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Interior Point Methods in Semidefinite Programming with Applications to Combinatorial Optimization
 SIAM Journal on Optimization
, 1993
"... We study the semidefinite programming problem (SDP), i.e the problem of optimization of a linear function of a symmetric matrix subject to linear equality constraints and the additional condition that the matrix be positive semidefinite. First we review the classical cone duality as specialized to S ..."
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Cited by 557 (12 self)
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We study the semidefinite programming problem (SDP), i.e the problem of optimization of a linear function of a symmetric matrix subject to linear equality constraints and the additional condition that the matrix be positive semidefinite. First we review the classical cone duality as specialized to SDP. Next we present an interior point algorithm which converges to the optimal solution in polynomial time. The approach is a direct extension of Ye's projective method for linear programming. We also argue that most known interior point methods for linear programs can be transformed in a mechanical way to algorithms for SDP with proofs of convergence and polynomial time complexity also carrying over in a similar fashion. Finally we study the significance of these results in a variety of combinatorial optimization problems including the general 01 integer programs, the maximum clique and maximum stable set problems in perfect graphs, the maximum k partite subgraph problem in graphs, and va...
Dictionary of protein secondary structure: pattern recognition of hydrogenbonded and geometrical features
 Biopolymers
, 1983
"... structure ..."
Logic Programming and Knowledge Representation
 Journal of Logic Programming
, 1994
"... In this paper, we review recent work aimed at the application of declarative logic programming to knowledge representation in artificial intelligence. We consider exten sions of the language of definite logic programs by classical (strong) negation, disjunc tion, and some modal operators and sh ..."
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Cited by 242 (20 self)
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In this paper, we review recent work aimed at the application of declarative logic programming to knowledge representation in artificial intelligence. We consider exten sions of the language of definite logic programs by classical (strong) negation, disjunc tion, and some modal operators and show how each of the added features extends the representational power of the language.
Communicated by the Editors
, 1977
"... In the paper a short proof is given for Kneser’s conjecture. The proof is based on Borsuk’s theorem and on a theorem of Gale. Recently, LOV~SZ has given a proof for Kneser’s conjecture [4]. He used Borsuk’s theorem. This fact gave the author the idea of the proof we present here. THEOREM (Kneser’s c ..."
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In the paper a short proof is given for Kneser’s conjecture. The proof is based on Borsuk’s theorem and on a theorem of Gale. Recently, LOV~SZ has given a proof for Kneser’s conjecture [4]. He used Borsuk’s theorem. This fact gave the author the idea of the proof we present here. THEOREM (Kneser’s
Approximation Algorithms for Disjoint Paths Problems
, 1996
"... The construction of disjoint paths in a network is a basic issue in combinatorial optimization: given a network, and specified pairs of nodes in it, we are interested in finding disjoint paths between as many of these pairs as possible. This leads to a variety of classical NPcomplete problems for w ..."
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Cited by 166 (0 self)
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The construction of disjoint paths in a network is a basic issue in combinatorial optimization: given a network, and specified pairs of nodes in it, we are interested in finding disjoint paths between as many of these pairs as possible. This leads to a variety of classical NPcomplete problems for which very little is known from the point of view of approximation algorithms. It has recently been brought into focus in work on problems such as VLSI layout and routing in highspeed networks; in these settings, the current lack of understanding of the disjoint paths problem is often an obstacle to the design of practical heuristics.
Linear smoothers and additive models
 The Annals of Statistics
, 1989
"... We study linear smoothers and their use in building nonparametric regression models. In part Qfthis paper we examine certain aspects of linear smoothers for scatterplots; examples of these are the running mean and running line, kernel, and cubic spline smoothers. The eigenvalue and singular value d ..."
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Cited by 99 (2 self)
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We study linear smoothers and their use in building nonparametric regression models. In part Qfthis paper we examine certain aspects of linear smoothers for scatterplots; examples of these are the running mean and running line, kernel, and cubic spline smoothers. The eigenvalue and singular value decompositions of the corresponding smoother matrix are used to qualitatively describe a smoother, and several other topics such as the number of degrees of freedom of a smoother are discussed. In the second part of the paper we describe how Iinearsmoothers can be used to estimate the additive model, a powerful nonparametric regression model, using the "backfitting algorithm". We study the convergence of the backfitting algorithm and prove its convergence for a class of smoothers that includes cubic e:ttJlCl€~nt jJI:::Jll<l.li:6I;:U least squares. algorithm and ' dis.cuss ev'W()r(is: Neaparametric, seanparametric, regression, GaussSeidelalgorithm,
All Pairs Shortest Paths using Bridging Sets and Rectangular Matrix Multiplication
 Journal of the ACM
, 2000
"... We present two new algorithms for solving the All Pairs Shortest Paths (APSP) problem for weighted directed graphs. Both algorithms use fast matrix multiplication algorithms. The first algorithm solves... ..."
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Cited by 88 (6 self)
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We present two new algorithms for solving the All Pairs Shortest Paths (APSP) problem for weighted directed graphs. Both algorithms use fast matrix multiplication algorithms. The first algorithm solves...
Proofs from the Book
, 1998
"... Paul Erdős liked to talk about The Book, in which God maintains the perfect proofs for mathematical theorems, following the dictum of G. H. Hardy that there is no permanent place for ugly mathematics. Erdős also said that you need not believe in God but, as a mathematician, you should believe in The ..."
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Cited by 87 (1 self)
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Paul Erdős liked to talk about The Book, in which God maintains the perfect proofs for mathematical theorems, following the dictum of G. H. Hardy that there is no permanent place for ugly mathematics. Erdős also said that you need not believe in God but, as a mathematician, you should believe in The Book. A few years ago, we suggested to him to write up a first (and very modest) approximation to The Book. He was enthusiastic about the idea and, characteristically, went to work immediately, filling page after page with his suggestions. Our book was supposed to appear in March 1998 as a present to Erdős ’ 85th birthday. With Paul’s unfortunate death in the summer of 1997, he is not listed as a coauthor. Instead this book is dedicated to his memory. We have no definition or characterization of what constitutes a proof from The Book: all we offer here is the examples that we have selected, hoping that our readers will share our enthusiasm about brilliant ideas, clever insights and wonderful observations. We also hope that our readers will
Small distortion and volume preserving embeddings for Planar and Euclidean metrics
, 1999
"... A finite metric space, (S,d) , contains a finite set of points and a distance function on pairs of points. A contraction is an embedding, h, of a finite metric space (S, d) into Rd where for any u, v E S, the Euclidean (&) distance between h(u) and h(v) is no more than d(u, v). The distortion of ..."
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Cited by 68 (1 self)
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A finite metric space, (S,d) , contains a finite set of points and a distance function on pairs of points. A contraction is an embedding, h, of a finite metric space (S, d) into Rd where for any u, v E S, the Euclidean (&) distance between h(u) and h(v) is no more than d(u, v). The distortion of the embedding is the maximum over pairs of the ratio of d(u, w) and the Euclidean distance between h(u) and h(v). Bourgain showed that any graphical metric could be embedded with distortion O(logn). Linial, London and Rabinovich and Aumman and Rabani used such embeddings to prove an O(log k) approximate maxflow mincut theorem for k commodity flow problems. A generalization of embeddings that preserve distances between pairsof points are embeddings that preserve volumes of larger sets. In particular, A (k, c)volume respecting embedding of npoints in any metric space is a contraction where every subset of k points has within an ck ’ factor of its maximal possible k ldimensional volume. Feige invented these embeddings in devising a polylogarithmic approximation algorithm for the bandwidth problem using these embeddings. Feige’s methods have subsequently been used by Vempala for approximating versions of the VLSI layout problem. Feise showed that a (k, O(10,g~‘ ~ n,/m)) volume r&ecting embedding ‘ eksted. ” Berecently found improved (k, 0 ( mdk log k + log n)) volume respecting embeddings. For metrics arising from planar graphs (planar metrics), we give (k,O(m)) volume respecting contractions. As a corollary, we give embeddings for
Results 1  10
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