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156
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
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Cited by 582 (23 self)
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Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first derivatives are available, and that the constraint gradients are sparse. We discuss
Efficiently Inducing Features of Conditional Random Fields
, 2003
"... Conditional Random Fields (CRFs) are undirected graphical models, a special case of which correspond to conditionallytrained finite state machines. A key advantage of CRFs is their great flexibility to include a wide variety of arbitrary, nonindependent features of the input. Faced with ..."
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Cited by 227 (12 self)
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Conditional Random Fields (CRFs) are undirected graphical models, a special case of which correspond to conditionallytrained finite state machines. A key advantage of CRFs is their great flexibility to include a wide variety of arbitrary, nonindependent features of the input. Faced with
Table Extraction Using Conditional Random Fields
, 2003
"... The ability to find tables and extract information from them is a necessary component of data mining, question answering, and other information retrieval tasks. Documents often contain tables in order to communicate densely packed, multidimensional information. Tables do this by employing layout pa ..."
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Cited by 147 (10 self)
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The ability to find tables and extract information from them is a necessary component of data mining, question answering, and other information retrieval tasks. Documents often contain tables in order to communicate densely packed, multidimensional information. Tables do this by employing layout patterns to efficiently indicate fields and records in twodimensional form.
Collective multilabel classification
 In Proc. of CIKM
, 2005
"... This Article is brought to you for free and open access by the Computer Science at ScholarWorks@UMass Amherst. It has been accepted for inclusion ..."
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Cited by 122 (3 self)
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This Article is brought to you for free and open access by the Computer Science at ScholarWorks@UMass Amherst. It has been accepted for inclusion
A Sqp Method For General Nonlinear Programs Using Only Equality Constrained Subproblems
 MATHEMATICAL PROGRAMMING
, 1993
"... In this paper we describe a new version of a sequential equality constrained quadratic programming method for general nonlinear programs with mixed equality and inequality constraints. Compared with an older version [34] it is much simpler to implement and allows any kind of changes of the working s ..."
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Cited by 69 (2 self)
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In this paper we describe a new version of a sequential equality constrained quadratic programming method for general nonlinear programs with mixed equality and inequality constraints. Compared with an older version [34] it is much simpler to implement and allows any kind of changes of the working set in every step. Our method relies on a strong regularity condition. As far as it is applicable the new approach is superior to conventional SQPmethods, as demonstrated by extensive numerical tests.
An Introduction to Conditional Random Fields
 Foundations and Trends in Machine Learning
, 2012
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An interior algorithm for nonlinear optimization that combines line search and trust region steps
 Mathematical Programming 107
, 2006
"... An interiorpoint method for nonlinear programming is presented. It enjoys the flexibility of switching between a line search method that computes steps by factoring the primaldual equations and a trust region method that uses a conjugate gradient iteration. Steps computed by direct factorization a ..."
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Cited by 58 (12 self)
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An interiorpoint method for nonlinear programming is presented. It enjoys the flexibility of switching between a line search method that computes steps by factoring the primaldual equations and a trust region method that uses a conjugate gradient iteration. Steps computed by direct factorization are always tried first, but if they are deemed ineffective, a trust region iteration that guarantees progress toward stationarity is invoked. To demonstrate its effectiveness, the algorithm is implemented in the Knitro [6, 28] software package and is extensively tested on a wide selection of test problems. 1
LBFGSB  Fortran Subroutines for LargeScale Bound Constrained Optimization
, 1994
"... LBFGSB is a limited memory algorithm for solving large nonlinear optimization problems subject to simple bounds on the variables. It is intended for problems in which information on the Hessian matrix is di cult to obtain, or for large dense problems. LBFGSB can also be used for unconstrained pr ..."
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Cited by 57 (3 self)
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LBFGSB is a limited memory algorithm for solving large nonlinear optimization problems subject to simple bounds on the variables. It is intended for problems in which information on the Hessian matrix is di cult to obtain, or for large dense problems. LBFGSB can also be used for unconstrained problems, and in this case performs similarly to its predecessor, algorithm LBFGS (Harwell routine VA15). The algorithm is implemented in Fortran 77.
TrustRegion InteriorPoint Algorithms For Minimization Problems With Simple Bounds
 SIAM J. CONTROL AND OPTIMIZATION
, 1995
"... Two trustregion interiorpoint algorithms for the solution of minimization problems with simple bounds are analyzed and tested. The algorithms scale the local model in a way similar to Coleman and Li [1]. The first algorithm is more usual in that the trust region and the local quadratic model are c ..."
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Cited by 56 (18 self)
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Two trustregion interiorpoint algorithms for the solution of minimization problems with simple bounds are analyzed and tested. The algorithms scale the local model in a way similar to Coleman and Li [1]. The first algorithm is more usual in that the trust region and the local quadratic model are consistently scaled. The second algorithm proposed here uses an unscaled trust region. A global convergence result for these algorithms is given and dogleg and conjugategradient algorithms to compute trial steps are introduced. Some numerical examples that show the advantages of the second algorithm are presented.