Semidefinite Programming, Combinatorial Optimization and Real Algebraic Geometry
|
|
- Jeffrey Cummings
- 6 years ago
- Views:
Transcription
1 Semidefinite Programming, Combinatorial Optimization and Real Algebraic Geometry assoc. prof., Ph.D. 1 1 UNM - Faculty of information studies Edinburgh, 16. September 2014
2 Outline Introduction Definition of SDP Dual theory
3 Definition of SDP Dual theory Some notation I - unit matrix, X R m n x = vec(x ) R mn x, y = x T y = i x iy i A, B = trace(a T B) = i,j a ijb ij
4 Definition of SDP Dual theory Some notation Symmetric matrices S n := {X R n n : X T = X } Cone of positive semidefinite matrices S n + := {X S n : u T Xu 0, u R n } - closed convex pointed cone Positive definite matrices S ++ n := {X S n : u T Xu > 0, u R n } The dual cone: (S + n ) = {Y S n : Y, X 0, X S + n } = S + n (Note: inf X 0 X, Y = 0 Y 0) For X S + n we use X 0.
5 Definition of SDP Dual theory Few properties of PSD matrices For A S n the following are equivalent A S n +, all eigenvalues of A are nonnegative real numbers, there exist P and D = Diag(d), d 0, such that A = PDP T, det AII 0 for every main submatrix A II (A II is main submatrix, if A = [a ij ], for i, j I {1,..., n}). BAB T S n +, for some non-singular B.
6 Definition of SDP Dual theory Few properties of PSD matrices For X, Y S + n : X, Y 0 and X, Y = 0 XY = 0. Schur s complement: if A S n ++ then [ ] A B B t 0 C B t A 1 B 0. C
7 Definition of SDP Dual theory Primal and the dual SDP Primal semidefinite programming problem (PSDP) inf C, X s. t. A i, X = b i, i, X S n + Dual semidefinite programming problem (DSDP) s. t. sup b T y i y ia i + Z = C Z S n + PSDP and DSDP are dual of each other.
8 Definition of SDP Dual theory Example 1: linear programming (LP) Linear programming problem (LP) in standard primal form min c T x p. p. Ax = b x 0 LP as PSDP: C = Diag(c), X = Diag(x), A i = Diag(A(i, :)) min C, X p. p. A i, X = b i, 1 i m, X S n +
9 Definition of SDP Dual theory Example 2: convex quadratic programming Def. Convex quadratic programming problem (CCP): Rem. inf f 0(x) p. p. f i (x) 0, i = 1,..., m. (CCP) where: f i (x) = x t U i x v t i x z i, U i 0. Note f i (x) 0 A i = [ ] I U 1/2 i x (U 1/2 i x) t vi t x + z i 0. [ [ [ ] I 0 0 U A i = ]+x 1/2 i (:, 1) 0 U 0 z 1 ]+ 1/2 i U 1/2 i (:, n) +x n i (1, :) v i1 U 1/2. i (n, :) v in
10 Definition of SDP Dual theory Example 2: convex quadratic programming cnt. Finding min of f 0 (x) is equiv. to finding min. of t with additional constraint f 0 (x) t. (CCP) is equivalent to: inf t p. p. Diag(A 0, A 1,..., A m ) 0, where [ ] I U 1/2 A 0 = 0 x (U 1/2 0 x) t v0 tx + z. 0 + t
11 Definition of SDP Dual theory Weak duality Let us define A i, X = b i =: A(X ) = b y i A i =: A T (y) i OPT P = inf { C, X ; A(X ) = b, X 0} and OPT D = sup {b t y ; A t (y) + Z = C, Z 0, y R m }. More definitions sup =, inf =. Thm.: OPT P OPT D. Proof: Duality gap: OPT P OPT D = C, X opt b T y opt = X opt, Z opt.
12 Definition of SDP Dual theory Strong duality Def.: PSDP is strictly feasible, if there exists X S ++ n A(X ) = b. DSDP is strictly feasible, if there exists pair (y, Z) R m S ++ n such that A T (y) + Z = C. Thm.: Let (DSDP) be strictly feasible. Then OPTP = OPT D. such that If OPT P < then there exists X 0 s.t. A(X ) = b and C, X = OPT P.
13 Definition of SDP Dual theory Example 3: optimum is not attained min [ ] 1 0, X 0 0 [ ] 0 1 p. p., X = 2, X [ ] x11 1 Feasible solutions: X = with x 1 x 22 > 0 in 22 x 11 x OPT P = 0, but OPT P is not attained. Interpretation : DSDP has no strictly feasible solution.
14 Definition of SDP Dual theory Example 4: positive duality gap min p. p , X , X = 0, , X = 2, X OPT P = 1, OPT D = 0.
15 Definition of SDP Dual theory Optimal conditions for SDP Let PSDP and DSDP be strictly feasible. Thm.: X in (y, Z ) are optimal for PSDP and DSDP if and only if: (prim. dop.) A(X ) = b, X 0 (dual. dop.) A T (y) + Z = C, Z 0 (zero duality gap) XZ = 0 (b T y = C, X )
16 Central path Assumptions eqs. Ai, X = b i linearly independant PSDP and DSDP strictly feasible Then the system (prim. dop.) A(X ) = b, X 0 (dual. dop.) A T (y) + Z = C, Z 0 XZ = µi µ > 0 has a unique solution (X µ, y µ, Z µ ). Def.: The central path: {(X µ, y µ, Z µ ): µ > 0}.
17 Example PSDP min p. p. [ ] 1 0, X 0 1 [ ] 0 1, X 1 0 = 2, X 0. DSDP max 2y p. p. Z = [ 1 ] y y 1 0.
18 The central path (primal part)
19 The properties of the central path Thm: The central path is a smooth curve parameterized by µ. Thm: The central path always converge lim µ 0 (X µ, y µ, Z µ ) = (X, y, Z ). Limit point is maximally complementary primal dual optimal solution. Def: Let (X, y, Z ) be primal dual optimal solution. It is maximally complementary if rank(x ) is maximal among all primal optimal solutions and rank(y ) is maximal among all dual optimal solutions. Proof: See e.g. H. Wolkowicz, R. Saigal, L. Vanderberghe (ed.): Handbook of Semidefinite Programming. Kluwer Academic Publishers, Boston-Dordrecht-London, 2000.
20 Path following IPMs Idea: We solve approximately the equations defining the central path (we follow the central path approximately).
21 Path following IPMs - more precisely Input: A, b R m, C S n, ε > 0, σ (0, 1) and (X 0, y 0, Z 0 ) strictly feasible for PSDP in DSDP 1. Set k := Repeat 2.1 µ k = X k, Z k /n 2.1 Solve A(X k + X ) = b, A T (y k + y) + Z + Z = C, (X k + X )(Z k + Z) = σµ k I. 2.2 X = ( X + ( X ) T )/ (X k+1, y k+1, Z k+1 ) := (X k + X, y k + y, Z k + Z). 2.4 k := k until X k+1, Z k+1 ε. Output: (X k, y k, Z k ).
22 Solving the system Note that the starting point is strictly feasible. A( X ) = 0, A T ( y) + Z = 0, XZ k + X k Z = µ k I X k Z k, FIRSTLY: Z = A T ( y) X = ( X + ( X ) T )/2, THEN: X = (µi X k Z k X k Z)Z 1 k FINALLY: A(X k A T ( y)z 1 k ) = A(µZ 1 k )+A(X k ) = b A(µZ 1 k ).
23 Solving the system: bottlenecks On each step we have to - compute one inverse (Z 1 k ) - compose the system matrix M y = b. Each m ij = A i, XA j Z 1 k takes O(m 2 n 2 + mn 3 ) flops. - Solve the system M y = b (takes O(m 3 ) flops) - Compute Z in X (takes O(mn 2 + n 3 ) flops)
24 Theoretical guaranty Thm.: Let (X 0, y 0, Z 0 ) be strictly feasible starting point with Z 1/2 XZ 1/2 µi θ X, Z. The described path following IPM gives ε-optimal solution in at most n/δ log(ε 1 X 0, S 0 ) iterations, where δ = n(1 σ). (1+θ) 2 1 (θ 2 + n(1 σ) 2 ) σθ. 2(1 θ) 3 2
25 (Povh, Rendl, Wiegele, 2005) Relaxed optimality conditions (primal feas.) A(X ) b, X 0 (dual feas.) A T (y) + Z C, Z 0 (zero opt. dual. gap) XZ = 0
26 (Povh, Rendl, Wiegele, 2005) Relaxed optimality conditions (primal feas.) A(X ) b, X 0 (dual feas.) A T (y) + Z C, Z 0 (zero opt. dual. gap) XZ = 0
27 Idea behind the Augmented Lagrangian approach to DSDP sup{b T y : A T y + Z = C, Z S n + } Replace DSDP by DSDP-L: min {b T y + X, C A T (y) Z + σ 2 C AT (y) Z 2 : Z 0}
28 BPM: algorithm INPUT: A, b, C S n. 1. Select σ > 0, X k = Repeat 3.1 Solve DSDP-L approximately to get y k and Z k Update X k+1 := X k + σ(z k C + A T (y k )) 3.3 k := k Until: stopping criteria is satisfied. OUTPUT: X k, y k, Z k. Efficient idea: alternating y and Z.
29 in praxis - we use software packages Nekaj najbolj razširjenih in robustnih paketa - SEDUMI ( - SDPT3 ( mattohkc/sdpt3.html). - SDPA ( - MOSEK ( - YALMIP ( To solve large SDP (m is large) we can use: - SDPLR ( burer/software/sdplr/) - spectral bundle method ( helmberg/sbmethod/). - boundary point method (
30 Literature 1. Miguel F. Anjos and Jean B. Lasserre. Handbook of Semidefinite, Conic and Polynomial Optimization: Theory, Algorithms, Software and Applications, volume 166 of International Series in Operational Research and Management Science. Springer, E. de Klerk: Aspects of Semidefinite Programming - Interior Point Algorithms and Selected Applications. Kluwer Academic Publishers, Dordrecht, M. Laurent. Sums of squares, moment matrices and optimization over polynomials, volume 149 of The IMA Volumes in Mathematics and its Applications, str Springer, J. Povh: Semidefinitno programiranje in kombinatorična optimizacija: magistrsko delo. Ljubljana, J. Povh: Semidefinitno programiranje. Obz. mat. fiz., 2002, letn. 49, št. 6, str H. Wolkowicz, R. Saigal, L. Vanderberghe (ed.): Handbook of Semidefinite Programming. Kluwer Academic Publishers, Boston-Dordrecht-London, 2000.
arxiv: v1 [math.oc] 26 Sep 2015
arxiv:1509.08021v1 [math.oc] 26 Sep 2015 Degeneracy in Maximal Clique Decomposition for Semidefinite Programs Arvind U. Raghunathan and Andrew V. Knyazev Mitsubishi Electric Research Laboratories 201 Broadway,
More informationSolving large Semidefinite Programs - Part 1 and 2
Solving large Semidefinite Programs - Part 1 and 2 Franz Rendl http://www.math.uni-klu.ac.at Alpen-Adria-Universität Klagenfurt Austria F. Rendl, Singapore workshop 2006 p.1/34 Overview Limits of Interior
More informationSecond-order cone programming
Outline Second-order cone programming, PhD Lehigh University Department of Industrial and Systems Engineering February 10, 2009 Outline 1 Basic properties Spectral decomposition The cone of squares The
More informationLecture: Introduction to LP, SDP and SOCP
Lecture: Introduction to LP, SDP and SOCP Zaiwen Wen Beijing International Center For Mathematical Research Peking University http://bicmr.pku.edu.cn/~wenzw/bigdata2015.html wenzw@pku.edu.cn Acknowledgement:
More informationIII. Applications in convex optimization
III. Applications in convex optimization nonsymmetric interior-point methods partial separability and decomposition partial separability first order methods interior-point methods Conic linear optimization
More informationProjection methods to solve SDP
Projection methods to solve SDP Franz Rendl http://www.math.uni-klu.ac.at Alpen-Adria-Universität Klagenfurt Austria F. Rendl, Oberwolfach Seminar, May 2010 p.1/32 Overview Augmented Primal-Dual Method
More informationE5295/5B5749 Convex optimization with engineering applications. Lecture 5. Convex programming and semidefinite programming
E5295/5B5749 Convex optimization with engineering applications Lecture 5 Convex programming and semidefinite programming A. Forsgren, KTH 1 Lecture 5 Convex optimization 2006/2007 Convex quadratic program
More information4. Algebra and Duality
4-1 Algebra and Duality P. Parrilo and S. Lall, CDC 2003 2003.12.07.01 4. Algebra and Duality Example: non-convex polynomial optimization Weak duality and duality gap The dual is not intrinsic The cone
More informationLecture 14: Optimality Conditions for Conic Problems
EE 227A: Conve Optimization and Applications March 6, 2012 Lecture 14: Optimality Conditions for Conic Problems Lecturer: Laurent El Ghaoui Reading assignment: 5.5 of BV. 14.1 Optimality for Conic Problems
More informationDegeneracy in Maximal Clique Decomposition for Semidefinite Programs
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Degeneracy in Maximal Clique Decomposition for Semidefinite Programs Raghunathan, A.U.; Knyazev, A. TR2016-040 July 2016 Abstract Exploiting
More informationSemidefinite Programming
Semidefinite Programming Basics and SOS Fernando Mário de Oliveira Filho Campos do Jordão, 2 November 23 Available at: www.ime.usp.br/~fmario under talks Conic programming V is a real vector space h, i
More informationContinuous Optimisation, Chpt 9: Semidefinite Optimisation
Continuous Optimisation, Chpt 9: Semidefinite Optimisation Peter J.C. Dickinson DMMP, University of Twente p.j.c.dickinson@utwente.nl http://dickinson.website/teaching/2017co.html version: 28/11/17 Monday
More informationIntroduction to Semidefinite Programming I: Basic properties a
Introduction to Semidefinite Programming I: Basic properties and variations on the Goemans-Williamson approximation algorithm for max-cut MFO seminar on Semidefinite Programming May 30, 2010 Semidefinite
More informationLecture: Algorithms for LP, SOCP and SDP
1/53 Lecture: Algorithms for LP, SOCP and SDP Zaiwen Wen Beijing International Center For Mathematical Research Peking University http://bicmr.pku.edu.cn/~wenzw/bigdata2018.html wenzw@pku.edu.cn Acknowledgement:
More informationSemidefinite Programming Basics and Applications
Semidefinite Programming Basics and Applications Ray Pörn, principal lecturer Åbo Akademi University Novia University of Applied Sciences Content What is semidefinite programming (SDP)? How to represent
More information12. Interior-point methods
12. Interior-point methods Convex Optimization Boyd & Vandenberghe inequality constrained minimization logarithmic barrier function and central path barrier method feasibility and phase I methods complexity
More informationResearch Note. A New Infeasible Interior-Point Algorithm with Full Nesterov-Todd Step for Semi-Definite Optimization
Iranian Journal of Operations Research Vol. 4, No. 1, 2013, pp. 88-107 Research Note A New Infeasible Interior-Point Algorithm with Full Nesterov-Todd Step for Semi-Definite Optimization B. Kheirfam We
More informationSummer School: Semidefinite Optimization
Summer School: Semidefinite Optimization Christine Bachoc Université Bordeaux I, IMB Research Training Group Experimental and Constructive Algebra Haus Karrenberg, Sept. 3 - Sept. 7, 2012 Duality Theory
More informationm i=1 c ix i i=1 F ix i F 0, X O.
What is SDP? for a beginner of SDP Copyright C 2005 SDPA Project 1 Introduction This note is a short course for SemiDefinite Programming s SDP beginners. SDP has various applications in, for example, control
More informationA semidefinite relaxation scheme for quadratically constrained quadratic problems with an additional linear constraint
Iranian Journal of Operations Research Vol. 2, No. 2, 20, pp. 29-34 A semidefinite relaxation scheme for quadratically constrained quadratic problems with an additional linear constraint M. Salahi Semidefinite
More informationA CONIC DANTZIG-WOLFE DECOMPOSITION APPROACH FOR LARGE SCALE SEMIDEFINITE PROGRAMMING
A CONIC DANTZIG-WOLFE DECOMPOSITION APPROACH FOR LARGE SCALE SEMIDEFINITE PROGRAMMING Kartik Krishnan Advanced Optimization Laboratory McMaster University Joint work with Gema Plaza Martinez and Tamás
More information15. Conic optimization
L. Vandenberghe EE236C (Spring 216) 15. Conic optimization conic linear program examples modeling duality 15-1 Generalized (conic) inequalities Conic inequality: a constraint x K where K is a convex cone
More informationCroatian Operational Research Review (CRORR), Vol. 3, 2012
126 127 128 129 130 131 132 133 REFERENCES [BM03] S. Burer and R.D.C. Monteiro (2003), A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization, Mathematical Programming
More informationLinear and non-linear programming
Linear and non-linear programming Benjamin Recht March 11, 2005 The Gameplan Constrained Optimization Convexity Duality Applications/Taxonomy 1 Constrained Optimization minimize f(x) subject to g j (x)
More informationLecture 6: Conic Optimization September 8
IE 598: Big Data Optimization Fall 2016 Lecture 6: Conic Optimization September 8 Lecturer: Niao He Scriber: Juan Xu Overview In this lecture, we finish up our previous discussion on optimality conditions
More informationSemidefinite Programming
Semidefinite Programming Notes by Bernd Sturmfels for the lecture on June 26, 208, in the IMPRS Ringvorlesung Introduction to Nonlinear Algebra The transition from linear algebra to nonlinear algebra has
More informationCSC Linear Programming and Combinatorial Optimization Lecture 10: Semidefinite Programming
CSC2411 - Linear Programming and Combinatorial Optimization Lecture 10: Semidefinite Programming Notes taken by Mike Jamieson March 28, 2005 Summary: In this lecture, we introduce semidefinite programming
More informationThe Ongoing Development of CSDP
The Ongoing Development of CSDP Brian Borchers Department of Mathematics New Mexico Tech Socorro, NM 87801 borchers@nmt.edu Joseph Young Department of Mathematics New Mexico Tech (Now at Rice University)
More informationEE 227A: Convex Optimization and Applications October 14, 2008
EE 227A: Convex Optimization and Applications October 14, 2008 Lecture 13: SDP Duality Lecturer: Laurent El Ghaoui Reading assignment: Chapter 5 of BV. 13.1 Direct approach 13.1.1 Primal problem Consider
More informationContinuous Optimisation, Chpt 9: Semidefinite Problems
Continuous Optimisation, Chpt 9: Semidefinite Problems Peter J.C. Dickinson DMMP, University of Twente p.j.c.dickinson@utwente.nl http://dickinson.website/teaching/2016co.html version: 21/11/16 Monday
More informationPOLYNOMIAL OPTIMIZATION WITH SUMS-OF-SQUARES INTERPOLANTS
POLYNOMIAL OPTIMIZATION WITH SUMS-OF-SQUARES INTERPOLANTS Sercan Yıldız syildiz@samsi.info in collaboration with Dávid Papp (NCSU) OPT Transition Workshop May 02, 2017 OUTLINE Polynomial optimization and
More informationI.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 informationModeling with semidefinite and copositive matrices
Modeling with semidefinite and copositive matrices Franz Rendl http://www.math.uni-klu.ac.at Alpen-Adria-Universität Klagenfurt Austria F. Rendl, Singapore workshop 2006 p.1/24 Overview Node and Edge relaxations
More informationExample: feasibility. Interpretation as formal proof. Example: linear inequalities and Farkas lemma
4-1 Algebra and Duality P. Parrilo and S. Lall 2006.06.07.01 4. Algebra and Duality Example: non-convex polynomial optimization Weak duality and duality gap The dual is not intrinsic The cone of valid
More informationCopositive Programming and Combinatorial Optimization
Copositive Programming and Combinatorial Optimization Franz Rendl http://www.math.uni-klu.ac.at Alpen-Adria-Universität Klagenfurt Austria joint work with I.M. Bomze (Wien) and F. Jarre (Düsseldorf) IMA
More informationStrong duality in Lasserre s hierarchy for polynomial optimization
Strong duality in Lasserre s hierarchy for polynomial optimization arxiv:1405.7334v1 [math.oc] 28 May 2014 Cédric Josz 1,2, Didier Henrion 3,4,5 Draft of January 24, 2018 Abstract A polynomial optimization
More informationLecture 5. The Dual Cone and Dual Problem
IE 8534 1 Lecture 5. The Dual Cone and Dual Problem IE 8534 2 For a convex cone K, its dual cone is defined as K = {y x, y 0, x K}. The inner-product can be replaced by x T y if the coordinates of the
More informationInterior Point Methods: Second-Order Cone Programming and Semidefinite Programming
School of Mathematics T H E U N I V E R S I T Y O H F E D I N B U R G Interior Point Methods: Second-Order Cone Programming and Semidefinite Programming Jacek Gondzio Email: J.Gondzio@ed.ac.uk URL: http://www.maths.ed.ac.uk/~gondzio
More informationThe Q Method for Symmetric Cone Programmin
The Q Method for Symmetric Cone Programming The Q Method for Symmetric Cone Programmin Farid Alizadeh and Yu Xia alizadeh@rutcor.rutgers.edu, xiay@optlab.mcma Large Scale Nonlinear and Semidefinite Progra
More informationEigenvalue Optimization for Solving the
Ecole Polytechnique Fédérale de Lausanne Master of Mathematical Sciences Semester project Prof. Daniel Kressner Supervisor: Petar Sirkovic Eigenvalue Optimization for Solving the MAX-CUT Problem Julien
More informationLecture 8. Strong Duality Results. September 22, 2008
Strong Duality Results September 22, 2008 Outline Lecture 8 Slater Condition and its Variations Convex Objective with Linear Inequality Constraints Quadratic Objective over Quadratic Constraints Representation
More informationConic Linear Optimization and its Dual. yyye
Conic Linear Optimization and Appl. MS&E314 Lecture Note #04 1 Conic Linear Optimization and its Dual Yinyu Ye Department of Management Science and Engineering Stanford University Stanford, CA 94305, U.S.A.
More informationSparse Matrix Theory and Semidefinite Optimization
Sparse Matrix Theory and Semidefinite Optimization Lieven Vandenberghe Department of Electrical Engineering University of California, Los Angeles Joint work with Martin S. Andersen and Yifan Sun Third
More informationCopositive Programming and Combinatorial Optimization
Copositive Programming and Combinatorial Optimization Franz Rendl http://www.math.uni-klu.ac.at Alpen-Adria-Universität Klagenfurt Austria joint work with M. Bomze (Wien) and F. Jarre (Düsseldorf) and
More informationApplications of semidefinite programming, symmetry and algebra to graph partitioning problems
Applications of semidefinite programming, symmetry and algebra to graph partitioning problems Edwin van Dam and Renata Sotirov Tilburg University, The Netherlands Summer 2018 Edwin van Dam (Tilburg) SDP,
More informationLecture 17: Primal-dual interior-point methods part II
10-725/36-725: Convex Optimization Spring 2015 Lecture 17: Primal-dual interior-point methods part II Lecturer: Javier Pena Scribes: Pinchao Zhang, Wei Ma Note: LaTeX template courtesy of UC Berkeley EECS
More informationIntroduction to Semidefinite Programs
Introduction to Semidefinite Programs Masakazu Kojima, Tokyo Institute of Technology Semidefinite Programming and Its Application January, 2006 Institute for Mathematical Sciences National University of
More informationApproximate Farkas Lemmas in Convex Optimization
Approximate Farkas Lemmas in Convex Optimization Imre McMaster University Advanced Optimization Lab AdvOL Graduate Student Seminar October 25, 2004 1 Exact Farkas Lemma Motivation 2 3 Future plans The
More informationLecture Note 5: Semidefinite Programming for Stability Analysis
ECE7850: Hybrid Systems:Theory and Applications Lecture Note 5: Semidefinite Programming for Stability Analysis Wei Zhang Assistant Professor Department of Electrical and Computer Engineering Ohio State
More informationInterior Point Algorithms for Constrained Convex Optimization
Interior Point Algorithms for Constrained Convex Optimization Chee Wei Tan CS 8292 : Advanced Topics in Convex Optimization and its Applications Fall 2010 Outline Inequality constrained minimization problems
More informationUniqueness of the Solutions of Some Completion Problems
Uniqueness of the Solutions of Some Completion Problems Chi-Kwong Li and Tom Milligan Abstract We determine the conditions for uniqueness of the solutions of several completion problems including the positive
More information14. Duality. ˆ Upper and lower bounds. ˆ General duality. ˆ Constraint qualifications. ˆ Counterexample. ˆ Complementary slackness.
CS/ECE/ISyE 524 Introduction to Optimization Spring 2016 17 14. Duality ˆ Upper and lower bounds ˆ General duality ˆ Constraint qualifications ˆ Counterexample ˆ Complementary slackness ˆ Examples ˆ Sensitivity
More informationReal Symmetric Matrices and Semidefinite Programming
Real Symmetric Matrices and Semidefinite Programming Tatsiana Maskalevich Abstract Symmetric real matrices attain an important property stating that all their eigenvalues are real. This gives rise to many
More informationInfeasible Primal-Dual (Path-Following) Interior-Point Methods for Semidefinite Programming*
Infeasible Primal-Dual (Path-Following) Interior-Point Methods for Semidefinite Programming* Yin Zhang Dept of CAAM, Rice University Outline (1) Introduction (2) Formulation & a complexity theorem (3)
More informationA solution approach for linear optimization with completely positive matrices
A solution approach for linear optimization with completely positive matrices Franz Rendl http://www.math.uni-klu.ac.at Alpen-Adria-Universität Klagenfurt Austria joint work with M. Bomze (Wien) and F.
More informationSEMIDEFINITE PROGRAM BASICS. Contents
SEMIDEFINITE PROGRAM BASICS BRIAN AXELROD Abstract. A introduction to the basics of Semidefinite programs. Contents 1. Definitions and Preliminaries 1 1.1. Linear Algebra 1 1.2. Convex Analysis (on R n
More informationParallel implementation of primal-dual interior-point methods for semidefinite programs
Parallel implementation of primal-dual interior-point methods for semidefinite programs Masakazu Kojima, Kazuhide Nakata Katsuki Fujisawa and Makoto Yamashita 3rd Annual McMaster Optimization Conference:
More informationOn the Sandwich Theorem and a approximation algorithm for MAX CUT
On the Sandwich Theorem and a 0.878-approximation algorithm for MAX CUT Kees Roos Technische Universiteit Delft Faculteit Electrotechniek. Wiskunde en Informatica e-mail: C.Roos@its.tudelft.nl URL: http://ssor.twi.tudelft.nl/
More informationConvexification of Mixed-Integer Quadratically Constrained Quadratic Programs
Convexification of Mixed-Integer Quadratically Constrained Quadratic Programs Laura Galli 1 Adam N. Letchford 2 Lancaster, April 2011 1 DEIS, University of Bologna, Italy 2 Department of Management Science,
More informationConvex Optimization M2
Convex Optimization M2 Lecture 8 A. d Aspremont. Convex Optimization M2. 1/57 Applications A. d Aspremont. Convex Optimization M2. 2/57 Outline Geometrical problems Approximation problems Combinatorial
More informationLagrange Duality. Daniel P. Palomar. Hong Kong University of Science and Technology (HKUST)
Lagrange Duality Daniel P. Palomar Hong Kong University of Science and Technology (HKUST) ELEC5470 - Convex Optimization Fall 2017-18, HKUST, Hong Kong Outline of Lecture Lagrangian Dual function Dual
More informationHow to generate weakly infeasible semidefinite programs via Lasserre s relaxations for polynomial optimization
CS-11-01 How to generate weakly infeasible semidefinite programs via Lasserre s relaxations for polynomial optimization Hayato Waki Department of Computer Science, The University of Electro-Communications
More informationResearch overview. Seminar September 4, Lehigh University Department of Industrial & Systems Engineering. Research overview.
Research overview Lehigh University Department of Industrial & Systems Engineering COR@L Seminar September 4, 2008 1 Duality without regularity condition Duality in non-exact arithmetic 2 interior point
More informationThe moment-lp and moment-sos approaches in optimization
The moment-lp and moment-sos approaches in optimization LAAS-CNRS and Institute of Mathematics, Toulouse, France Workshop Linear Matrix Inequalities, Semidefinite Programming and Quantum Information Theory
More informationPrimal-Dual Geometry of Level Sets and their Explanatory Value of the Practical Performance of Interior-Point Methods for Conic Optimization
Primal-Dual Geometry of Level Sets and their Explanatory Value of the Practical Performance of Interior-Point Methods for Conic Optimization Robert M. Freund M.I.T. June, 2010 from papers in SIOPT, Mathematics
More informationLecture 5. Theorems of Alternatives and Self-Dual Embedding
IE 8534 1 Lecture 5. Theorems of Alternatives and Self-Dual Embedding IE 8534 2 A system of linear equations may not have a solution. It is well known that either Ax = c has a solution, or A T y = 0, c
More informationLecture: Examples of LP, SOCP and SDP
1/34 Lecture: Examples of LP, SOCP and SDP Zaiwen Wen Beijing International Center For Mathematical Research Peking University http://bicmr.pku.edu.cn/~wenzw/bigdata2018.html wenzw@pku.edu.cn Acknowledgement:
More informationELE539A: Optimization of Communication Systems Lecture 15: Semidefinite Programming, Detection and Estimation Applications
ELE539A: Optimization of Communication Systems Lecture 15: Semidefinite Programming, Detection and Estimation Applications Professor M. Chiang Electrical Engineering Department, Princeton University March
More informationCS711008Z Algorithm Design and Analysis
CS711008Z Algorithm Design and Analysis Lecture 8 Linear programming: interior point method Dongbo Bu Institute of Computing Technology Chinese Academy of Sciences, Beijing, China 1 / 31 Outline Brief
More informationThe moment-lp and moment-sos approaches
The moment-lp and moment-sos approaches LAAS-CNRS and Institute of Mathematics, Toulouse, France CIRM, November 2013 Semidefinite Programming Why polynomial optimization? LP- and SDP- CERTIFICATES of POSITIVITY
More information10 Numerical methods for constrained problems
10 Numerical methods for constrained problems min s.t. f(x) h(x) = 0 (l), g(x) 0 (m), x X The algorithms can be roughly divided the following way: ˆ primal methods: find descent direction keeping inside
More information12. Interior-point methods
12. Interior-point methods Convex Optimization Boyd & Vandenberghe inequality constrained minimization logarithmic barrier function and central path barrier method feasibility and phase I methods complexity
More informationIdentifying Redundant Linear Constraints in Systems of Linear Matrix. Inequality Constraints. Shafiu Jibrin
Identifying Redundant Linear Constraints in Systems of Linear Matrix Inequality Constraints Shafiu Jibrin (shafiu.jibrin@nau.edu) Department of Mathematics and Statistics Northern Arizona University, Flagstaff
More informationTamás Terlaky George N. and Soteria Kledaras 87 Endowed Chair Professor. Chair, Department of Industrial and Systems Engineering Lehigh University
5th SJOM Bejing, 2011 Cone Linear Optimization (CLO) From LO, SOCO and SDO Towards Mixed-Integer CLO Tamás Terlaky George N. and Soteria Kledaras 87 Endowed Chair Professor. Chair, Department of Industrial
More informationLecture 1. 1 Conic programming. MA 796S: Convex Optimization and Interior Point Methods October 8, Consider the conic program. min.
MA 796S: Convex Optimization and Interior Point Methods October 8, 2007 Lecture 1 Lecturer: Kartik Sivaramakrishnan Scribe: Kartik Sivaramakrishnan 1 Conic programming Consider the conic program min s.t.
More informationInfeasible Primal-Dual (Path-Following) Interior-Point Methods for Semidefinite Programming*
Infeasible Primal-Dual (Path-Following) Interior-Point Methods for Semidefinite Programming* Notes for CAAM 564, Spring 2012 Dept of CAAM, Rice University Outline (1) Introduction (2) Formulation & a complexity
More informationCopositive and Semidefinite Relaxations of the Quadratic Assignment Problem (appeared in Discrete Optimization 6 (2009) )
Copositive and Semidefinite Relaxations of the Quadratic Assignment Problem (appeared in Discrete Optimization 6 (2009) 231 241) Janez Povh Franz Rendl July 22, 2009 Abstract Semidefinite relaxations of
More informationLMI MODELLING 4. CONVEX LMI MODELLING. Didier HENRION. LAAS-CNRS Toulouse, FR Czech Tech Univ Prague, CZ. Universidad de Valladolid, SP March 2009
LMI MODELLING 4. CONVEX LMI MODELLING Didier HENRION LAAS-CNRS Toulouse, FR Czech Tech Univ Prague, CZ Universidad de Valladolid, SP March 2009 Minors A minor of a matrix F is the determinant of a submatrix
More informationELEMENTARY REFORMULATION AND SUCCINCT CERTIFICATES IN CONIC LINEAR PROGRAMMING. Minghui Liu
ELEMENTARY REFORMULATION AND SUCCINCT CERTIFICATES IN CONIC LINEAR PROGRAMMING Minghui Liu A dissertation submitted to the faculty of the University of North Carolina at Chapel Hill in partial fulfillment
More informationConvex Optimization & Lagrange Duality
Convex Optimization & Lagrange Duality Chee Wei Tan CS 8292 : Advanced Topics in Convex Optimization and its Applications Fall 2010 Outline Convex optimization Optimality condition Lagrange duality KKT
More informationConvex 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 informationAgenda. Applications of semidefinite programming. 1 Control and system theory. 2 Combinatorial and nonconvex optimization
Agenda Applications of semidefinite programming 1 Control and system theory 2 Combinatorial and nonconvex optimization 3 Spectral estimation & super-resolution Control and system theory SDP in wide use
More informationNotes on the decomposition result of Karlin et al. [2] for the hierarchy of Lasserre by M. Laurent, December 13, 2012
Notes on the decomposition result of Karlin et al. [2] for the hierarchy of Lasserre by M. Laurent, December 13, 2012 We present the decomposition result of Karlin et al. [2] for the hierarchy of Lasserre
More informationA path following interior-point algorithm for semidefinite optimization problem based on new kernel function. djeffal
Journal of Mathematical Modeling Vol. 4, No., 206, pp. 35-58 JMM A path following interior-point algorithm for semidefinite optimization problem based on new kernel function El Amir Djeffal a and Lakhdar
More informationEfficient Solution of Maximum-Entropy Sampling Problems
Efficient Solution of Maximum-Entropy Sampling Problems Kurt M. Anstreicher July 10, 018 Abstract We consider a new approach for the maximum-entropy sampling problem (MESP) that is based on bounds obtained
More informationThe maximal stable set problem : Copositive programming and Semidefinite Relaxations
The maximal stable set problem : Copositive programming and Semidefinite Relaxations Kartik Krishnan Department of Mathematical Sciences Rensselaer Polytechnic Institute Troy, NY 12180 USA kartis@rpi.edu
More informationA ten page introduction to conic optimization
CHAPTER 1 A ten page introduction to conic optimization This background chapter gives an introduction to conic optimization. We do not give proofs, but focus on important (for this thesis) tools and concepts.
More informationNew stopping criteria for detecting infeasibility in conic optimization
Optimization Letters manuscript No. (will be inserted by the editor) New stopping criteria for detecting infeasibility in conic optimization Imre Pólik Tamás Terlaky Received: March 21, 2008/ Accepted:
More informationLift me up but not too high Fast algorithms to solve SDP s with block-diagonal constraints
Lift me up but not too high Fast algorithms to solve SDP s with block-diagonal constraints Nicolas Boumal Université catholique de Louvain (Belgium) IDeAS seminar, May 13 th, 2014, Princeton The Riemannian
More informationMustapha Ç. Pinar 1. Communicated by Jean Abadie
RAIRO Operations Research RAIRO Oper. Res. 37 (2003) 17-27 DOI: 10.1051/ro:2003012 A DERIVATION OF LOVÁSZ THETA VIA AUGMENTED LAGRANGE DUALITY Mustapha Ç. Pinar 1 Communicated by Jean Abadie Abstract.
More informationLecture 3: Semidefinite Programming
Lecture 3: Semidefinite Programming Lecture Outline Part I: Semidefinite programming, examples, canonical form, and duality Part II: Strong Duality Failure Examples Part III: Conditions for strong duality
More informationRelaxations and Randomized Methods for Nonconvex QCQPs
Relaxations and Randomized Methods for Nonconvex QCQPs Alexandre d Aspremont, Stephen Boyd EE392o, Stanford University Autumn, 2003 Introduction While some special classes of nonconvex problems can be
More information18. Primal-dual interior-point methods
L. Vandenberghe EE236C (Spring 213-14) 18. Primal-dual interior-point methods primal-dual central path equations infeasible primal-dual method primal-dual method for self-dual embedding 18-1 Symmetric
More informationEE/ACM Applications of Convex Optimization in Signal Processing and Communications Lecture 17
EE/ACM 150 - Applications of Convex Optimization in Signal Processing and Communications Lecture 17 Andre Tkacenko Signal Processing Research Group Jet Propulsion Laboratory May 29, 2012 Andre Tkacenko
More informationMathematical Optimisation, Chpt 2: Linear Equations and inequalities
Mathematical Optimisation, Chpt 2: Linear Equations and inequalities Peter J.C. Dickinson p.j.c.dickinson@utwente.nl http://dickinson.website version: 12/02/18 Monday 5th February 2018 Peter J.C. Dickinson
More informationLecture: 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 informationConvex Optimization and Support Vector Machine
Convex Optimization and Support Vector Machine Problem 0. Consider a two-class classification problem. The training data is L n = {(x 1, t 1 ),..., (x n, t n )}, where each t i { 1, 1} and x i R p. We
More informationPrimal-Dual Interior-Point Methods
Primal-Dual Interior-Point Methods Lecturer: Aarti Singh Co-instructor: Pradeep Ravikumar Convex Optimization 10-725/36-725 Outline Today: Primal-dual interior-point method Special case: linear programming
More information5. 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 informationSupport Vector Machines: Maximum Margin Classifiers
Support Vector Machines: Maximum Margin Classifiers Machine Learning and Pattern Recognition: September 16, 2008 Piotr Mirowski Based on slides by Sumit Chopra and Fu-Jie Huang 1 Outline What is behind
More information