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22
Determinant maximization with linear matrix inequality constraints
 SIAM Journal on Matrix Analysis and Applications
, 1998
"... constraints ..."
Linear Programming, Complexity Theory and Elementary Functional Analysis
 Mathematical Programming
, 1995
"... This paper was conceived in part while the author was sponsored by the visiting scientist program at the IBM T.J. Watson Research Center. Special thanks to Mike Shub, Roy Adler and Shmuel Winograd for their generosity. 1 Introduction ..."
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Cited by 87 (1 self)
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This paper was conceived in part while the author was sponsored by the visiting scientist program at the IBM T.J. Watson Research Center. Special thanks to Mike Shub, Roy Adler and Shmuel Winograd for their generosity. 1 Introduction
An affine scaling methodology for best basis selection
 IEEE Trans. Signal Processing
, 1999
"... Abstract — A methodology is developed to derive algorithms for optimal basis selection by minimizing diversity measures proposed by Wickerhauser and Donoho. These measures include the pnormlike (`(p 1)) diversity measures and the Gaussian and Shannon entropies. The algorithm development methodolog ..."
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Cited by 79 (11 self)
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Abstract — A methodology is developed to derive algorithms for optimal basis selection by minimizing diversity measures proposed by Wickerhauser and Donoho. These measures include the pnormlike (`(p 1)) diversity measures and the Gaussian and Shannon entropies. The algorithm development methodology uses a factored representation for the gradient and involves successive relaxation of the Lagrangian necessary condition. This yields algorithms that are intimately related to the Affine Scaling Transformation (AST) based methods commonly employed by the interior point approach to nonlinear optimization. The algorithms minimizing the `(p 1) diversity measures are equivalent to a recently developed class of algorithms called FOCal Underdetermined System Solver (FOCUSS). The general nature of the methodology provides a systematic approach for deriving this class of algorithms and a natural mechanism for extending them. It also facilitates a better understanding of the convergence behavior and a strengthening of the convergence results. The Gaussian entropy minimization algorithm is shown to be equivalent to a wellbehaved p =0normlike optimization algorithm. Computer experiments demonstrate that the pnormlike and the Gaussian entropy algorithms perform well, converging to sparse solutions. The Shannon entropy algorithm produces solutions that are concentrated but are shown to not converge to a fully sparse solution. I.
InfeasibleStart PrimalDual Methods And Infeasibility Detectors For Nonlinear Programming Problems
 Mathematical Programming
, 1996
"... In this paper we present several "infeasiblestart" pathfollowing and potentialreduction primaldual interiorpoint methods for nonlinear conic problems. These methods try to find a recession direction of the feasible set of a selfdual homogeneous primaldual problem. The methods under considerat ..."
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Cited by 31 (5 self)
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In this paper we present several "infeasiblestart" pathfollowing and potentialreduction primaldual interiorpoint methods for nonlinear conic problems. These methods try to find a recession direction of the feasible set of a selfdual homogeneous primaldual problem. The methods under consideration generate an fflsolution for an fflperturbation of an initial strictly (primal and dual) feasible problem in O( p ln fflae f ) iterations, where is the parameter of a selfconcordant barrier for the cone, ffl is a relative accuracy and ae f is a feasibility measure. We also discuss the behavior of pathfollowing methods as applied to infeasible problems. We prove that strict infeasibility (primal or dual) can be detected in O( p ln ae \Delta ) iterations, where ae \Delta is a primal or dual infeasibility measure. 1 Introduction Nesterov and Nemirovskii [9] first developed and investigated extensions of several classes of interiorpoint algorithms for linear programming t...
An efficient algorithm for minimizing a sum of Euclidean norms with applications
 SIAM Journal on Optimization
, 1997
"... Abstract. In recent years rich theories on polynomialtime interiorpoint algorithms have been developed. These theories and algorithms can be applied to many nonlinear optimization problems to yield better complexity results for various applications. In this paper, the problem of minimizing a sum o ..."
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Cited by 22 (4 self)
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Abstract. In recent years rich theories on polynomialtime interiorpoint algorithms have been developed. These theories and algorithms can be applied to many nonlinear optimization problems to yield better complexity results for various applications. In this paper, the problem of minimizing a sum of Euclidean norms is studied. This problem is convex but not everywhere differentiable. By transforming the problem into a standard convex programming problem in conic form, we show that an ɛoptimal solution can be computed efficiently using interiorpoint algorithms. As applications to this problem, polynomialtime algorithms are derived for the Euclidean single facility location problem, the Euclidean multifacility location problem, and the shortest network under a given tree topology. In particular, by solving the Newton equation in linear time using Gaussian elimination on leaves of a tree, we present an algorithm which computes an ɛoptimal solution to the shortest network under a given full Steiner topology interconnecting N regular points, in O(N √ N(log(¯c/ɛ)+ log N)) arithmetic operations where ¯c is the largest pairwise distance among the given points. The previous bestknown result on this problem is a graphical algorithm which requires O(N 2) arithmetic operations under certain conditions. Key words. polynomial time, interiorpoint algorithm, minimizing a sum of Euclidean norms, Euclidean facilities location, shortest networks, Steiner minimum trees
Quadratically Convergent Algorithms for Optimal Dextrous Hand Grasping
 IEEE Transactions on Robotics and Automation
"... Abstract—There is a robotic balancing task, namely realtime dextroushand grasping, for which linearly constrained, positive definite programming gives a quite satisfactory solution from an engineering point of view. We here propose refinements of this approach to reduce the computational effort. T ..."
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Cited by 14 (3 self)
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Abstract—There is a robotic balancing task, namely realtime dextroushand grasping, for which linearly constrained, positive definite programming gives a quite satisfactory solution from an engineering point of view. We here propose refinements of this approach to reduce the computational effort. The refinements include elimination of structural constraints in the positive definite matrices, orthogonalization of the grasp maps, and giving a precise Newton step size selection rule. Index Terms—Dextrous hand, gradient flow, Newton algorithm, optimal grasping, positive definite programming, Riemannian geometry, robotic hand. I.
A feasible BFGS interior point algorithm for solving strongly convex minimization problems
 SIAM J. OPTIM
, 2000
"... We propose a BFGS primaldual interior point method for minimizing a convex function on a convex set defined by equality and inequality constraints. The algorithm generates feasible iterates and consists in computing approximate solutions of the optimality conditions perturbed by a sequence of posit ..."
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Cited by 13 (1 self)
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We propose a BFGS primaldual interior point method for minimizing a convex function on a convex set defined by equality and inequality constraints. The algorithm generates feasible iterates and consists in computing approximate solutions of the optimality conditions perturbed by a sequence of positive parameters µ converging to zero. We prove that it converges qsuperlinearly for each fixed µ. We also show that it is globally convergent to the analytic center of the primaldual optimalset when µ tends to 0 and strict complementarity holds.
Interior Point Methods: Current Status And Future Directions
, 1997
"... This article provides a synopsis of the major developments in interior point methods for mathematical programming in the last thirteen years, and discusses current and future research directions in interior point methods, with a brief selective guide to the research literature. AMS Subject Classific ..."
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Cited by 13 (0 self)
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This article provides a synopsis of the major developments in interior point methods for mathematical programming in the last thirteen years, and discusses current and future research directions in interior point methods, with a brief selective guide to the research literature. AMS Subject Classification: 90C, 90C05, 90C60 Keywords: Linear Programming, Newton's Method, Interior Point Methods, Barrier Method, Semidefinite Programming, SelfConcordance, Convex Programming, Condition Numbers 1 An earlier version of this article has previously appeared in OPTIMA  Mathematical Programming Society Newsletter No. 51, 1996 2 M.I.T. Sloan School of Management, Building E40149A, Cambridge, MA 02139, USA. email: rfreund@mit.edu 3 The Institute of Statistical Mathematics, 467 MinamiAzabu, Minatoku, Tokyo 106 JAPAN. email: mizuno@ism.ac.jp INTERIOR POINT METHODS 1 1 Introduction and Synopsis The purpose of this article is twofold: to provide a synopsis of the major developments in ...
An Efficient Algorithm for Minimizing a Sum of PNorms
 SIAM Journal on Optimization
, 1997
"... We study the problem of minimizing a sum of pnorms where p is a fixed real number in the interval [1; 1]. Several practical algorithms have been proposed to solve this problem. However, none of them has a known polynomial time complexity. In this paper, we transform the problem into standard conic ..."
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Cited by 13 (2 self)
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We study the problem of minimizing a sum of pnorms where p is a fixed real number in the interval [1; 1]. Several practical algorithms have been proposed to solve this problem. However, none of them has a known polynomial time complexity. In this paper, we transform the problem into standard conic form. Unlike those in most convex optimization problems, the cone for the pnorm problem is not selfdual unless p = 2. Nevertheless, we are able to construct two logarithmically homogeneous selfconcordant barrier functions for this problem. The barrier parameter of the first barrier function does not depend on p. The barrier parameter of the second barrier function increases with p. Using both barrier functions, we present a primaldual potential reduction algorithm to compute an ffloptimal solution in polynomial time that is independent of p. Computational experiences of a Matlab implementation are also reported. Key words. Shortest network, Steiner minimum trees, facilities location, po...