Index. affine dependency, 133 minimal, 133 affine hull, 392 affinely independent, 393 α-bb, 230, 258, 297 approximate solutions, 9 approximation, 86
|
|
- Ezra Townsend
- 5 years ago
- Views:
Transcription
1 Index affine dependency, 133 minimal, 133 affine hull, 392 affinely independent, 393 α-bb, 230, 258, 297 approximate solutions, 9 approximation, 86 AP X, 10 aspiration level, 86, 110 atomic clusters, 372 augmented Lagrangian, 266 backtracking, 49 BARON, 335 basin hopping, 45, 56, 75, 81, 376, 388 monotonic, 75 Bayesian approach, 117 best start, 51 best-first, 292 bias, 102 billiard simulation, 384 binary clusters, 378 bisection rule, 298 Boltzmann distribution, 75 Boolean quadric polytopes, 205 bound factors, 163 bound improving selection, 293 branch and bound (BB), 289 algorithm, 290, 382 tree, 291 branching, 295 breadth-first, 292 bumpiness, 87, 93 Carathéodory s theorem, 11, 392 CEC test problems, 369 centered forms, 257 circuit intersection point, 134 clique, 23 clique inequalities, 205 clustering methods, 52 clusters, 372 combination rule child-parent, 49 child-population, 49 child-population Gauss Seidel, 49 completely monotonic function, 94 completely positive matrices, 395 concave envelope, 127 concave function, 395 concave minimization, 259, 309, 350 concentrated log likelihood, 105 cone, 309 completely positive, 26 copositive, 26 polyhedral, 309 conformational space annealing, 62, 376 conic programming, 26 conjugate function, 128 constraint filtering scheme, 170 constraint propagation, 348 constraint qualification, 260 continuation methods, 83 convex envelope, 79, 127 convex extension property, 281 convex function, 395 convex hull, 391 convex outer approximation, 126, 275 convex relaxation, 125 convex set, 391 convex underestimator, 126 cooling schedule, 75 copositive bound, 248 copositive cone, 26, 302 copositive matrices, 395 critical value,
2 434 Index cut-type inequalities, 205 cutting planes, 335 decision problem, 9 decomposition difference-of-convex (DC), 240 deep space maneuver, 386 δ-feasible, 289 δ-optimal solution, 290 depth-first, 292 deterministic sequential algorithm, 121 difference of convex (DC), 240 canonical form, 242 decomposition, 240, 321 spectral, 246 function, 240 optimization problem, 242, 315 undominated decomposition, 243, 244 difference of monotonic (DM) function, 251 problem, 324 differential evolution (DE), 66, 388 DIRECT, 266 direct, 69 direct move, 59 direct mutation, 377 dissimilarity, 61 criterion, 376 measure, 61 distance geometry, 84, 378 domain reduction, 153, 335 feasibility based, 335 optimality based, 335 doubly nonnegative matrices, 395 dual bounds, 259 dual cone, 394 dynamic lattice, 377 eccentricity, 305 edge-concave, 132 ellipsoid, 321 epigraph, 273, 396 ε-approximate solution, 10 ε-optimal solution, 290 (ε,δ)-optimal solution, 290 essential ε-optimal solution, 334 essential feasible set, 334 exact penalty, 266 exactness in the limit, 294 exhaustiveness property, 296 expected improvement, 117 exponential-type augmented Lagrangian, 266 extreme face, 350 extreme point, 392 facet representation, 158 facial cut, 351 factorable function, 267 fathoming rules, 358 feasibility based domain reduction, 335 feasible region, 3 filled function, 77 filter method, 388 force fields, 372 Frobenius inner product, 26, 394 fully polynomial time approximation scheme (FPTAS), 10 funnel, 47, 75, 370 bottom, 47 hopping, 377 γ -concavity cut, 314, 350 γ -extension, 313, 352 γ -valid cut, 352 generalized critical value, 225 generalized trust region problem, 14 generating set, 129 genetic algorithms, 66 geometric branching, 295 Gerschgorin theorem, 231 Gibbs distribution, 75 global information, 121 global minima, 3 global phase, 41 GLOBALlib, 370 GloballyGenerate, 44 gradient ideal, 220 greedy randomized adaptive search (GRASP), 68 hidden convexity, 12 hit-and-run, 79 homotopy methods, 83
3 Index 435 ideal, 219 improvement, 41 strict, 41 inclusion function, 256, 358 increasing function, 250 interpolation, 86 multivariate, 88 polynomial, 89 radial function, 90 intersection cut, 314 interval analysis, 358 interval arithmetic, 231, 255, 297 isolated singularities at infinity, 226 isotone, 256 isotonic property, 326 iterated local search, 45, 56, 57 Karush Kuhn Tucker (KKT), 23 conditions, 23, 325 point, 23 Kriging, 101, 117 Kuhn s triangulation, 137, 148 Lagrange function, 96 Lagrangian dual, 260 function, 259 Laplacian matrix, 29 LeGO, 85 Lennard-Jones, 372 linear (sum-of-ratios) fractional programming, 18 linear boundary value form, 258 linear complementarity, 286 linear constraint factors, 165 linear fractional multiplicative programming problem, 17 linear multiplicative programming, 18 linearity domains, 158 linearization phase, 163 Lipschitz constant, 252 Lipschitz function, 252 local information, 120 local Lipschitz, 255 local optimum, 3, 45 at level 0, 46 at level 1, 46 local phase, 41 local smoothing, 81 localizes, 122 LocallyGenerate, 44 Lovász extension, 138 Markov stochastic processes, 75 max bisection, 39 max cut, 29 maximum clique problem, 23, 26 mean value form function, 257 memetic algorithms, 48 merit function, 86, 87 model of computation bit model, 8 black box, 9 molecular conformation, 84, 371 moment, 227 monotonic basin hopping (MBH), 57, 75 monotonic optimization, 250 monotonicity test, 358 Morse potential, 72, 372 multilevel single linkage, 53 multilinear functions, 161, 286 Multistart, 51, 52 multivariate interpolation, 88 n-simplex, 133 natural interval extension, 257 negative definite, 393 negative semidefinite, 393 nested sequence, 296 nonlinearities removal range reduction, 340 nonnegative matrices, 394 nonnegative polynomials, 218 normal conical algorithms, 319 normal set, 251 objective function, 3 ω-subdivision rule, 299 one row relaxation, 188 optimality based domain reduction, 335 order relation, 41 pair potential, 372 parallelotope, 37 Pareto, 70 particle swarm optimization (PSO), 63, 66
4 436 Index Peano curve, 254 Piyavskii Shubert algorithm, 253 planet swing-by, 386 polyblocks, 252 polyhedral convex envelope, 130 polyhedral subdivision, 155 polyhedron, 391 polynomial constraint factors, 166 polynomial programming, 217 polynomial time approximation scheme (PTAS), 10 polytope, 391 pooling problems, 370 population basin hopping (PBH), 59, 68 dissimilarity based, 61 embarassing parallelism, 60 greedy, 60 population-based methods, 42 positive definite, 393 positive semidefinite, 393 positively homogeneous function, 276 posynomial functions, 286 potential, 372 principal gradient tentacle, 225 projective error, 272 pure adaptive search (PAS), 78 pure random search (PRS), 50, 74, 78 quadratic programming (QP), 8, 16, 172 standard, 23 quadratically constrained quadratic programming (QCQP), 211 quasi-concave function, 11, 359, 397 quasi-convex function, 397 quotient ring, 219 radial basis center, 90 cubic, 91 function, 91 Gaussian, 91 linear, 91 multiquadric, 91 thin plate spline, 91 radial function, 90 radical, 219 radical ideal, 219 randomized algorithms, 9 range reduction, 335 nonlinearities removal, 340 standard, 339 ray, 309 real variety, 219 recession cone, 277, 393 reformulation phase, 163 reformulation-linearizationtechnique (RLT), 163 regression, 86, 106 regularization parameter, 108 relaxation, 125 restricted candidate list, 68 reverse convex constraint, 242, 314 reverse normal set, 251 reverse polyblock, 252, 324 robust, 388 sandwich algorithm, 272 scatter search, 84 Schur complement, 394 second-order cone, 395 sees the global optimum, 122 selection operator, 48 semidefinite program, 12 sensor localization, 379 separable quadratic knapsack problem, 16 sequential methods, 42 Shepard s interpolation, 369 Shor relaxation, 247 signomial, 286 simple linkage, 55 simplex, 11, 302, 391 simulated annealing, 48, 58, 74 Slater s condition, 260 smoothing methods, 79 space trajectory, 370, 386 space-filling curve, 254 spectral DC decomposition, 246 spline, 90 cubic, 98 cubic natural, 99 standard quadratic program (StQP), 23, 247 standard range reduction, 339 stochastic sequential algorithm, 122 strictly concave function, 395
5 Index 437 strictly conditional positive definite function, 93 strictly convex function, 395 strictly positive definite function, 93 strong consistency, 331 subgradient, 396 subgradient projection, 322 submodular, 137 subset problem, 16 sufficiently rich class of problems, 119 sum-of-squares (SOS), 218 relaxation, 221 support vector machine (SVM), 85 supporting hyperplane, 392 surrogate bound, 259 surrogate model, 85 surrogate-based optimization one stage methods, 86 two stage methods, 86 survival, 49 systematic error, 102 vertex polyhedral convex envelope, 130 vertical error, 272 weakly convex, 320 zero-dimensional, 219 tabu-search, 48 Taylor form function, 258 temperature, 75 test problems, 363 CEC, 369 Shepard, 369 trace, 394 triangle inequalities, 205 triangulation, 133 Kuhn, 137, 148 truncated tangency variety, 226 trust region problem, 12, 321 tunneling function, 76 method, 76 2LMP problem, 17 two-phase, 376 unisolvent, 90 unit simplex, 391 universal Kriging, 105 variable neighborhood search (VNS), 45, 63 variety, 219 vertex, 392
Nonlinear Programming
Nonlinear Programming SECOND EDITION Dimitri P. Bertsekas Massachusetts Institute of Technology WWW site for book Information and Orders http://world.std.com/~athenasc/index.html Athena Scientific, Belmont,
More informationConvex Optimization. Lijun Zhang Modification of
Convex Optimization Lijun Zhang zlj@nju.edu.cn http://cs.nju.edu.cn/zlj Modification of http://stanford.edu/~boyd/cvxbook/bv_cvxslides.pdf Outline Introduction Convex Sets & Functions Convex Optimization
More informationProgramming, numerics and optimization
Programming, numerics and optimization Lecture C-4: Constrained optimization Łukasz Jankowski ljank@ippt.pan.pl Institute of Fundamental Technological Research Room 4.32, Phone +22.8261281 ext. 428 June
More informationShiqian Ma, MAT-258A: Numerical Optimization 1. Chapter 2. Convex Optimization
Shiqian Ma, MAT-258A: Numerical Optimization 1 Chapter 2 Convex Optimization Shiqian Ma, MAT-258A: Numerical Optimization 2 2.1. Convex Optimization General optimization problem: min f 0 (x) s.t., f i
More informationTheoretical Concepts of Machine Learning
Theoretical Concepts of Machine Learning Part 2 Institute of Bioinformatics Johannes Kepler University, Linz, Austria Outline 1 Introduction 2 Generalization Error 3 Maximum Likelihood 4 Noise Models 5
More informationDavid G. Luenberger Yinyu Ye. Linear and Nonlinear. Programming. Fourth Edition. ö Springer
David G. Luenberger Yinyu Ye Linear and Nonlinear Programming Fourth Edition ö Springer Contents 1 Introduction 1 1.1 Optimization 1 1.2 Types of Problems 2 1.3 Size of Problems 5 1.4 Iterative Algorithms
More informationIE598 Big Data Optimization Summary Nonconvex Optimization
IE598 Big Data Optimization Summary Nonconvex Optimization Instructor: Niao He April 16, 2018 1 This Course Big Data Optimization Explore modern optimization theories, algorithms, and big data applications
More informationA Course in Convexity
A Course in Convexity Alexander Barvinok Graduate Studies in Mathematics Volume 54 American Mathematical Society Providence, Rhode Island Preface vii Chapter I. Convex Sets at Large 1 1. Convex Sets. Main
More informationLecture 7: Support Vector Machine
Lecture 7: Support Vector Machine Hien Van Nguyen University of Houston 9/28/2017 Separating hyperplane Red and green dots can be separated by a separating hyperplane Two classes are separable, i.e., each
More informationMathematical Programming and Research Methods (Part II)
Mathematical Programming and Research Methods (Part II) 4. Convexity and Optimization Massimiliano Pontil (based on previous lecture by Andreas Argyriou) 1 Today s Plan Convex sets and functions Types
More informationLinear methods for supervised learning
Linear methods for supervised learning LDA Logistic regression Naïve Bayes PLA Maximum margin hyperplanes Soft-margin hyperplanes Least squares resgression Ridge regression Nonlinear feature maps Sometimes
More informationConvex Sets (cont.) Convex Functions
Convex Sets (cont.) Convex Functions Optimization - 10725 Carlos Guestrin Carnegie Mellon University February 27 th, 2008 1 Definitions of convex sets Convex v. Non-convex sets Line segment definition:
More informationModule 1 Lecture Notes 2. Optimization Problem and Model Formulation
Optimization Methods: Introduction and Basic concepts 1 Module 1 Lecture Notes 2 Optimization Problem and Model Formulation Introduction In the previous lecture we studied the evolution of optimization
More informationDM545 Linear and Integer Programming. Lecture 2. The Simplex Method. Marco Chiarandini
DM545 Linear and Integer Programming Lecture 2 The Marco Chiarandini Department of Mathematics & Computer Science University of Southern Denmark Outline 1. 2. 3. 4. Standard Form Basic Feasible Solutions
More informationMVE165/MMG630, Applied Optimization Lecture 8 Integer linear programming algorithms. Ann-Brith Strömberg
MVE165/MMG630, Integer linear programming algorithms Ann-Brith Strömberg 2009 04 15 Methods for ILP: Overview (Ch. 14.1) Enumeration Implicit enumeration: Branch and bound Relaxations Decomposition methods:
More informationSimplicial Global Optimization
Simplicial Global Optimization Julius Žilinskas Vilnius University, Lithuania September, 7 http://web.vu.lt/mii/j.zilinskas Global optimization Find f = min x A f (x) and x A, f (x ) = f, where A R n.
More informationProbabilistic Graphical Models
School of Computer Science Probabilistic Graphical Models Theory of Variational Inference: Inner and Outer Approximation Eric Xing Lecture 14, February 29, 2016 Reading: W & J Book Chapters Eric Xing @
More informationApplied Integer Programming
Applied Integer Programming D.S. Chen; R.G. Batson; Y. Dang Fahimeh 8.2 8.7 April 21, 2015 Context 8.2. Convex sets 8.3. Describing a bounded polyhedron 8.4. Describing unbounded polyhedron 8.5. Faces,
More informationData Mining Chapter 8: Search and Optimization Methods Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University
Data Mining Chapter 8: Search and Optimization Methods Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University Search & Optimization Search and Optimization method deals with
More informationTHEORY OF LINEAR AND INTEGER PROGRAMMING
THEORY OF LINEAR AND INTEGER PROGRAMMING ALEXANDER SCHRIJVER Centrum voor Wiskunde en Informatica, Amsterdam A Wiley-Inter science Publication JOHN WILEY & SONS^ Chichester New York Weinheim Brisbane Singapore
More informationContents. I Basics 1. Copyright by SIAM. Unauthorized reproduction of this article is prohibited.
page v Preface xiii I Basics 1 1 Optimization Models 3 1.1 Introduction... 3 1.2 Optimization: An Informal Introduction... 4 1.3 Linear Equations... 7 1.4 Linear Optimization... 10 Exercises... 12 1.5
More informationOptimization under uncertainty: modeling and solution methods
Optimization under uncertainty: modeling and solution methods Paolo Brandimarte Dipartimento di Scienze Matematiche Politecnico di Torino e-mail: paolo.brandimarte@polito.it URL: http://staff.polito.it/paolo.brandimarte
More informationLocal and Global Minimum
Local and Global Minimum Stationary Point. From elementary calculus, a single variable function has a stationary point at if the derivative vanishes at, i.e., 0. Graphically, the slope of the function
More informationConvex Programs. COMPSCI 371D Machine Learning. COMPSCI 371D Machine Learning Convex Programs 1 / 21
Convex Programs COMPSCI 371D Machine Learning COMPSCI 371D Machine Learning Convex Programs 1 / 21 Logistic Regression! Support Vector Machines Support Vector Machines (SVMs) and Convex Programs SVMs are
More informationConvex Optimization Euclidean Distance Geometry 2ε
Convex Optimization Euclidean Distance Geometry 2ε In my career, I found that the best people are the ones that really understand the content, and they re a pain in the butt to manage. But you put up with
More informationInteger and Combinatorial Optimization
Integer and Combinatorial Optimization GEORGE NEMHAUSER School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, Georgia LAURENCE WOLSEY Center for Operations Research and
More informationIntroduction to Modern Control Systems
Introduction to Modern Control Systems Convex Optimization, Duality and Linear Matrix Inequalities Kostas Margellos University of Oxford AIMS CDT 2016-17 Introduction to Modern Control Systems November
More informationAdvanced Operations Research Techniques IE316. Quiz 2 Review. Dr. Ted Ralphs
Advanced Operations Research Techniques IE316 Quiz 2 Review Dr. Ted Ralphs IE316 Quiz 2 Review 1 Reading for The Quiz Material covered in detail in lecture Bertsimas 4.1-4.5, 4.8, 5.1-5.5, 6.1-6.3 Material
More informationSimplex Algorithm in 1 Slide
Administrivia 1 Canonical form: Simplex Algorithm in 1 Slide If we do pivot in A r,s >0, where c s
More informationGEOMETRIC TOOLS FOR COMPUTER GRAPHICS
GEOMETRIC TOOLS FOR COMPUTER GRAPHICS PHILIP J. SCHNEIDER DAVID H. EBERLY MORGAN KAUFMANN PUBLISHERS A N I M P R I N T O F E L S E V I E R S C I E N C E A M S T E R D A M B O S T O N L O N D O N N E W
More informationCME307/MS&E311 Optimization Theory Summary
CME307/MS&E311 Optimization Theory Summary Yinyu Ye Department of Management Science and Engineering Stanford University Stanford, CA 94305, U.S.A. http://www.stanford.edu/~yyye http://www.stanford.edu/class/msande311/
More informationCombinatorial Geometry & Topology arising in Game Theory and Optimization
Combinatorial Geometry & Topology arising in Game Theory and Optimization Jesús A. De Loera University of California, Davis LAST EPISODE... We discuss the content of the course... Convex Sets A set is
More informationLecture 4. Convexity Robust cost functions Optimizing non-convex functions. 3B1B Optimization Michaelmas 2017 A. Zisserman
Lecture 4 3B1B Optimization Michaelmas 2017 A. Zisserman Convexity Robust cost functions Optimizing non-convex functions grid search branch and bound simulated annealing evolutionary optimization The Optimization
More informationTutorial on Convex Optimization for Engineers
Tutorial on Convex Optimization for Engineers M.Sc. Jens Steinwandt Communications Research Laboratory Ilmenau University of Technology PO Box 100565 D-98684 Ilmenau, Germany jens.steinwandt@tu-ilmenau.de
More informationLinear programming and duality theory
Linear programming and duality theory Complements of Operations Research Giovanni Righini Linear Programming (LP) A linear program is defined by linear constraints, a linear objective function. Its variables
More informationLinear and Integer Programming :Algorithms in the Real World. Related Optimization Problems. How important is optimization?
Linear and Integer Programming 15-853:Algorithms in the Real World Linear and Integer Programming I Introduction Geometric Interpretation Simplex Method Linear or Integer programming maximize z = c T x
More informationConic Duality. yyye
Conic Linear Optimization and Appl. MS&E314 Lecture Note #02 1 Conic Duality Yinyu Ye Department of Management Science and Engineering Stanford University Stanford, CA 94305, U.S.A. http://www.stanford.edu/
More information60 2 Convex sets. {x a T x b} {x ã T x b}
60 2 Convex sets Exercises Definition of convexity 21 Let C R n be a convex set, with x 1,, x k C, and let θ 1,, θ k R satisfy θ i 0, θ 1 + + θ k = 1 Show that θ 1x 1 + + θ k x k C (The definition of convexity
More informationCalifornia Institute of Technology Crash-Course on Convex Optimization Fall Ec 133 Guilherme Freitas
California Institute of Technology HSS Division Crash-Course on Convex Optimization Fall 2011-12 Ec 133 Guilherme Freitas In this text, we will study the following basic problem: maximize x C f(x) subject
More informationLecture 3. Corner Polyhedron, Intersection Cuts, Maximal Lattice-Free Convex Sets. Tepper School of Business Carnegie Mellon University, Pittsburgh
Lecture 3 Corner Polyhedron, Intersection Cuts, Maximal Lattice-Free Convex Sets Gérard Cornuéjols Tepper School of Business Carnegie Mellon University, Pittsburgh January 2016 Mixed Integer Linear Programming
More informationINTRODUCTION TO LINEAR AND NONLINEAR PROGRAMMING
INTRODUCTION TO LINEAR AND NONLINEAR PROGRAMMING DAVID G. LUENBERGER Stanford University TT ADDISON-WESLEY PUBLISHING COMPANY Reading, Massachusetts Menlo Park, California London Don Mills, Ontario CONTENTS
More informationConvex Optimization - Chapter 1-2. Xiangru Lian August 28, 2015
Convex Optimization - Chapter 1-2 Xiangru Lian August 28, 2015 1 Mathematical optimization minimize f 0 (x) s.t. f j (x) 0, j=1,,m, (1) x S x. (x 1,,x n ). optimization variable. f 0. R n R. objective
More informationUnconstrained Optimization Principles of Unconstrained Optimization Search Methods
1 Nonlinear Programming Types of Nonlinear Programs (NLP) Convexity and Convex Programs NLP Solutions Unconstrained Optimization Principles of Unconstrained Optimization Search Methods Constrained Optimization
More informationConvexity: an introduction
Convexity: an introduction Geir Dahl CMA, Dept. of Mathematics and Dept. of Informatics University of Oslo 1 / 74 1. Introduction 1. Introduction what is convexity where does it arise main concepts and
More informationCS675: Convex and Combinatorial Optimization Spring 2018 Convex Sets. Instructor: Shaddin Dughmi
CS675: Convex and Combinatorial Optimization Spring 2018 Convex Sets Instructor: Shaddin Dughmi Outline 1 Convex sets, Affine sets, and Cones 2 Examples of Convex Sets 3 Convexity-Preserving Operations
More informationConvex Optimization Euclidean Distance Geometry 2ε
Convex Optimization Euclidean Distance Geometry 2ε 1 Overview 19 2 Convex geometry 31 2.1 Convex set.................................... 31 2.2 Vectorized-matrix inner product........................ 42
More informationEc 181: Convex Analysis and Economic Theory
Division of the Humanities and Social Sciences Ec 181: Convex Analysis and Economic Theory KC Border Winter 2018 v. 2018.03.08::13.11 src: front KC Border: for Ec 181, Winter 2018 Woe to the author who
More informationConvex Geometry arising in Optimization
Convex Geometry arising in Optimization Jesús A. De Loera University of California, Davis Berlin Mathematical School Summer 2015 WHAT IS THIS COURSE ABOUT? Combinatorial Convexity and Optimization PLAN
More informationLecture 2 September 3
EE 381V: Large Scale Optimization Fall 2012 Lecture 2 September 3 Lecturer: Caramanis & Sanghavi Scribe: Hongbo Si, Qiaoyang Ye 2.1 Overview of the last Lecture The focus of the last lecture was to give
More informationData Analysis 3. Support Vector Machines. Jan Platoš October 30, 2017
Data Analysis 3 Support Vector Machines Jan Platoš October 30, 2017 Department of Computer Science Faculty of Electrical Engineering and Computer Science VŠB - Technical University of Ostrava Table of
More informationLec13p1, ORF363/COS323
Lec13 Page 1 Lec13p1, ORF363/COS323 This lecture: Semidefinite programming (SDP) Definition and basic properties Review of positive semidefinite matrices SDP duality SDP relaxations for nonconvex optimization
More informationIntroduction to Mathematical Programming IE496. Final Review. Dr. Ted Ralphs
Introduction to Mathematical Programming IE496 Final Review Dr. Ted Ralphs IE496 Final Review 1 Course Wrap-up: Chapter 2 In the introduction, we discussed the general framework of mathematical modeling
More informationCME307/MS&E311 Theory Summary
CME307/MS&E311 Theory Summary Yinyu Ye Department of Management Science and Engineering Stanford University Stanford, CA 94305, U.S.A. http://www.stanford.edu/~yyye http://www.stanford.edu/class/msande311/
More informationLecture 5: Properties of convex sets
Lecture 5: Properties of convex sets Rajat Mittal IIT Kanpur This week we will see properties of convex sets. These properties make convex sets special and are the reason why convex optimization problems
More informationNumerical Optimization
Numerical Optimization Quantitative Macroeconomics Raül Santaeulàlia-Llopis MOVE-UAB and Barcelona GSE Fall 2018 Raül Santaeulàlia-Llopis (MOVE-UAB,BGSE) QM: Numerical Optimization Fall 2018 1 / 46 1 Introduction
More informationMath 5593 Linear Programming Lecture Notes
Math 5593 Linear Programming Lecture Notes Unit II: Theory & Foundations (Convex Analysis) University of Colorado Denver, Fall 2013 Topics 1 Convex Sets 1 1.1 Basic Properties (Luenberger-Ye Appendix B.1).........................
More informationCOMS 4771 Support Vector Machines. Nakul Verma
COMS 4771 Support Vector Machines Nakul Verma Last time Decision boundaries for classification Linear decision boundary (linear classification) The Perceptron algorithm Mistake bound for the perceptron
More informationA Short SVM (Support Vector Machine) Tutorial
A Short SVM (Support Vector Machine) Tutorial j.p.lewis CGIT Lab / IMSC U. Southern California version 0.zz dec 004 This tutorial assumes you are familiar with linear algebra and equality-constrained optimization/lagrange
More informationLECTURE 13: SOLUTION METHODS FOR CONSTRAINED OPTIMIZATION. 1. Primal approach 2. Penalty and barrier methods 3. Dual approach 4. Primal-dual approach
LECTURE 13: SOLUTION METHODS FOR CONSTRAINED OPTIMIZATION 1. Primal approach 2. Penalty and barrier methods 3. Dual approach 4. Primal-dual approach Basic approaches I. Primal Approach - Feasible Direction
More informationToday. Golden section, discussion of error Newton s method. Newton s method, steepest descent, conjugate gradient
Optimization Last time Root finding: definition, motivation Algorithms: Bisection, false position, secant, Newton-Raphson Convergence & tradeoffs Example applications of Newton s method Root finding in
More informationLecture 2: August 31
10-725/36-725: Convex Optimization Fall 2016 Lecture 2: August 31 Lecturer: Lecturer: Ryan Tibshirani Scribes: Scribes: Lidan Mu, Simon Du, Binxuan Huang 2.1 Review A convex optimization problem is of
More informationKernel Methods & Support Vector Machines
& Support Vector Machines & Support Vector Machines Arvind Visvanathan CSCE 970 Pattern Recognition 1 & Support Vector Machines Question? Draw a single line to separate two classes? 2 & Support Vector
More informationExpectation Propagation
Expectation Propagation Erik Sudderth 6.975 Week 11 Presentation November 20, 2002 Introduction Goal: Efficiently approximate intractable distributions Features of Expectation Propagation (EP): Deterministic,
More informationSupport Vector Machines.
Support Vector Machines srihari@buffalo.edu SVM Discussion Overview 1. Overview of SVMs 2. Margin Geometry 3. SVM Optimization 4. Overlapping Distributions 5. Relationship to Logistic Regression 6. Dealing
More informationInteger Programming Theory
Integer Programming Theory Laura Galli October 24, 2016 In the following we assume all functions are linear, hence we often drop the term linear. In discrete optimization, we seek to find a solution x
More information6 Randomized rounding of semidefinite programs
6 Randomized rounding of semidefinite programs We now turn to a new tool which gives substantially improved performance guarantees for some problems We now show how nonlinear programming relaxations can
More informationMathematical Tools in Computer Graphics with C# Implementations Table of Contents
Mathematical Tools in Computer Graphics with C# Implementations by Hardy Alexandre, Willi-Hans Steeb, World Scientific Publishing Company, Incorporated, 2008 Table of Contents List of Figures Notation
More informationLecture 2: August 29, 2018
10-725/36-725: Convex Optimization Fall 2018 Lecturer: Ryan Tibshirani Lecture 2: August 29, 2018 Scribes: Adam Harley Note: LaTeX template courtesy of UC Berkeley EECS dept. Disclaimer: These notes have
More informationLaGO - A solver for mixed integer nonlinear programming
LaGO - A solver for mixed integer nonlinear programming Ivo Nowak June 1 2005 Problem formulation MINLP: min f(x, y) s.t. g(x, y) 0 h(x, y) = 0 x [x, x] y [y, y] integer MINLP: - n
More information3 INTEGER LINEAR PROGRAMMING
3 INTEGER LINEAR PROGRAMMING PROBLEM DEFINITION Integer linear programming problem (ILP) of the decision variables x 1,..,x n : (ILP) subject to minimize c x j j n j= 1 a ij x j x j 0 x j integer n j=
More informationCS 435, 2018 Lecture 2, Date: 1 March 2018 Instructor: Nisheeth Vishnoi. Convex Programming and Efficiency
CS 435, 2018 Lecture 2, Date: 1 March 2018 Instructor: Nisheeth Vishnoi Convex Programming and Efficiency In this lecture, we formalize convex programming problem, discuss what it means to solve it efficiently
More informationFast-Lipschitz Optimization
Fast-Lipschitz Optimization DREAM Seminar Series University of California at Berkeley September 11, 2012 Carlo Fischione ACCESS Linnaeus Center, Electrical Engineering KTH Royal Institute of Technology
More informationIntroduction to Optimization
Introduction to Optimization Second Order Optimization Methods Marc Toussaint U Stuttgart Planned Outline Gradient-based optimization (1st order methods) plain grad., steepest descent, conjugate grad.,
More informationGEOMETRIC LIBRARY. Maharavo Randrianarivony
GEOMETRIC LIBRARY Maharavo Randrianarivony During the last four years, I have maintained a numerical geometric library. The constituting routines, which are summarized in the following list, are implemented
More informationContents. Preface... VII. Part I Classical Topics Revisited
Contents Preface........................................................ VII Part I Classical Topics Revisited 1 Sphere Packings........................................... 3 1.1 Kissing Numbers of Spheres..............................
More informationAPPLIED OPTIMIZATION WITH MATLAB PROGRAMMING
APPLIED OPTIMIZATION WITH MATLAB PROGRAMMING Second Edition P. Venkataraman Rochester Institute of Technology WILEY JOHN WILEY & SONS, INC. CONTENTS PREFACE xiii 1 Introduction 1 1.1. Optimization Fundamentals
More informationLinear Programming and its Applications
Linear Programming and its Applications Outline for Today What is linear programming (LP)? Examples Formal definition Geometric intuition Why is LP useful? A first look at LP algorithms Duality Linear
More informationSubmodularity Reading Group. Matroid Polytopes, Polymatroid. M. Pawan Kumar
Submodularity Reading Group Matroid Polytopes, Polymatroid M. Pawan Kumar http://www.robots.ox.ac.uk/~oval/ Outline Linear Programming Matroid Polytopes Polymatroid Polyhedron Ax b A : m x n matrix b:
More information25. NLP algorithms. ˆ Overview. ˆ Local methods. ˆ Constrained optimization. ˆ Global methods. ˆ Black-box methods.
CS/ECE/ISyE 524 Introduction to Optimization Spring 2017 18 25. NLP algorithms ˆ Overview ˆ Local methods ˆ Constrained optimization ˆ Global methods ˆ Black-box methods ˆ Course wrap-up Laurent Lessard
More informationModern Methods of Data Analysis - WS 07/08
Modern Methods of Data Analysis Lecture XV (04.02.08) Contents: Function Minimization (see E. Lohrmann & V. Blobel) Optimization Problem Set of n independent variables Sometimes in addition some constraints
More informationChapter 14 Global Search Algorithms
Chapter 14 Global Search Algorithms An Introduction to Optimization Spring, 2015 Wei-Ta Chu 1 Introduction We discuss various search methods that attempts to search throughout the entire feasible set.
More informationStatistical Modeling with Spline Functions Methodology and Theory
This is page 1 Printer: Opaque this Statistical Modeling with Spline Functions Methodology and Theory Mark H. Hansen University of California at Los Angeles Jianhua Z. Huang University of Pennsylvania
More informationB553 Lecture 12: Global Optimization
B553 Lecture 12: Global Optimization Kris Hauser February 20, 2012 Most of the techniques we have examined in prior lectures only deal with local optimization, so that we can only guarantee convergence
More information1. INTRODUCTION ABSTRACT
Copyright 2008, Society of Photo-Optical Instrumentation Engineers (SPIE). This paper was published in the proceedings of the August 2008 SPIE Annual Meeting and is made available as an electronic preprint
More informationCOM Optimization for Communications Summary: Convex Sets and Convex Functions
1 Convex Sets Affine Sets COM524500 Optimization for Communications Summary: Convex Sets and Convex Functions A set C R n is said to be affine if A point x 1, x 2 C = θx 1 + (1 θ)x 2 C, θ R (1) y = k θ
More informationAlgebraic Geometry of Segmentation and Tracking
Ma191b Winter 2017 Geometry of Neuroscience Geometry of lines in 3-space and Segmentation and Tracking This lecture is based on the papers: Reference: Marco Pellegrini, Ray shooting and lines in space.
More informationConstrained optimization
Constrained optimization A general constrained optimization problem has the form where The Lagrangian function is given by Primal and dual optimization problems Primal: Dual: Weak duality: Strong duality:
More informationLecture 2. Topology of Sets in R n. August 27, 2008
Lecture 2 Topology of Sets in R n August 27, 2008 Outline Vectors, Matrices, Norms, Convergence Open and Closed Sets Special Sets: Subspace, Affine Set, Cone, Convex Set Special Convex Sets: Hyperplane,
More informationAffine function. suppose f : R n R m is affine (f(x) =Ax + b with A R m n, b R m ) the image of a convex set under f is convex
Affine function suppose f : R n R m is affine (f(x) =Ax + b with A R m n, b R m ) the image of a convex set under f is convex S R n convex = f(s) ={f(x) x S} convex the inverse image f 1 (C) of a convex
More information5.3 Cutting plane methods and Gomory fractional cuts
5.3 Cutting plane methods and Gomory fractional cuts (ILP) min c T x s.t. Ax b x 0integer feasible region X Assumption: a ij, c j and b i integer. Observation: The feasible region of an ILP can be described
More informationLecture 2 Convex Sets
Optimization Theory and Applications Lecture 2 Convex Sets Prof. Chun-Hung Liu Dept. of Electrical and Computer Engineering National Chiao Tung University Fall 2016 2016/9/29 Lecture 2: Convex Sets 1 Outline
More informationConvex Optimization CMU-10725
Convex Optimization CMU-10725 Ellipsoid Methods Barnabás Póczos & Ryan Tibshirani Outline Linear programs Simplex algorithm Running time: Polynomial or Exponential? Cutting planes & Ellipsoid methods for
More informationCS599: Convex and Combinatorial Optimization Fall 2013 Lecture 4: Convex Sets. Instructor: Shaddin Dughmi
CS599: Convex and Combinatorial Optimization Fall 2013 Lecture 4: Convex Sets Instructor: Shaddin Dughmi Announcements New room: KAP 158 Today: Convex Sets Mostly from Boyd and Vandenberghe. Read all of
More informationRecent Developments in Model-based Derivative-free Optimization
Recent Developments in Model-based Derivative-free Optimization Seppo Pulkkinen April 23, 2010 Introduction Problem definition The problem we are considering is a nonlinear optimization problem with constraints:
More informationCONVEX OPTIMIZATION: A SELECTIVE OVERVIEW
1! CONVEX OPTIMIZATION: A SELECTIVE OVERVIEW Dimitri Bertsekas! M.I.T.! Taiwan! May 2010! 2! OUTLINE! Convexity issues in optimization! Common geometrical framework for duality and minimax! Unifying framework
More informationChapter 4 Convex Optimization Problems
Chapter 4 Convex Optimization Problems Shupeng Gui Computer Science, UR October 16, 2015 hupeng Gui (Computer Science, UR) Convex Optimization Problems October 16, 2015 1 / 58 Outline 1 Optimization problems
More informationPolyhedral Compilation Foundations
Polyhedral Compilation Foundations Louis-Noël Pouchet pouchet@cse.ohio-state.edu Dept. of Computer Science and Engineering, the Ohio State University Feb 15, 2010 888.11, Class #4 Introduction: Polyhedral
More informationbe a polytope. has such a representation iff it contains the origin in its interior. For a generic, sort the inequalities so that
( Shelling (Bruggesser-Mani 1971) and Ranking Let be a polytope. has such a representation iff it contains the origin in its interior. For a generic, sort the inequalities so that. a ranking of vertices
More informationDiscrete Optimization 2010 Lecture 5 Min-Cost Flows & Total Unimodularity
Discrete Optimization 2010 Lecture 5 Min-Cost Flows & Total Unimodularity Marc Uetz University of Twente m.uetz@utwente.nl Lecture 5: sheet 1 / 26 Marc Uetz Discrete Optimization Outline 1 Min-Cost Flows
More informationPreface to the Second Edition. Preface to the First Edition. 1 Introduction 1
Preface to the Second Edition Preface to the First Edition vii xi 1 Introduction 1 2 Overview of Supervised Learning 9 2.1 Introduction... 9 2.2 Variable Types and Terminology... 9 2.3 Two Simple Approaches
More information