p. 5 First line of Section 1.4. Change nonzero vector v R n to nonzero vector v C n int([x, y]) = (x, y)

Size: px
Start display at page:

Download "p. 5 First line of Section 1.4. Change nonzero vector v R n to nonzero vector v C n int([x, y]) = (x, y)"

Transcription

1 Corrections for the first printing of INTRODUCTION TO NON- LINEAR OPTIMIZATION: THEORY, ALGORITHMS, AND AP- PLICATIONS WITH MATLAB, SIAM, 204, by Amir Beck 2 Last Updated: June 8, 207 p. 5 First line of Section.4. Change nonzero vecr v R n nonzero vecr v C n p. 7 In Example.5, remove the line (it is incorrect when n > ) int([x, y]) = (x, y) p. 7 One line after Definition.6, remove open line segments. p. 8 At the end of the page, the sentence starting from For any i =, 2,..., n, the directional derivative at... until the end of the page should change For any i =, 2,..., n, if the limit f f(x + te i ) f(x) (x) = lim x i t 0 t exists, then it is called the i-th partial derivative of f at x. p.9. Line -9. Replace o( ) : R n + R with o( ) : R + R p.9 two lines before Theorem 2.7, replace charachterization with characterization p. 2 Change A 2 A 2 = UEU T UEU T = UEEU T = UDU = A. A 2 A 2 = UEU T UEU T = UEEU T = UDU T = A. (add transpose the last U) p. 37 Two lines before (3.) change if A if not of full column if A is not of full column p. 47 Line 4. Change (ˆx, R) (ˆx, ˆR) p. 47 In Lemma 3.5, change r = ˆx 2 y n+ ˆr = ˆx 2 y n+ The corrections were incorporated in the second printing of the book 2 I would like thank Heinz Bauschke, David Cohen, Christian Kanzow, Luba Tetruashvili and Yakov Vaisbourd for their invaluable comments that helped compile the list of corrections.

2 p. 58 At the end, replace Therefore, with It holds that p. 62 Last sentence before the MATLAB code. Change p. 68 Change anchor point anchor points p. 69 Two lines before (4.4), change x a, a 2,..., a m x 0 a, a 2,..., a m. p. 72 Change cannot be verified easily cannot be easily guaranteed p. 72 The two occurrences of min{f(x 0 ), f(x ),..., f(x p )} should be replaced with min{f(x 0 ), f(x ),..., f(x m )} p. 73 Line 5 in Section After meaning that add there exists L > 0 for which p. 73 After denote the class by C,. add the following sentence: For a given D R n, the set of all continuously differentiable functions over D whose gradient satisfies the above Lipschitz condition for any x, y D is denoted by C, L (D). p. 74 Lemma The sentence Let f C, L (Rn ). Then for any x, y R n should be replaced with Let D R n and f C, L (D) for some L > 0. Then for any x, y D satisfying [x, y] D it holds that p. 85 Replace the sentence Combining the latter equality with the fact that 2 f(x k ) mi implies that 2 f(x k ) m. Hence, Since 2 f(x k ) mi, it follows that 2 f(x k ) m. Hence, p. 94 Two occurrences of 7 iterations should be replaced with 8 iterations p. 05 Line 3. Change The set C is clearly a convex cone The set C is clearly a convex set p. 05 Example 6.7. Change Lorenz Lorentz p. 05 Last line. Change K 2 = {(x, x 2 ) T : x t + x 2 0} = {(x, x 2 ) : x = 0, x 2 0}, K 2 = {(x, x 2 ) T : x t+x 2 0 for all t R} = {(x, x 2 ) : x = 0, x 2 0}, p. 06 Just before Theorem 6.23, change recall that in Carathéodory n + vecrs recall that in Carathéodory s theorem n + vecrs p. 07 2nd line. Change convex hull conic hull. 2

3 p. In definition 6.33, change Let S R n Let S R n be a convex set p. 8 Line 4. After concave add over a convex set C R n. p. 20 Last line. Replace the global minimizer with a global minimizer. p. 23 Second line in the premise of Theorem 7.2. Add over C between convex and if and only if p. 33 Change Then by the Krein-Milman theorem (Theorem 6.35), for any Then obviously, for any (it is correct as it is, but there s no real need use Krein-Milman) p. 40 Second line of the proof of Lemma It should be y q and not y p p. 47 Second line in Section 8. and one line after (8.), change closed and convex set convex set p. 47 Change level sets of convex sets level sets of convex functions p. 47 Line -8. Delete the sentence starting with The set C is closed and ending with see Theorem p. 49 Change we assume that α i β i+ for any i =, 2,..., n we assume that α i β j for any j > i p. 50 Change since the condition α i β i+ will guarantee since the feasibility condition will guarantee p. 57 Line -4. Should be on R n + instead of on R n p. 69 Line -3. Change local optimum local minimum. p. 7 Line -3. Change differetiable differentiable p. 72 Just before (9.6), change stationry stationary p. 74 Theorem 9.8. Change Then z = P C (x) if and only if Then z = P C (x) if and only if z C and p. 77 In the premise of Lemma 9.2, change Suppose that L L 2 Suppose that L L 2 > 0. p. 82 Equation (9.29) should end with a period and not a comma p. 90 Exercise 9.6. Change dented denoted p. 97 Change x x in equations (0.8), (0.0), (0.) and (0.2) 3

4 p. 207 In definition., change continuously differentiable over the closed and convex set continuously differentiable over the set. p. 207 Lemma.2. Change the problem from (G) (G) min h(x) s.t. x C, min f(x) s.t. x C, p. 22 In the last line, change the constraint x 2 + 2x x 3 3 = 2 x 2 + 2x x 2 3 p. 25 Change the first sentence of the proof of Theorem.3: As in the proof of the necessity of the KKT conditions (Theorem.5), By Theorem.4, p. 225 In the definition of I(x ), change f i (x ) = 0 g i (x ) = 0 p. 226 Change ( ) 0 2 xl(x, x 2, µ) ( ) 0 d 2 0 d 2 ( ) ( ) 0 d2 2 0 x L(x, x 2, µ) 0 d 2 p. 233 Line 4. Remove f from the chain of equalities/inequalities. p. 238 Line -6. Change λ, λ 2, λ m λ, λ 2,..., λ m p. 239 First line. Change there are there may be p. 239 In the premise of Theorem 2.2 change being finite-valued functions being functions p. 24 Just before Theorem 2.5, add the sentence Although the theorem holds for any convex set C, we will state and prove it only for convex sets with a nonempty interior p. 24 Theorem 2.5. Replace convex set and let with convex set with a nonempty interior and let The beginning of the proof of Theorem 2.5 should be: Although the theorem holds for any convex set C, we will prove it only for sets with a nonempty interior p. 242 In the statement of Theorem, 2.6, replace two nonempty convex sets such that with two convex sets with nonempty interiors such that 4

5 p. 242 First line of the proof of Theorem 2.6, after the parentheses write with a nonempty interior p. 242 First line. Replace Note that S, T are nonempty and convex and in addition with Note that S, T are convex with nonempty interiors and in addition p. 259 In the first displayed equation, change L(x, λ, λ 2,..., λ m ) L(x, y, y 2,..., y m, λ, λ 2,..., λ m ). p th line after (2.27). Remove (see also part (i) of Exercise.3) p. 267 At the displayed equation at the beginning of Section change the right-hand side of the inequality and equality constraints from 0. p. 272 In question 2.2, change Lat Let 5

Lecture 6 - Convex Sets

Lecture 6 - Convex Sets Lecture 6 - Convex Sets Definition A set C R n is called convex if for any x, y C and λ [0, 1], the point λx + (1 λ)y belongs to C. The above definition is equivalent to saying that for any x, y C, the

More information

Additional Exercises for Introduction to Nonlinear Optimization Amir Beck March 16, 2017

Additional Exercises for Introduction to Nonlinear Optimization Amir Beck March 16, 2017 Additional Exercises for Introduction to Nonlinear Optimization Amir Beck March 16, 2017 Chapter 1 - Mathematical Preliminaries 1.1 Let S R n. (a) Suppose that T is an open set satisfying T S. Prove that

More information

Convex Optimization Lecture 6: KKT Conditions, and applications

Convex Optimization Lecture 6: KKT Conditions, and applications Convex Optimization Lecture 6: KKT Conditions, and applications Dr. Michel Baes, IFOR / ETH Zürich Quick recall of last week s lecture Various aspects of convexity: The set of minimizers is convex. Convex

More information

Lecture 3: Lagrangian duality and algorithms for the Lagrangian dual problem

Lecture 3: Lagrangian duality and algorithms for the Lagrangian dual problem Lecture 3: Lagrangian duality and algorithms for the Lagrangian dual problem Michael Patriksson 0-0 The Relaxation Theorem 1 Problem: find f := infimum f(x), x subject to x S, (1a) (1b) where f : R n R

More information

Constrained Optimization

Constrained Optimization 1 / 22 Constrained Optimization ME598/494 Lecture Max Yi Ren Department of Mechanical Engineering, Arizona State University March 30, 2015 2 / 22 1. Equality constraints only 1.1 Reduced gradient 1.2 Lagrange

More information

Shiqian Ma, MAT-258A: Numerical Optimization 1. Chapter 4. Subgradient

Shiqian Ma, MAT-258A: Numerical Optimization 1. Chapter 4. Subgradient Shiqian Ma, MAT-258A: Numerical Optimization 1 Chapter 4 Subgradient Shiqian Ma, MAT-258A: Numerical Optimization 2 4.1. Subgradients definition subgradient calculus duality and optimality conditions Shiqian

More information

Additional Homework Problems

Additional Homework Problems Additional Homework Problems Robert M. Freund April, 2004 2004 Massachusetts Institute of Technology. 1 2 1 Exercises 1. Let IR n + denote the nonnegative orthant, namely IR + n = {x IR n x j ( ) 0,j =1,...,n}.

More information

Math 273a: Optimization Subgradients of convex functions

Math 273a: Optimization Subgradients of convex functions Math 273a: Optimization Subgradients of convex functions Made by: Damek Davis Edited by Wotao Yin Department of Mathematics, UCLA Fall 2015 online discussions on piazza.com 1 / 42 Subgradients Assumptions

More information

Extreme Abridgment of Boyd and Vandenberghe s Convex Optimization

Extreme Abridgment of Boyd and Vandenberghe s Convex Optimization Extreme Abridgment of Boyd and Vandenberghe s Convex Optimization Compiled by David Rosenberg Abstract Boyd and Vandenberghe s Convex Optimization book is very well-written and a pleasure to read. The

More information

Some Properties of the Augmented Lagrangian in Cone Constrained Optimization

Some Properties of the Augmented Lagrangian in Cone Constrained Optimization MATHEMATICS OF OPERATIONS RESEARCH Vol. 29, No. 3, August 2004, pp. 479 491 issn 0364-765X eissn 1526-5471 04 2903 0479 informs doi 10.1287/moor.1040.0103 2004 INFORMS Some Properties of the Augmented

More information

5. Duality. Lagrangian

5. Duality. Lagrangian 5. Duality Convex Optimization Boyd & Vandenberghe Lagrange dual problem weak and strong duality geometric interpretation optimality conditions perturbation and sensitivity analysis examples generalized

More information

A CHARACTERIZATION OF STRICT LOCAL MINIMIZERS OF ORDER ONE FOR STATIC MINMAX PROBLEMS IN THE PARAMETRIC CONSTRAINT CASE

A CHARACTERIZATION OF STRICT LOCAL MINIMIZERS OF ORDER ONE FOR STATIC MINMAX PROBLEMS IN THE PARAMETRIC CONSTRAINT CASE Journal of Applied Analysis Vol. 6, No. 1 (2000), pp. 139 148 A CHARACTERIZATION OF STRICT LOCAL MINIMIZERS OF ORDER ONE FOR STATIC MINMAX PROBLEMS IN THE PARAMETRIC CONSTRAINT CASE A. W. A. TAHA Received

More information

UNDERGROUND LECTURE NOTES 1: Optimality Conditions for Constrained Optimization Problems

UNDERGROUND LECTURE NOTES 1: Optimality Conditions for Constrained Optimization Problems UNDERGROUND LECTURE NOTES 1: Optimality Conditions for Constrained Optimization Problems Robert M. Freund February 2016 c 2016 Massachusetts Institute of Technology. All rights reserved. 1 1 Introduction

More information

Lagrange Relaxation and Duality

Lagrange Relaxation and Duality Lagrange Relaxation and Duality As we have already known, constrained optimization problems are harder to solve than unconstrained problems. By relaxation we can solve a more difficult problem by a simpler

More information

Primal/Dual Decomposition Methods

Primal/Dual Decomposition Methods Primal/Dual Decomposition Methods Daniel P. Palomar Hong Kong University of Science and Technology (HKUST) ELEC5470 - Convex Optimization Fall 2018-19, HKUST, Hong Kong Outline of Lecture Subgradients

More information

Assignment 1: From the Definition of Convexity to Helley Theorem

Assignment 1: From the Definition of Convexity to Helley Theorem Assignment 1: From the Definition of Convexity to Helley Theorem Exercise 1 Mark in the following list the sets which are convex: 1. {x R 2 : x 1 + i 2 x 2 1, i = 1,..., 10} 2. {x R 2 : x 2 1 + 2ix 1x

More information

Optimality Conditions for Constrained Optimization

Optimality Conditions for Constrained Optimization 72 CHAPTER 7 Optimality Conditions for Constrained Optimization 1. First Order Conditions In this section we consider first order optimality conditions for the constrained problem P : minimize f 0 (x)

More information

Barrier Method. Javier Peña Convex Optimization /36-725

Barrier Method. Javier Peña Convex Optimization /36-725 Barrier Method Javier Peña Convex Optimization 10-725/36-725 1 Last time: Newton s method For root-finding F (x) = 0 x + = x F (x) 1 F (x) For optimization x f(x) x + = x 2 f(x) 1 f(x) Assume f strongly

More information

Convex Optimization M2

Convex Optimization M2 Convex Optimization M2 Lecture 3 A. d Aspremont. Convex Optimization M2. 1/49 Duality A. d Aspremont. Convex Optimization M2. 2/49 DMs DM par email: dm.daspremont@gmail.com A. d Aspremont. Convex Optimization

More information

Lecture 18: Optimization Programming

Lecture 18: Optimization Programming Fall, 2016 Outline Unconstrained Optimization 1 Unconstrained Optimization 2 Equality-constrained Optimization Inequality-constrained Optimization Mixture-constrained Optimization 3 Quadratic Programming

More information

Lecture 3. Optimization Problems and Iterative Algorithms

Lecture 3. Optimization Problems and Iterative Algorithms Lecture 3 Optimization Problems and Iterative Algorithms January 13, 2016 This material was jointly developed with Angelia Nedić at UIUC for IE 598ns Outline Special Functions: Linear, Quadratic, Convex

More information

lim sup f(x k ) d k < 0, {α k } K 0. Hence, by the definition of the Armijo rule, we must have for some index k 0

lim sup f(x k ) d k < 0, {α k } K 0. Hence, by the definition of the Armijo rule, we must have for some index k 0 Corrections for the book NONLINEAR PROGRAMMING: 2ND EDITION Athena Scientific 1999 by Dimitri P. Bertsekas Note: Many of these corrections have been incorporated in the 2nd Printing of the book. See the

More information

IE 521 Convex Optimization

IE 521 Convex Optimization Lecture 1: 16th January 2019 Outline 1 / 20 Which set is different from others? Figure: Four sets 2 / 20 Which set is different from others? Figure: Four sets 3 / 20 Interior, Closure, Boundary Definition.

More information

The general programming problem is the nonlinear programming problem where a given function is maximized subject to a set of inequality constraints.

The general programming problem is the nonlinear programming problem where a given function is maximized subject to a set of inequality constraints. 1 Optimization Mathematical programming refers to the basic mathematical problem of finding a maximum to a function, f, subject to some constraints. 1 In other words, the objective is to find a point,

More information

Locally convex spaces, the hyperplane separation theorem, and the Krein-Milman theorem

Locally convex spaces, the hyperplane separation theorem, and the Krein-Milman theorem 56 Chapter 7 Locally convex spaces, the hyperplane separation theorem, and the Krein-Milman theorem Recall that C(X) is not a normed linear space when X is not compact. On the other hand we could use semi

More information

1 Convexity, concavity and quasi-concavity. (SB )

1 Convexity, concavity and quasi-concavity. (SB ) UNIVERSITY OF MARYLAND ECON 600 Summer 2010 Lecture Two: Unconstrained Optimization 1 Convexity, concavity and quasi-concavity. (SB 21.1-21.3.) For any two points, x, y R n, we can trace out the line of

More information

Optimization and Optimal Control in Banach Spaces

Optimization and Optimal Control in Banach Spaces Optimization and Optimal Control in Banach Spaces Bernhard Schmitzer October 19, 2017 1 Convex non-smooth optimization with proximal operators Remark 1.1 (Motivation). Convex optimization: easier to solve,

More information

Introduction to Machine Learning Lecture 7. Mehryar Mohri Courant Institute and Google Research

Introduction to Machine Learning Lecture 7. Mehryar Mohri Courant Institute and Google Research Introduction to Machine Learning Lecture 7 Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Convex Optimization Differentiation Definition: let f : X R N R be a differentiable function,

More information

I.3. LMI DUALITY. Didier HENRION EECI Graduate School on Control Supélec - Spring 2010

I.3. LMI DUALITY. Didier HENRION EECI Graduate School on Control Supélec - Spring 2010 I.3. LMI DUALITY Didier HENRION henrion@laas.fr EECI Graduate School on Control Supélec - Spring 2010 Primal and dual For primal problem p = inf x g 0 (x) s.t. g i (x) 0 define Lagrangian L(x, z) = g 0

More information

Numerical Optimization

Numerical Optimization Constrained Optimization Computer Science and Automation Indian Institute of Science Bangalore 560 012, India. NPTEL Course on Constrained Optimization Constrained Optimization Problem: min h j (x) 0,

More information

Applications of Linear Programming

Applications of Linear Programming Applications of Linear Programming lecturer: András London University of Szeged Institute of Informatics Department of Computational Optimization Lecture 9 Non-linear programming In case of LP, the goal

More information

Convex Optimization Boyd & Vandenberghe. 5. Duality

Convex Optimization Boyd & Vandenberghe. 5. Duality 5. Duality Convex Optimization Boyd & Vandenberghe Lagrange dual problem weak and strong duality geometric interpretation optimality conditions perturbation and sensitivity analysis examples generalized

More information

Subgradient. Acknowledgement: this slides is based on Prof. Lieven Vandenberghes lecture notes. definition. subgradient calculus

Subgradient. Acknowledgement: this slides is based on Prof. Lieven Vandenberghes lecture notes. definition. subgradient calculus 1/41 Subgradient Acknowledgement: this slides is based on Prof. Lieven Vandenberghes lecture notes definition subgradient calculus duality and optimality conditions directional derivative Basic inequality

More information

Lecture 25: Subgradient Method and Bundle Methods April 24

Lecture 25: Subgradient Method and Bundle Methods April 24 IE 51: Convex Optimization Spring 017, UIUC Lecture 5: Subgradient Method and Bundle Methods April 4 Instructor: Niao He Scribe: Shuanglong Wang Courtesy warning: hese notes do not necessarily cover everything

More information

The Relation Between Pseudonormality and Quasiregularity in Constrained Optimization 1

The Relation Between Pseudonormality and Quasiregularity in Constrained Optimization 1 October 2003 The Relation Between Pseudonormality and Quasiregularity in Constrained Optimization 1 by Asuman E. Ozdaglar and Dimitri P. Bertsekas 2 Abstract We consider optimization problems with equality,

More information

Chap 2. Optimality conditions

Chap 2. Optimality conditions Chap 2. Optimality conditions Version: 29-09-2012 2.1 Optimality conditions in unconstrained optimization Recall the definitions of global, local minimizer. Geometry of minimization Consider for f C 1

More information

1 Computing with constraints

1 Computing with constraints Notes for 2017-04-26 1 Computing with constraints Recall that our basic problem is minimize φ(x) s.t. x Ω where the feasible set Ω is defined by equality and inequality conditions Ω = {x R n : c i (x)

More information

Appendix PRELIMINARIES 1. THEOREMS OF ALTERNATIVES FOR SYSTEMS OF LINEAR CONSTRAINTS

Appendix PRELIMINARIES 1. THEOREMS OF ALTERNATIVES FOR SYSTEMS OF LINEAR CONSTRAINTS Appendix PRELIMINARIES 1. THEOREMS OF ALTERNATIVES FOR SYSTEMS OF LINEAR CONSTRAINTS Here we consider systems of linear constraints, consisting of equations or inequalities or both. A feasible solution

More information

Finite Dimensional Optimization Part III: Convex Optimization 1

Finite Dimensional Optimization Part III: Convex Optimization 1 John Nachbar Washington University March 21, 2017 Finite Dimensional Optimization Part III: Convex Optimization 1 1 Saddle points and KKT. These notes cover another important approach to optimization,

More information

Optimisation in Higher Dimensions

Optimisation in Higher Dimensions CHAPTER 6 Optimisation in Higher Dimensions Beyond optimisation in 1D, we will study two directions. First, the equivalent in nth dimension, x R n such that f(x ) f(x) for all x R n. Second, constrained

More information

Chapter 2 Convex Analysis

Chapter 2 Convex Analysis Chapter 2 Convex Analysis The theory of nonsmooth analysis is based on convex analysis. Thus, we start this chapter by giving basic concepts and results of convexity (for further readings see also [202,

More information

LECTURE 25: REVIEW/EPILOGUE LECTURE OUTLINE

LECTURE 25: REVIEW/EPILOGUE LECTURE OUTLINE LECTURE 25: REVIEW/EPILOGUE LECTURE OUTLINE CONVEX ANALYSIS AND DUALITY Basic concepts of convex analysis Basic concepts of convex optimization Geometric duality framework - MC/MC Constrained optimization

More information

Examination paper for TMA4180 Optimization I

Examination paper for TMA4180 Optimization I Department of Mathematical Sciences Examination paper for TMA4180 Optimization I Academic contact during examination: Phone: Examination date: 26th May 2016 Examination time (from to): 09:00 13:00 Permitted

More information

Nonlinear Programming 3rd Edition. Theoretical Solutions Manual Chapter 6

Nonlinear Programming 3rd Edition. Theoretical Solutions Manual Chapter 6 Nonlinear Programming 3rd Edition Theoretical Solutions Manual Chapter 6 Dimitri P. Bertsekas Massachusetts Institute of Technology Athena Scientific, Belmont, Massachusetts 1 NOTE This manual contains

More information

Duality Theory of Constrained Optimization

Duality Theory of Constrained Optimization Duality Theory of Constrained Optimization Robert M. Freund April, 2014 c 2014 Massachusetts Institute of Technology. All rights reserved. 1 2 1 The Practical Importance of Duality Duality is pervasive

More information

SWFR ENG 4TE3 (6TE3) COMP SCI 4TE3 (6TE3) Continuous Optimization Algorithm. Convex Optimization. Computing and Software McMaster University

SWFR ENG 4TE3 (6TE3) COMP SCI 4TE3 (6TE3) Continuous Optimization Algorithm. Convex Optimization. Computing and Software McMaster University SWFR ENG 4TE3 (6TE3) COMP SCI 4TE3 (6TE3) Continuous Optimization Algorithm Convex Optimization Computing and Software McMaster University General NLO problem (NLO : Non Linear Optimization) (N LO) min

More information

Sparse Optimization Lecture: Dual Certificate in l 1 Minimization

Sparse Optimization Lecture: Dual Certificate in l 1 Minimization Sparse Optimization Lecture: Dual Certificate in l 1 Minimization Instructor: Wotao Yin July 2013 Note scriber: Zheng Sun Those who complete this lecture will know what is a dual certificate for l 1 minimization

More information

4. Convex Sets and (Quasi-)Concave Functions

4. Convex Sets and (Quasi-)Concave Functions 4. Convex Sets and (Quasi-)Concave Functions Daisuke Oyama Mathematics II April 17, 2017 Convex Sets Definition 4.1 A R N is convex if (1 α)x + αx A whenever x, x A and α [0, 1]. A R N is strictly convex

More information

More First-Order Optimization Algorithms

More First-Order Optimization Algorithms More First-Order Optimization Algorithms Yinyu Ye Department of Management Science and Engineering Stanford University Stanford, CA 94305, U.S.A. http://www.stanford.edu/ yyye Chapters 3, 8, 3 The SDM

More information

Constrained Optimization Theory

Constrained Optimization Theory Constrained Optimization Theory Stephen J. Wright 1 2 Computer Sciences Department, University of Wisconsin-Madison. IMA, August 2016 Stephen Wright (UW-Madison) Constrained Optimization Theory IMA, August

More information

8 Barrier Methods for Constrained Optimization

8 Barrier Methods for Constrained Optimization IOE 519: NL, Winter 2012 c Marina A. Epelman 55 8 Barrier Methods for Constrained Optimization In this subsection, we will restrict our attention to instances of constrained problem () that have inequality

More information

Notes for Functional Analysis

Notes for Functional Analysis Notes for Functional Analysis Wang Zuoqin (typed by Xiyu Zhai) November 6, 2015 1 Lecture 18 1.1 The convex hull Let X be any vector space, and E X a subset. Definition 1.1. The convex hull of E is the

More information

Continuous Optimisation, Chpt 7: Proper Cones

Continuous Optimisation, Chpt 7: Proper Cones Continuous Optimisation, Chpt 7: Proper Cones Peter J.C. Dickinson DMMP, University of Twente p.j.c.dickinson@utwente.nl http://dickinson.website/teaching/2017co.html version: 10/11/17 Monday 13th November

More information

Lecture 12 Unconstrained Optimization (contd.) Constrained Optimization. October 15, 2008

Lecture 12 Unconstrained Optimization (contd.) Constrained Optimization. October 15, 2008 Lecture 12 Unconstrained Optimization (contd.) Constrained Optimization October 15, 2008 Outline Lecture 11 Gradient descent algorithm Improvement to result in Lec 11 At what rate will it converge? Constrained

More information

Some Background Math Notes on Limsups, Sets, and Convexity

Some Background Math Notes on Limsups, Sets, and Convexity EE599 STOCHASTIC NETWORK OPTIMIZATION, MICHAEL J. NEELY, FALL 2008 1 Some Background Math Notes on Limsups, Sets, and Convexity I. LIMITS Let f(t) be a real valued function of time. Suppose f(t) converges

More information

UC Berkeley Department of Electrical Engineering and Computer Science. EECS 227A Nonlinear and Convex Optimization. Solutions 5 Fall 2009

UC Berkeley Department of Electrical Engineering and Computer Science. EECS 227A Nonlinear and Convex Optimization. Solutions 5 Fall 2009 UC Berkeley Department of Electrical Engineering and Computer Science EECS 227A Nonlinear and Convex Optimization Solutions 5 Fall 2009 Reading: Boyd and Vandenberghe, Chapter 5 Solution 5.1 Note that

More information

DO NOT OPEN THIS QUESTION BOOKLET UNTIL YOU ARE TOLD TO DO SO

DO NOT OPEN THIS QUESTION BOOKLET UNTIL YOU ARE TOLD TO DO SO QUESTION BOOKLET EECS 227A Fall 2009 Midterm Tuesday, Ocotober 20, 11:10-12:30pm DO NOT OPEN THIS QUESTION BOOKLET UNTIL YOU ARE TOLD TO DO SO You have 80 minutes to complete the midterm. The midterm consists

More information

Duality. Lagrange dual problem weak and strong duality optimality conditions perturbation and sensitivity analysis generalized inequalities

Duality. Lagrange dual problem weak and strong duality optimality conditions perturbation and sensitivity analysis generalized inequalities Duality Lagrange dual problem weak and strong duality optimality conditions perturbation and sensitivity analysis generalized inequalities Lagrangian Consider the optimization problem in standard form

More information

Optimization. Yuh-Jye Lee. March 21, Data Science and Machine Intelligence Lab National Chiao Tung University 1 / 29

Optimization. Yuh-Jye Lee. March 21, Data Science and Machine Intelligence Lab National Chiao Tung University 1 / 29 Optimization Yuh-Jye Lee Data Science and Machine Intelligence Lab National Chiao Tung University March 21, 2017 1 / 29 You Have Learned (Unconstrained) Optimization in Your High School Let f (x) = ax

More information

Errata: First Printing of Mitzenmacher/Upfal Probability and Computing

Errata: First Printing of Mitzenmacher/Upfal Probability and Computing Errata: First Printing of Mitzenmacher/Upfal Probability and Computing Michael Mitzenmacher and Eli Upfal October 10, 2006 We would like to thank the many of you who have bought our book, and we would

More information

An Infeasible Interior Proximal Method for Convex Programming Problems with Linear Constraints 1

An Infeasible Interior Proximal Method for Convex Programming Problems with Linear Constraints 1 An Infeasible Interior Proximal Method for Convex Programming Problems with Linear Constraints 1 Nobuo Yamashita 2, Christian Kanzow 3, Tomoyui Morimoto 2, and Masao Fuushima 2 2 Department of Applied

More information

Lectures on Parametric Optimization: An Introduction

Lectures on Parametric Optimization: An Introduction -2 Lectures on Parametric Optimization: An Introduction Georg Still University of Twente, The Netherlands version: March 29, 2018 Contents Chapter 1. Introduction and notation 3 1.1. Introduction 3 1.2.

More information

First order optimality conditions for mathematical programs with second-order cone complementarity constraints

First order optimality conditions for mathematical programs with second-order cone complementarity constraints First order optimality conditions for mathematical programs with second-order cone complementarity constraints Jane J. Ye and Jinchuan Zhou April 9, 05 Abstract In this paper we consider a mathematical

More information

1. Introduction The nonlinear complementarity problem (NCP) is to nd a point x 2 IR n such that hx; F (x)i = ; x 2 IR n + ; F (x) 2 IRn + ; where F is

1. Introduction The nonlinear complementarity problem (NCP) is to nd a point x 2 IR n such that hx; F (x)i = ; x 2 IR n + ; F (x) 2 IRn + ; where F is New NCP-Functions and Their Properties 3 by Christian Kanzow y, Nobuo Yamashita z and Masao Fukushima z y University of Hamburg, Institute of Applied Mathematics, Bundesstrasse 55, D-2146 Hamburg, Germany,

More information

Lecture 2 - Unconstrained Optimization Definition[Global Minimum and Maximum]Let f : S R be defined on a set S R n. Then

Lecture 2 - Unconstrained Optimization Definition[Global Minimum and Maximum]Let f : S R be defined on a set S R n. Then Lecture 2 - Unconstrained Optimization Definition[Global Minimum and Maximum]Let f : S R be defined on a set S R n. Then 1. x S is a global minimum point of f over S if f (x) f (x ) for any x S. 2. x S

More information

Extreme points of compact convex sets

Extreme points of compact convex sets Extreme points of compact convex sets In this chapter, we are going to show that compact convex sets are determined by a proper subset, the set of its extreme points. Let us start with the main definition.

More information

Inequality Constraints

Inequality Constraints Chapter 2 Inequality Constraints 2.1 Optimality Conditions Early in multivariate calculus we learn the significance of differentiability in finding minimizers. In this section we begin our study of the

More information

GEORGIA INSTITUTE OF TECHNOLOGY H. MILTON STEWART SCHOOL OF INDUSTRIAL AND SYSTEMS ENGINEERING LECTURE NOTES OPTIMIZATION III

GEORGIA INSTITUTE OF TECHNOLOGY H. MILTON STEWART SCHOOL OF INDUSTRIAL AND SYSTEMS ENGINEERING LECTURE NOTES OPTIMIZATION III GEORGIA INSTITUTE OF TECHNOLOGY H. MILTON STEWART SCHOOL OF INDUSTRIAL AND SYSTEMS ENGINEERING LECTURE NOTES OPTIMIZATION III CONVEX ANALYSIS NONLINEAR PROGRAMMING THEORY NONLINEAR PROGRAMMING ALGORITHMS

More information

SOME STABILITY RESULTS FOR THE SEMI-AFFINE VARIATIONAL INEQUALITY PROBLEM. 1. Introduction

SOME STABILITY RESULTS FOR THE SEMI-AFFINE VARIATIONAL INEQUALITY PROBLEM. 1. Introduction ACTA MATHEMATICA VIETNAMICA 271 Volume 29, Number 3, 2004, pp. 271-280 SOME STABILITY RESULTS FOR THE SEMI-AFFINE VARIATIONAL INEQUALITY PROBLEM NGUYEN NANG TAM Abstract. This paper establishes two theorems

More information

Lecture: Duality.

Lecture: Duality. Lecture: Duality http://bicmr.pku.edu.cn/~wenzw/opt-2016-fall.html Acknowledgement: this slides is based on Prof. Lieven Vandenberghe s lecture notes Introduction 2/35 Lagrange dual problem weak and strong

More information

Lagrangian Duality Theory

Lagrangian Duality Theory Lagrangian Duality Theory Yinyu Ye Department of Management Science and Engineering Stanford University Stanford, CA 94305, U.S.A. http://www.stanford.edu/ yyye Chapter 14.1-4 1 Recall Primal and Dual

More information

AN INTERIOR-POINT METHOD FOR NONLINEAR OPTIMIZATION PROBLEMS WITH LOCATABLE AND SEPARABLE NONSMOOTHNESS

AN INTERIOR-POINT METHOD FOR NONLINEAR OPTIMIZATION PROBLEMS WITH LOCATABLE AND SEPARABLE NONSMOOTHNESS AN INTERIOR-POINT METHOD FOR NONLINEAR OPTIMIZATION PROBLEMS WITH LOCATABLE AND SEPARABLE NONSMOOTHNESS MARTIN SCHMIDT Abstract. Many real-world optimization models comse nonconvex and nonlinear as well

More information

3.10 Lagrangian relaxation

3.10 Lagrangian relaxation 3.10 Lagrangian relaxation Consider a generic ILP problem min {c t x : Ax b, Dx d, x Z n } with integer coefficients. Suppose Dx d are the complicating constraints. Often the linear relaxation and the

More information

Optimization. Yuh-Jye Lee. March 28, Data Science and Machine Intelligence Lab National Chiao Tung University 1 / 40

Optimization. Yuh-Jye Lee. March 28, Data Science and Machine Intelligence Lab National Chiao Tung University 1 / 40 Optimization Yuh-Jye Lee Data Science and Machine Intelligence Lab National Chiao Tung University March 28, 2017 1 / 40 The Key Idea of Newton s Method Let f : R n R be a twice differentiable function

More information

Design and Analysis of Algorithms Lecture Notes on Convex Optimization CS 6820, Fall Nov 2 Dec 2016

Design and Analysis of Algorithms Lecture Notes on Convex Optimization CS 6820, Fall Nov 2 Dec 2016 Design and Analysis of Algorithms Lecture Notes on Convex Optimization CS 6820, Fall 206 2 Nov 2 Dec 206 Let D be a convex subset of R n. A function f : D R is convex if it satisfies f(tx + ( t)y) tf(x)

More information

Lecture 3: Functions of Symmetric Matrices

Lecture 3: Functions of Symmetric Matrices Lecture 3: Functions of Symmetric Matrices Yilin Mo July 2, 2015 1 Recap 1 Bayes Estimator: (a Initialization: (b Correction: f(x 0 Y 1 = f(x 0 f(x k Y k = αf(y k x k f(x k Y k 1, where ( 1 α = f(y k x

More information

Nonlinear Optimization

Nonlinear Optimization Nonlinear Optimization Etienne de Klerk (UvT)/Kees Roos e-mail: C.Roos@ewi.tudelft.nl URL: http://www.isa.ewi.tudelft.nl/ roos Course WI3031 (Week 4) February-March, A.D. 2005 Optimization Group 1 Outline

More information

Applied Mathematics Letters

Applied Mathematics Letters Applied Mathematics Letters 25 (2012) 974 979 Contents lists available at SciVerse ScienceDirect Applied Mathematics Letters journal homepage: www.elsevier.com/locate/aml On dual vector equilibrium problems

More information

A Continuation Method for the Solution of Monotone Variational Inequality Problems

A Continuation Method for the Solution of Monotone Variational Inequality Problems A Continuation Method for the Solution of Monotone Variational Inequality Problems Christian Kanzow Institute of Applied Mathematics University of Hamburg Bundesstrasse 55 D 20146 Hamburg Germany e-mail:

More information

First-order optimality conditions for mathematical programs with second-order cone complementarity constraints

First-order optimality conditions for mathematical programs with second-order cone complementarity constraints First-order optimality conditions for mathematical programs with second-order cone complementarity constraints Jane J. Ye Jinchuan Zhou Abstract In this paper we consider a mathematical program with second-order

More information

List of Errata for the Book A Mathematical Introduction to Compressive Sensing

List of Errata for the Book A Mathematical Introduction to Compressive Sensing List of Errata for the Book A Mathematical Introduction to Compressive Sensing Simon Foucart and Holger Rauhut This list was last updated on September 22, 2018. If you see further errors, please send us

More information

On sequential optimality conditions for constrained optimization. José Mario Martínez martinez

On sequential optimality conditions for constrained optimization. José Mario Martínez  martinez On sequential optimality conditions for constrained optimization José Mario Martínez www.ime.unicamp.br/ martinez UNICAMP, Brazil 2011 Collaborators This talk is based in joint papers with Roberto Andreani

More information

Lectures 9 and 10: Constrained optimization problems and their optimality conditions

Lectures 9 and 10: Constrained optimization problems and their optimality conditions Lectures 9 and 10: Constrained optimization problems and their optimality conditions Coralia Cartis, Mathematical Institute, University of Oxford C6.2/B2: Continuous Optimization Lectures 9 and 10: Constrained

More information

Lecture: Duality of LP, SOCP and SDP

Lecture: Duality of LP, SOCP and SDP 1/33 Lecture: Duality of LP, SOCP and SDP Zaiwen Wen Beijing International Center For Mathematical Research Peking University http://bicmr.pku.edu.cn/~wenzw/bigdata2017.html wenzw@pku.edu.cn Acknowledgement:

More information

4TE3/6TE3. Algorithms for. Continuous Optimization

4TE3/6TE3. Algorithms for. Continuous Optimization 4TE3/6TE3 Algorithms for Continuous Optimization (Duality in Nonlinear Optimization ) Tamás TERLAKY Computing and Software McMaster University Hamilton, January 2004 terlaky@mcmaster.ca Tel: 27780 Optimality

More information

AN AUGMENTED LAGRANGIAN AFFINE SCALING METHOD FOR NONLINEAR PROGRAMMING

AN AUGMENTED LAGRANGIAN AFFINE SCALING METHOD FOR NONLINEAR PROGRAMMING AN AUGMENTED LAGRANGIAN AFFINE SCALING METHOD FOR NONLINEAR PROGRAMMING XIAO WANG AND HONGCHAO ZHANG Abstract. In this paper, we propose an Augmented Lagrangian Affine Scaling (ALAS) algorithm for general

More information

Outline. Roadmap for the NPP segment: 1 Preliminaries: role of convexity. 2 Existence of a solution

Outline. Roadmap for the NPP segment: 1 Preliminaries: role of convexity. 2 Existence of a solution Outline Roadmap for the NPP segment: 1 Preliminaries: role of convexity 2 Existence of a solution 3 Necessary conditions for a solution: inequality constraints 4 The constraint qualification 5 The Lagrangian

More information

Coordinate Update Algorithm Short Course Subgradients and Subgradient Methods

Coordinate Update Algorithm Short Course Subgradients and Subgradient Methods Coordinate Update Algorithm Short Course Subgradients and Subgradient Methods Instructor: Wotao Yin (UCLA Math) Summer 2016 1 / 30 Notation f : H R { } is a closed proper convex function domf := {x R n

More information

2.098/6.255/ Optimization Methods Practice True/False Questions

2.098/6.255/ Optimization Methods Practice True/False Questions 2.098/6.255/15.093 Optimization Methods Practice True/False Questions December 11, 2009 Part I For each one of the statements below, state whether it is true or false. Include a 1-3 line supporting sentence

More information

Algorithms for Constrained Optimization

Algorithms for Constrained Optimization 1 / 42 Algorithms for Constrained Optimization ME598/494 Lecture Max Yi Ren Department of Mechanical Engineering, Arizona State University April 19, 2015 2 / 42 Outline 1. Convergence 2. Sequential quadratic

More information

1. Introduction. We consider the following constrained optimization problem:

1. Introduction. We consider the following constrained optimization problem: SIAM J. OPTIM. Vol. 26, No. 3, pp. 1465 1492 c 2016 Society for Industrial and Applied Mathematics PENALTY METHODS FOR A CLASS OF NON-LIPSCHITZ OPTIMIZATION PROBLEMS XIAOJUN CHEN, ZHAOSONG LU, AND TING

More information

5.6 Penalty method and augmented Lagrangian method

5.6 Penalty method and augmented Lagrangian method 5.6 Penalty method and augmented Lagrangian method Consider a generic NLP problem min f (x) s.t. c i (x) 0 i I c i (x) = 0 i E (1) x R n where f and the c i s are of class C 1 or C 2, and I and E are the

More information

FIRST- AND SECOND-ORDER OPTIMALITY CONDITIONS FOR MATHEMATICAL PROGRAMS WITH VANISHING CONSTRAINTS 1. Tim Hoheisel and Christian Kanzow

FIRST- AND SECOND-ORDER OPTIMALITY CONDITIONS FOR MATHEMATICAL PROGRAMS WITH VANISHING CONSTRAINTS 1. Tim Hoheisel and Christian Kanzow FIRST- AND SECOND-ORDER OPTIMALITY CONDITIONS FOR MATHEMATICAL PROGRAMS WITH VANISHING CONSTRAINTS 1 Tim Hoheisel and Christian Kanzow Dedicated to Jiří Outrata on the occasion of his 60th birthday Preprint

More information

ON GENERALIZED-CONVEX CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION

ON GENERALIZED-CONVEX CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION ON GENERALIZED-CONVEX CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION CHRISTIAN GÜNTHER AND CHRISTIANE TAMMER Abstract. In this paper, we consider multi-objective optimization problems involving not necessarily

More information

SECTION C: CONTINUOUS OPTIMISATION LECTURE 11: THE METHOD OF LAGRANGE MULTIPLIERS

SECTION C: CONTINUOUS OPTIMISATION LECTURE 11: THE METHOD OF LAGRANGE MULTIPLIERS SECTION C: CONTINUOUS OPTIMISATION LECTURE : THE METHOD OF LAGRANGE MULTIPLIERS HONOUR SCHOOL OF MATHEMATICS OXFORD UNIVERSITY HILARY TERM 005 DR RAPHAEL HAUSER. Examples. In this lecture we will take

More information

Technische Universität Ilmenau Institut für Mathematik

Technische Universität Ilmenau Institut für Mathematik Technische Universität Ilmenau Institut für Mathematik Preprint No. M 18/03 A branch-and-bound based algorithm for nonconvex multiobjective optimization Julia Niebling, Gabriele Eichfelder Februar 018

More information

6. Proximal gradient method

6. Proximal gradient method L. Vandenberghe EE236C (Spring 2013-14) 6. Proximal gradient method motivation proximal mapping proximal gradient method with fixed step size proximal gradient method with line search 6-1 Proximal mapping

More information

Enhanced Fritz John Optimality Conditions and Sensitivity Analysis

Enhanced Fritz John Optimality Conditions and Sensitivity Analysis Enhanced Fritz John Optimality Conditions and Sensitivity Analysis Dimitri P. Bertsekas Laboratory for Information and Decision Systems Massachusetts Institute of Technology March 2016 1 / 27 Constrained

More information

Fast proximal gradient methods

Fast proximal gradient methods L. Vandenberghe EE236C (Spring 2013-14) Fast proximal gradient methods fast proximal gradient method (FISTA) FISTA with line search FISTA as descent method Nesterov s second method 1 Fast (proximal) gradient

More information

Subgradients. subgradients and quasigradients. subgradient calculus. optimality conditions via subgradients. directional derivatives

Subgradients. subgradients and quasigradients. subgradient calculus. optimality conditions via subgradients. directional derivatives Subgradients subgradients and quasigradients subgradient calculus optimality conditions via subgradients directional derivatives Prof. S. Boyd, EE392o, Stanford University Basic inequality recall basic

More information