The major success story of natural language processing over the last decade has been the development of high-accuracy statistical methods for a wide-range of language applications. The availability of large textual data sets has made it possible to employ increasingly sophisticated statistical models to improve performance on language tasks. However, oftentimes these more complex models come at...

A Lagrangian relaxation network for graph matching is presented. The problem is formulated as follows: given graphs G and g, find a permutation matrix M that brings the two sets of vertices into correspondence. Permutation matrix constraints are formulated in the framework of deterministic annealing. Our approach is in the same spirit as a Lagrangian decomposition approach in that the row and c...

In contrast to proteins, many classes of functionally related RNA molecules show a rather weak sequence conservation but instead a fairly well conserved secondary structure. Hence it is clear, that any method that relates RNA sequences in form of (multiple) alignments should take structural features into account. Since multiple alignments are of great importance for subsequent data analysis, re...

This paper proposes an augmented Lagrange Hopfield network (ALHN) for combined heat and power economic dispatch (CHPED) problem. The ALHN method is the continuous Hopfield neural network with its energy function based on augmented Lagrangian relaxation. In the proposed ALHN, the energy function is augmented by Hopfield terms from Hopfield neural network and penalty factors from augmented Lagran...

We prove a general result demonstrating the power of Lagrangian relaxation in solving constrained maximization problems with arbitrary objective functions. This yields a unified approach for solving a wide class of subset selection problems with linear constraints. Given a problem in this class and some small ε ∈ (0, 1), we show that if there exists a ρ-approximation algorithm for the Lagrangia...

This article is interestedin the problemof the linguisticcontent of a speech corpus. Depending on the target task (speech recognition, speech synthesis, etc) we try to control the phonological and linguistic content of the corpus by collectingan optimal set of sentences which make it possible to cover a preset description of phonological attributes (prosodic tags, allophones, syllables, etc) un...

This paper presents an enhanced merit order (EMO) and augmented Lagrange Hopfield network (ALHN) for unit commitment (UC). The EMO method is a merit order based heuristic search for unit scheduling and ALHN is a continuous Hopfield network based on augmented Lagrange relaxation for economic dispatch problem. First, generating units are sorted in ascending average production cost and committed t...

For a quadratic optimization problem (QOP) with linear equality constraints in continuous nonnegative variables and binary variables, we propose three relaxations in simplified forms with a parameter λ: Lagrangian, completely positive, and copositive relaxations. These relaxations are obtained by reducing the QOP to an equivalent QOP with a single quadratic equality constraint in nonnegative va...

For a quadratic optimization problem (QOP) with linear equality constraints in continuous nonnegative variables and binary variables, we propose three relaxations in simplified forms with a parameter λ: Lagrangian, completely positive, and copositive relaxations. These relaxations are obtained by reducing the QOP to an equivalent QOP with a single quadratic equality constraint in nonnegative va...

We present fast, accurate, direct nonprojective dependency parsers with thirdorder features. Our approach uses AD3, an accelerated dual decomposition algorithm which we extend to handle specialized head automata and sequential head bigram models. Experiments in fourteen languages yield parsing speeds competitive to projective parsers, with state-ofthe-art accuracies for the largest datasets (En...