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23
On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing
 In Proc. EMNLP
, 2010
"... This paper introduces dual decomposition as a framework for deriving inference algorithms for NLP problems. The approach relies on standard dynamicprogramming algorithms as oracle solvers for subproblems, together with a simple method for forcing agreement between the different oracles. The approa ..."
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Cited by 74 (4 self)
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This paper introduces dual decomposition as a framework for deriving inference algorithms for NLP problems. The approach relies on standard dynamicprogramming algorithms as oracle solvers for subproblems, together with a simple method for forcing agreement between the different oracles. The approach provably solves a linear programming (LP) relaxation of the global inference problem. It leads to algorithms that are simple, in that they use existing decoding algorithms; efficient, in that they avoid exact algorithms for the full model; and often exact, in that empirically they often recover the correct solution in spite of using an LP relaxation. We give experimental results on two problems: 1) the combination of two lexicalized parsing models; and 2) the combination of a lexicalized parsing model and a trigram partofspeech tagger. 1
A Catalog of Steiner Tree Formulations
, 1993
"... We present some existing and some new formulations for the Steiner tree and Steiner arborescence problems. We show the equivalence of many of these formulations. In particular, we establish the equivalence between the classical bidirected dicut relaxation and two vertex weighted undirected relaxatio ..."
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Cited by 33 (0 self)
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We present some existing and some new formulations for the Steiner tree and Steiner arborescence problems. We show the equivalence of many of these formulations. In particular, we establish the equivalence between the classical bidirected dicut relaxation and two vertex weighted undirected relaxations. The motivation behind this study is a characterization of the feasible region of the dicut relaxation in the natural space corresponding to the Steiner tree problem.
The Steiner tree polytope and related polyhedra
, 1994
"... We consider the vertexweighted version of the undirected Steiner tree problem. In this problem, a cost is incurred both for the vertices and the edges present in the Steiner tree. We completely describe the associated polytope by linear inequalities when the underlying graph is seriesparallel. For ..."
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Cited by 30 (1 self)
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We consider the vertexweighted version of the undirected Steiner tree problem. In this problem, a cost is incurred both for the vertices and the edges present in the Steiner tree. We completely describe the associated polytope by linear inequalities when the underlying graph is seriesparallel. For general graphs, this formulation can be interpreted as a (partial) extended formulation for the Steiner tree problem. By projecting this formulation, we obtain some very large classes of facetdefining valid inequalities for the Steiner tree polytope.
A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing
"... Dual decomposition, and more generally Lagrangian relaxation, is a classical method for combinatorial optimization; it has recently been applied to several inference problems in natural language processing (NLP). This tutorial gives an overview of the technique. We describe example algorithms, descr ..."
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Cited by 26 (4 self)
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Dual decomposition, and more generally Lagrangian relaxation, is a classical method for combinatorial optimization; it has recently been applied to several inference problems in natural language processing (NLP). This tutorial gives an overview of the technique. We describe example algorithms, describe formal guarantees for the method, and describe practical issues in implementing the algorithms. While our examples are predominantly drawn from the NLP literature, the material should be of general relevance to inference problems in machine learning. A central theme of this tutorial is that Lagrangian relaxation is naturally applied in conjunction with a broad class of combinatorial algorithms, allowing inference in models that go significantly beyond previous work on Lagrangian relaxation for inference in graphical models.
Extended formulations in combinatorial optimization
 OPTIMA
, 2013
"... The concept of representing a polytope that is associated with some combinatorial optimization problem as a linear projection of a higherdimensional polyhedron has recently received increasing attention. In this paper (written for the newsletter Optima of the Mathematical Optimization Society), we ..."
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Cited by 16 (2 self)
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The concept of representing a polytope that is associated with some combinatorial optimization problem as a linear projection of a higherdimensional polyhedron has recently received increasing attention. In this paper (written for the newsletter Optima of the Mathematical Optimization Society), we provide a brief introduction to this topic and sketch some of the recent developments with respect to both tools for constructing such extended formulations as well as lower bounds on their sizes.
Exact Decoding of Syntactic Translation Models through Lagrangian Relaxation
, 2011
"... We describe an exact decoding algorithm for syntaxbased statistical translation. The approach uses Lagrangian relaxation to decompose the decoding problem into tractable subproblems, thereby avoiding exhaustive dynamic programming. The method recovers exact solutions, with certificates of optimalit ..."
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Cited by 14 (3 self)
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We describe an exact decoding algorithm for syntaxbased statistical translation. The approach uses Lagrangian relaxation to decompose the decoding problem into tractable subproblems, thereby avoiding exhaustive dynamic programming. The method recovers exact solutions, with certificates of optimality, on over 97 % of test examples; it has comparable speed to stateoftheart decoders.
Extended Formulations for Packing and Partitioning Orbitopes
, 2008
"... We give compact extended formulations for the packing and partitioning orbitopes (with respect to the full symmetric group) described and analyzed in [6]. These polytopes are the convex hulls of all 0/1matrices with lexicographically sorted columns and at most, resp. exactly, one 1entry per row. ..."
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Cited by 9 (2 self)
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We give compact extended formulations for the packing and partitioning orbitopes (with respect to the full symmetric group) described and analyzed in [6]. These polytopes are the convex hulls of all 0/1matrices with lexicographically sorted columns and at most, resp. exactly, one 1entry per row. They are important objects for symmetry reduction in certain integer programs. Using the extended formulations, we also derive a rather simple proof of the fact [6] that basically shiftedcolumn inequalities suffice in order to describe those orbitopes linearly.
An extension of disjunctive programming and its impact for compact tree formulations
, 2010
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POLYHEDRAL COMBINATORICS
"... Polyhedral combinatorics is a rich mathematical subject motivated by integer and linear programming. While not exhaustive, this survey covers a variety of interesting topics, so let’s get right to it! ..."
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Cited by 5 (0 self)
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Polyhedral combinatorics is a rich mathematical subject motivated by integer and linear programming. While not exhaustive, this survey covers a variety of interesting topics, so let’s get right to it!