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53
A tutorial on support vector regression
, 2004
"... In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing ..."
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Cited by 470 (2 self)
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In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.
Fast Contact Force Computation for Nonpenetrating Rigid Bodies
, 1994
"... A new algorithm for computing contact forces between solid objects with friction is presented. The algorithm allows a mix of contact points with static and dynamic friction. In contrast to previous approaches, the problem of computing contact forces is not transformed into an optimization problem. B ..."
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Cited by 214 (6 self)
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A new algorithm for computing contact forces between solid objects with friction is presented. The algorithm allows a mix of contact points with static and dynamic friction. In contrast to previous approaches, the problem of computing contact forces is not transformed into an optimization problem. Because of this, the need for sophisticated optimization software packages is eliminated. For both systems with and without friction, the algorithm has proven to be considerably faster, simpler, and more reliable than previous approaches to the problem. In particular, implementation of the algorithm by nonspecialists in numerical programming is quite feasible.
CUTE: Constrained and unconstrained testing environment
, 1993
"... The purpose of this paper is to discuss the scope and functionality of a versatile environment for testing small and largescale nonlinear optimization algorithms. Although many of these facilities were originally produced by the authors in conjunction with the software package LANCELOT, we belie ..."
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Cited by 152 (3 self)
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The purpose of this paper is to discuss the scope and functionality of a versatile environment for testing small and largescale nonlinear optimization algorithms. Although many of these facilities were originally produced by the authors in conjunction with the software package LANCELOT, we believe that they will be useful in their own right and should be available to researchers for their development of optimization software. The tools are available by anonymous ftp from a number of sources and may, in many cases, be installed automatically. The scope of a major collection of test problems written in the standard input format (SIF) used by the LANCELOT software package is described. Recognising that most software was not written with the SIF in mind, we provide tools to assist in building an interface between this input format and other optimization packages. These tools already provide a link between the SIF and an number of existing packages, including MINOS and OSL. In ad...
A FortrantoC converter
 AT&T Bell Laboratories
, 1992
"... We describe f 2c, a program that translates Fortran 77 into C or C++. F 2c lets one portably mix C and Fortran and makes a large body of welltested Fortran source code available to C environments. 1. ..."
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Cited by 34 (0 self)
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We describe f 2c, a program that translates Fortran 77 into C or C++. F 2c lets one portably mix C and Fortran and makes a large body of welltested Fortran source code available to C environments. 1.
On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs
, 1994
"... A general decomposition framework for large convex optimization problems based on augmented Lagrangians is described. The approach is then applied to multistage stochastic programming problems in two different ways: by decomposing the problem into scenarios or decomposing it into nodes corresponding ..."
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Cited by 33 (4 self)
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A general decomposition framework for large convex optimization problems based on augmented Lagrangians is described. The approach is then applied to multistage stochastic programming problems in two different ways: by decomposing the problem into scenarios or decomposing it into nodes corresponding to stages. In both cases the method has favorable convergence properties and a structure which makes it convenient for parallel computing environments. Keywords: Stochastic Programming, Decomposition, Augmented Lagrangian, Jacobi Method, Parallel Computation. iii iv On Augmented Lagrangian Decomposition Methods For Multistage Stochastic Programs Andrzej Ruszczy'nski 1 Introduction Multistage stochastic optimization problems belong to the most difficult problems of mathematical programming. Their size grows very quickly with the number of stages and with the number of events (scenarios) incorporated into the model. Although problems of this type occur frequently in applications (like,...
Regularized Principal Manifolds
 In Computational Learning Theory: 4th European Conference
, 2001
"... Many settings of unsupervised learning can be viewed as quantization problems  the minimization ..."
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Cited by 32 (4 self)
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Many settings of unsupervised learning can be viewed as quantization problems  the minimization
Optimal operation of multi reservoir systems: stateoftheart review
 J. Water Resour. Plann. Manag
, 2004
"... Abstract: With construction of new largescale water storage projects on the wane in the U.S. and other developed countries, attention must focus on improving the operational effectiveness and efficiency of existing reservoir systems for maximizing the beneficial uses of these projects. Optimal coor ..."
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Cited by 24 (0 self)
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Abstract: With construction of new largescale water storage projects on the wane in the U.S. and other developed countries, attention must focus on improving the operational effectiveness and efficiency of existing reservoir systems for maximizing the beneficial uses of these projects. Optimal coordination of the many facets of reservoir systems requires the assistance of computer modeling tools to provide information for rational management and operational decisions. The purpose of this review is to assess the stateoftheart in optimization of reservoir system management and operations and consider future directions for additional research and application. Optimization methods designed to prevail over the highdimensional, dynamic, nonlinear, and stochastic characteristics of reservoir systems are scrutinized, as well as extensions into multiobjective optimization. Application of heuristic programming methods using evolutionary and genetic algorithms are described, along with application of neural networks and fuzzy rulebased systems for inferring reservoir system operating rules.
The Cell Structures of Certain Lattices
, 1991
"... . The most important lattices in Euclidean space of dimension n 8 are the lattices A n (n ³ 2), D n (n ³ 4), E n (n = 6 , 7 , 8) and their duals. In this paper we determine the cell structures of all these lattices and their Voronoi and Delaunay polytopes in a uniform manner. The results for E 6 * ..."
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Cited by 19 (8 self)
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. The most important lattices in Euclidean space of dimension n 8 are the lattices A n (n ³ 2), D n (n ³ 4), E n (n = 6 , 7 , 8) and their duals. In this paper we determine the cell structures of all these lattices and their Voronoi and Delaunay polytopes in a uniform manner. The results for E 6 * and E 7 * simplify recent work of Worley, and also provide what may be new spacefilling polytopes in dimensions 6 and 7. 1. Introduction The CoxeterDynkin diagrams of types A n , D n , E 6 , E 7 and E 8 arise in surprisingly different parts of mathematics  see the discussions by Arnold [1] and Hazewinkel et al. [30]. In the present paper we study __________________ * This paper appeared in {\m Miscellanea mathematica}, P. Hilton, F. Hirzebruch, and R. Remmert, Eds., SpringerVerlag, NY, 1991, pp. 71107. (**) From the English version AutodaFe(Continuum, New York, p. 385) as translated by C. V. Wedgwood: "You have but to know an object by its proper name for it to lose its dange...
Experience with a Primal Presolve Algorithm
 IN LARGE SCALE OPTIMIZATION: STATE OF THE
, 1994
"... Sometimes an optimization problem can be simplified to a form that is faster to solve. Indeed, sometimes it is convenient to state a problem in a way that admits some obvious simplifications, such as eliminating fixed variables and removing constraints that become redundant after simple bounds on th ..."
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Cited by 13 (4 self)
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Sometimes an optimization problem can be simplified to a form that is faster to solve. Indeed, sometimes it is convenient to state a problem in a way that admits some obvious simplifications, such as eliminating fixed variables and removing constraints that become redundant after simple bounds on the variables have been updated appropriately. Because of this convenience, the AMPL modeling system includes a "presolver" that attempts to simplify a problem before passing it to a solver. The current AMPL presolver carries out all the primal simplifications described by Brearely et al. in 1975. This paper describes AMPL's presolver, discusses reconstruction of dual values for eliminated constraints, and presents some computational results.
The LP Dual Active Set Algorithm
, 1998
"... . An overview is given for a new algorithm, the LP Dual Active Set Algorithm, to solve linear programming problems. In its pure form, the algorithm uses a series of projections to ascend the dual function. These projections can be approximated using proximal techniques, and both iterative and direct ..."
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Cited by 12 (8 self)
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. An overview is given for a new algorithm, the LP Dual Active Set Algorithm, to solve linear programming problems. In its pure form, the algorithm uses a series of projections to ascend the dual function. These projections can be approximated using proximal techniques, and both iterative and direct methods can be applied to obtain highly accurate, small norm solutions to both the primal and the dual problem. High Performance Algorithms and Software in Nonlinear Optimization R. De Leone, A. Murli, P. M. Pardalos, and G. Toraldo, eds. Kluwer, Dordrecht, 1998, pp. 243254. This research was supported by the National Science Foundation. 1 1. Introduction. In this paper we give an overview of the LP Dual Active Set Algorithm (LP DASA) for solving a linear programming problem of the form: minimize c T x subject to Ax = b; l x u: (1) Here A is an m n matrix and x 2 R n . The Dual Active Set Algorithm originates from an algorithm to solve dual control problems presented i...