205 00000 n
0000038737 00000 n
0000038842 00000 n
0000041356 00000 n
0000041461 00000 n
0000043929 00000 n
0000044034 00000 n
0000046572 00000 n
0000046677 00000 n
0000049182 00000 n
0000049287 00000 n
0000051853 00000 n
0000051958 00000 n
0000054477 00000 n
0000054582 00000 n
0000057068 00000 n
0000057173 00000 n
0000059767 00000 n
0000059872 00000 n
0000062431 00000 n
0000062536 00000 n
0000065048 00000 n
0000065172 00000 n
0000065899 00000 n
0000066027 00000 n
trailer
<<
/Size 61
/Root 1 0 R
/Info 5 0 R
>>
startxref
66101
%%EOF