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On Lagrangian relaxation of quadratic matrix constraints
 SIAM J. Matrix Anal. Appl
, 2000
"... Abstract. Quadratically constrained quadratic programs (QQPs) play an important modeling role for many diverse problems. These problems are in general NP hard and numerically intractable. Lagrangian relaxations often provide good approximate solutions to these hard problems. Such relaxations are equ ..."
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Abstract. Quadratically constrained quadratic programs (QQPs) play an important modeling role for many diverse problems. These problems are in general NP hard and numerically intractable. Lagrangian relaxations often provide good approximate solutions to these hard problems. Such relaxations are equivalent to semidefinite programming relaxations. For several special cases of QQP, e.g., convex programs and trust region subproblems, the Lagrangian relaxation provides the exact optimal value, i.e., there is a zero duality gap. However, this is not true for the general QQP, or even the QQP with two convex constraints, but a nonconvex objective. In this paper we consider a certain QQP where the quadratic constraints correspond to the matrix orthogonality condition XXT = I. For this problem we show that the Lagrangian dual based on relaxing the constraints XXT = I and the seemingly redundant constraints XT X = I has a zero duality gap. This result has natural applications to quadratic assignment and graph partitioning problems, as well as the problem of minimizing the weighted sum of the largest eigenvalues of a matrix. We also show that the technique of relaxing quadratic matrix constraints can be used to obtain a strengthened semidefinite relaxation for the maxcut problem. Key words. Lagrangian relaxations, quadratically constrained quadratic programs, semidefinite programming, quadratic assignment, graph partitioning, maxcut problems
A Unifying Investigation of InteriorPoint Methods for Convex Programming
 Faculty of Mathematics and Informatics, TU Delft, NL2628 BL
, 1992
"... In the recent past a number of papers were written that present low complexity interiorpoint methods for different classes of convex programs. Goal of this article is to show that the logarithmic barrier function associated with these programs is selfconcordant, and that the analyses of interiorpo ..."
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In the recent past a number of papers were written that present low complexity interiorpoint methods for different classes of convex programs. Goal of this article is to show that the logarithmic barrier function associated with these programs is selfconcordant, and that the analyses of interiorpoint methods for these programs can thus be reduced to the analysis of interiorpoint methods with selfconcordant barrier functions. Key words: interiorpoint method, barrier function, dual geometric programming, (extended) entropy programming, primal and dual l p programming, relative Lipschitz condition, scaled Lipschitz condition, selfconcordance. 1 Introduction The efficiency of a barrier method for solving convex programs strongly depends on the properties of the barrier function used. A key property that is sufficient to prove fast convergence for barrier methods is the property of selfconcordance introduced in [17]. This condition not only allows a proof of polynomial convergen...
Notes on Duality in Second Order and POrder Cone Optimization
, 2000
"... Recently, the socalled second order cone optimization problem has received much attention, because the problem has many applications and the problem can at least in theory be solved eciently by interiorpoint methods. In this note we treat duality for second order cone optimization problems and in ..."
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Recently, the socalled second order cone optimization problem has received much attention, because the problem has many applications and the problem can at least in theory be solved eciently by interiorpoint methods. In this note we treat duality for second order cone optimization problems and in particular whether a nonzero duality gap can be introduced when casting a convex quadratically constrained optimization problem as a second order cone optimization problem. Furthermore, we also discuss the porder cone optimization problem which is a natural generalization of the second order case. Specically, we suggest a new selfconcordant barrier for the porder cone optimization problem. 1 Introdution The second order cone optimization problem can be stated as (SOCP) minimize f T x subject to jjA i x b i jj c i: x d i ; i = 1; : : : ; k; Hx = h: where A i 2 R (m i 1)n and H 2 R ln and all the other quantities have conforming dimensions. c i: denotes the ith row of ...
SEMIDEFINITE AND LAGRANGIAN RELAXATIONS FOR HARD COMBINATORIAL PROBLEMS
"... Semidefinite Programming is currently a very exciting and active area of research. Semidefinite relaxations generally provide very tight bounds for many classes of numerically hard problems. In addition, these relaxations can be solved efficiently by interiorpoint methods. In this ..."
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Cited by 3 (3 self)
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Semidefinite Programming is currently a very exciting and active area of research. Semidefinite relaxations generally provide very tight bounds for many classes of numerically hard problems. In addition, these relaxations can be solved efficiently by interiorpoint methods. In this
A Conic Formulation for L P Norm Optimization
, 2000
"... In this paper, we formulate the l p norm optimization problem as a conic optimization problem, derive its standard duality properties and show it can be solved in polynomial time. We first define an ad hoc closed convex cone L p , study its properties and derive its dual. This allows us to express ..."
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In this paper, we formulate the l p norm optimization problem as a conic optimization problem, derive its standard duality properties and show it can be solved in polynomial time. We first define an ad hoc closed convex cone L p , study its properties and derive its dual. This allows us to express the standard l p norm optimization primal problem as a conic problem involving L p . Using convex conic duality and our knowledge about L p , we proceed to derive the dual of this problem and prove the wellknown regularity properties of this primaldual pair, i.e. zero duality gap and primal attainment. Finally, we prove that the class of l p norm optimization problems can be solved up to a given accuracy in polynomial time, using the framework of interiorpoint algorithms and selfconcordant barriers.
Topics In Convex Optimization: InteriorPoint Methods, Conic Duality and Approximations
"... Contents Table of Contents i List of gures v Preface vii Introduction 1 I INTERIORPOINT METHODS 5 1 Interiorpoint methods for linear optimization 7 1.1.1 Linear optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.1.2 The simplex method . . . . . . . . . . . . . . . . . . . . ..."
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Contents Table of Contents i List of gures v Preface vii Introduction 1 I INTERIORPOINT METHODS 5 1 Interiorpoint methods for linear optimization 7 1.1.1 Linear optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.1.2 The simplex method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.1.3 A rst glimpse on interiorpoint methods . . . . . . . . . . . . . . . . 9 1.1.4 A short historical account . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2 Building blocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2.1 Duality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2.2 Optimality conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.2.3 Newton's method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.2.4 Barrier function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.2.5 The central path . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Algorithms for Convex Multiquadratic Programming
, 1997
"... A convex multiquadratic program is defined as minimizing a strictly convex quadratic function subject to convex quadratic inequality constraints. The associated Lagrangian dual problem is a strictly concave maximization problem subject to non negativity constraints. In this thesis three methods for ..."
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A convex multiquadratic program is defined as minimizing a strictly convex quadratic function subject to convex quadratic inequality constraints. The associated Lagrangian dual problem is a strictly concave maximization problem subject to non negativity constraints. In this thesis three methods for solving the dual program are developed. The methods are based on Projected Gradient Method, Sequential Quadratic Programming, and Affine Scaling respectively. Furthermore an algorithm for solving a convex quadratic program subject to a spheric constraint, which uses Hessenberg reduction of a symmetric positive semidefinite matrix, is developed. Computational results for dense and randomly generated small to medium size convex multiquadratic programs are presented. i Acknowledgments This report constitutes my master thesis. The work presented in this thesis was conducted at the Department of Industrial and Systems Engineering (ISE), at University of Florida, Gainesville, Florida during the...
Deriving Duality for l_pnorm Optimization Using Conic Optimization
, 1999
"... In this paper, we formulate the l p norm optimization problem as a conic optimization problem and derive its standard duality properties. We first define an ad hoc closed convex cone L and derive its dual. We express then the standard l p norm optimization primal problem as a conic problem involvi ..."
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In this paper, we formulate the l p norm optimization problem as a conic optimization problem and derive its standard duality properties. We first define an ad hoc closed convex cone L and derive its dual. We express then the standard l p norm optimization primal problem as a conic problem involving L. Using convex conic duality, we derive the dual of this problem and prove the wellknown regularity properties of this primaldual pair, i.e. zero duality gap and dual attainment.