A feasible method for optimization with orthogonality constraints

Minimization with orthogonality constraints (e.g., ) and/or spherical constraints (e.g., ) has wide applications in polynomial optimization, combinatorial optimization, eigenvalue problems, sparse PCA, p-harmonic flows, 1-bit compressive sensing, matrix rank minimization, etc. These problems are dif...

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Vydáno v:Mathematical programming Ročník 142; číslo 1-2; s. 397 - 434
Hlavní autoři: Wen, Zaiwen, Yin, Wotao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2013
Springer Nature B.V
Témata:
ISSN:0025-5610, 1436-4646
On-line přístup:Získat plný text
Tagy: Přidat tag
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Abstract Minimization with orthogonality constraints (e.g., ) and/or spherical constraints (e.g., ) has wide applications in polynomial optimization, combinatorial optimization, eigenvalue problems, sparse PCA, p-harmonic flows, 1-bit compressive sensing, matrix rank minimization, etc. These problems are difficult because the constraints are not only non-convex but numerically expensive to preserve during iterations. To deal with these difficulties, we apply the Cayley transform—a Crank-Nicolson-like update scheme—to preserve the constraints and based on it, develop curvilinear search algorithms with lower flops compared to those based on projections and geodesics. The efficiency of the proposed algorithms is demonstrated on a variety of test problems. In particular, for the maxcut problem, it exactly solves a decomposition formulation for the SDP relaxation. For polynomial optimization, nearest correlation matrix estimation and extreme eigenvalue problems, the proposed algorithms run very fast and return solutions no worse than those from their state-of-the-art algorithms. For the quadratic assignment problem, a gap 0.842 % to the best known solution on the largest problem “tai256c” in QAPLIB can be reached in 5 min on a typical laptop.
AbstractList Minimization with orthogonality constraints (e.g., $$X logical or top X = I$$) and/or spherical constraints (e.g., $$\Vert x\Vert _2 = 1$$) has wide applications in polynomial optimization, combinatorial optimization, eigenvalue problems, sparse PCA, p-harmonic flows, 1-bit compressive sensing, matrix rank minimization, etc. These problems are difficult because the constraints are not only non-convex but numerically expensive to preserve during iterations. To deal with these difficulties, we apply the Cayley transform-a Crank-Nicolson-like update scheme-to preserve the constraints and based on it, develop curvilinear search algorithms with lower flops compared to those based on projections and geodesics. The efficiency of the proposed algorithms is demonstrated on a variety of test problems. In particular, for the maxcut problem, it exactly solves a decomposition formulation for the SDP relaxation. For polynomial optimization, nearest correlation matrix estimation and extreme eigenvalue problems, the proposed algorithms run very fast and return solutions no worse than those from their state-of-the-art algorithms. For the quadratic assignment problem, a gap 0.842 % to the best known solution on the largest problem "tai256ca[euro] in QAPLIB can be reached in 5 min on a typical laptop.
Minimization with orthogonality constraints (e.g., ) and/or spherical constraints (e.g., ) has wide applications in polynomial optimization, combinatorial optimization, eigenvalue problems, sparse PCA, p-harmonic flows, 1-bit compressive sensing, matrix rank minimization, etc. These problems are difficult because the constraints are not only non-convex but numerically expensive to preserve during iterations. To deal with these difficulties, we apply the Cayley transform—a Crank-Nicolson-like update scheme—to preserve the constraints and based on it, develop curvilinear search algorithms with lower flops compared to those based on projections and geodesics. The efficiency of the proposed algorithms is demonstrated on a variety of test problems. In particular, for the maxcut problem, it exactly solves a decomposition formulation for the SDP relaxation. For polynomial optimization, nearest correlation matrix estimation and extreme eigenvalue problems, the proposed algorithms run very fast and return solutions no worse than those from their state-of-the-art algorithms. For the quadratic assignment problem, a gap 0.842 % to the best known solution on the largest problem “tai256c” in QAPLIB can be reached in 5 min on a typical laptop.
(ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image) Minimization with orthogonality constraints (e.g., ...) and/or spherical constraints (e.g., ...) has wide applications in polynomial optimization, combinatorial optimization, eigenvalue problems, sparse PCA, p-harmonic flows, 1-bit compressive sensing, matrix rank minimization, etc. These problems are difficult because the constraints are not only non-convex but numerically expensive to preserve during iterations. To deal with these difficulties, we apply the Cayley transform--a Crank-Nicolson-like update scheme--to preserve the constraints and based on it, develop curvilinear search algorithms with lower flops compared to those based on projections and geodesics. The efficiency of the proposed algorithms is demonstrated on a variety of test problems. In particular, for the maxcut problem, it exactly solves a decomposition formulation for the SDP relaxation. For polynomial optimization, nearest correlation matrix estimation and extreme eigenvalue problems, the proposed algorithms run very fast and return solutions no worse than those from their state-of-the-art algorithms. For the quadratic assignment problem, a gap 0.842 % to the best known solution on the largest problem "tai256c" in QAPLIB can be reached in 5 min on a typical laptop.[PUBLICATION ABSTRACT]
Author Yin, Wotao
Wen, Zaiwen
Author_xml – sequence: 1
  givenname: Zaiwen
  surname: Wen
  fullname: Wen, Zaiwen
  email: wendouble@gmail.com, zw2109@sjtu.edu.cn
  organization: Department of Mathematics and Institute of Natural Sciences, Shanghai Jiaotong University
– sequence: 2
  givenname: Wotao
  surname: Yin
  fullname: Yin, Wotao
  organization: Department of Computational and Applied Mathematics, Rice University
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Keywords Nearest correlation matrix
Invariant subspace
65K05
Curvilinear search
Maxcut SDP
90C26
Eigenvalue and eigenvector
Quadratic assignment problem
90C27
Orthogonality constraint
Cayley transformation
90C30
Polynomial optimization
Stiefel manifold
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Spherical constraint
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2013-12-00
20131201
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PublicationTitle Mathematical programming
PublicationTitleAbbrev Math. Program
PublicationYear 2013
Publisher Springer Berlin Heidelberg
Springer Nature B.V
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References Friedland, Nocedal, Overton (CR21) 1987; 24
He, Li, Zhang (CR27) 2010; 125
CR38
Absil, Mahony, Sepulchre (CR2) 2008
Malick, Povh, Rendl, Wiegele (CR35) 2009; 20
CR34
CR33
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Minimization with orthogonality constraints (e.g., $$X logical or top X = I$$) and/or spherical constraints (e.g., $$\Vert x\Vert _2 = 1$$) has wide...
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SubjectTerms Algorithms
Analysis
Applied mathematics
Assignment problem
Calculus of Variations and Optimal Control; Optimization
Combinatorics
Eigenvalues
Full Length Paper
Mathematical and Computational Physics
Mathematical Methods in Physics
Mathematical programming
Mathematics
Mathematics and Statistics
Mathematics of Computing
Numerical Analysis
Optimization
Partial differential equations
Polynomials
Studies
Theoretical
Topological manifolds
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