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88
The TOMLAB Graphical User Interface for Nonlinear Programming. Advanced Modeling and Optimization
 in MATLAB. Annals of Operations Research, Modeling Languages and Approaches: Submitted
, 1999
"... The paper presents a Graphical User Interface (GUI) for nonlinear programming in Matlab. The GUI gives easy access to all features in the NLPLIB TB (NonLinear Programming LIBrary Toolbox) � a set of Matlab solvers, test problems, graphical and computational utilities for unconstrained and constrain ..."
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Cited by 14 (9 self)
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The paper presents a Graphical User Interface (GUI) for nonlinear programming in Matlab. The GUI gives easy access to all features in the NLPLIB TB (NonLinear Programming LIBrary Toolbox) � a set of Matlab solvers, test problems, graphical and computational utilities for unconstrained and constrained optimization, quadratic programming, unconstrained and constrained nonlinear least squares, boxbounded global optimization, global mixedinteger nonlinear programming, and exponential sum model tting. The GUI also runs the linear programming problems in the linear and discrete optimization toolbox OPERA TB. Both NLPLIB TB and OPERA TB are part of TOMLAB � an environment in Matlab for research and teaching in optimization. Presently, NLPLIB TB implements more than 25 solver algorithms, and it is possible to call solvers in the Math Works Optimization Toolbox. MEX le interfaces are developed for seven Fortran and C solvers, and others are easily added using the same type of interface routines. There are four ways to solve a problem: by a direct call to the solver routine or a call to amultisolver driver routine, or interactively, using the Graphical User Interface or a menu system. The GUI may alsobe used as a preprocessor to generate Matlab code for standalone runs. Alargeset of standard test problems is implemented in TOMLAB. Furthermore, using MEX le interfaces, problems in the CUTE test problem data base and problems de ned in the AMPL modeling language can be solved.
Algorithms and software for convex mixed integer nonlinear programs, IMA Volumes
"... Abstract. This paper provides a survey of recent progress and software for solving convex mixed integer nonlinear programs (MINLP)s, where the objective and constraints are defined by convex functions and integrality restrictions are imposed on a subset of the decision variables. Convex MINLPs have ..."
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Cited by 11 (2 self)
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Abstract. This paper provides a survey of recent progress and software for solving convex mixed integer nonlinear programs (MINLP)s, where the objective and constraints are defined by convex functions and integrality restrictions are imposed on a subset of the decision variables. Convex MINLPs have received sustained attention in recent years. By exploiting analogies to wellknown techniques for solving mixed integer linear programs and incorporating these techniques into software, significant improvements have been made in the ability to solve these problems. Key words. Mixed Integer Nonlinear Programming; Branch and Bound; AMS(MOS) subject classifications.
Discrete Vt Assignment and Gate Sizing Using a SelfSnapping Continuous Formulation
"... AbstractThis paper presents a novel approach towards the simultaneous Vtassignment and gatesizing problem. This inherently discrete problem is formulated as a continuous problem, allowing it to be solved using any of several widely available and highly efficient nonlinear optimizers. We prove tha ..."
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Cited by 11 (0 self)
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AbstractThis paper presents a novel approach towards the simultaneous Vtassignment and gatesizing problem. This inherently discrete problem is formulated as a continuous problem, allowing it to be solved using any of several widely available and highly efficient nonlinear optimizers. We prove that, under our formulation, the optimal solution has discrete Vts assigned to almost every gate, thus eliminating the need for a sophisticated snapping heuristic. We show that this technique performs dualVt assignment and gate sizing in a very efficient manner. Compared to a sensitivity based method, we achieve average leakage savings of 31 % and average total power savings of 7.4 % with very efficient runtimes. 1.
The TOMLAB NLPLIB Toolbox for Nonlinear Programming. Advanced Modeling and Optimization
, 1999
"... The paper presents the toolbox NLPLIB TB 1.0 (NonLinear Programming LIBrary) � a set of Matlab solvers, test problems, graphical and computational utilities for unconstrained and constrained optimization, quadratic programming, unconstrained and constrained nonlinear least squares, boxbounded globa ..."
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Cited by 10 (7 self)
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The paper presents the toolbox NLPLIB TB 1.0 (NonLinear Programming LIBrary) � a set of Matlab solvers, test problems, graphical and computational utilities for unconstrained and constrained optimization, quadratic programming, unconstrained and constrained nonlinear least squares, boxbounded global optimization, global mixedinteger nonlinear programming, and exponential sum model tting. NLPLIB TB, like the toolbox OPERA TB for linear and discrete optimization, is a part of TOMLAB � an environment in Matlab for research and teaching in optimization. TOMLAB currently solves small and medium size dense problems. Presently, NLPLIB TB implements more than 25 solver algorithms, and it is possible to call solvers in the Matlab Optimization Toolbox. MEX le interfaces are prepared for seven Fortran and C solvers, and others are easily added using the same type of interface routines. Currently, MEX le interfaces have beendeveloped for MINOS, NPSOL, NPOPT, NLSSOL, LPOPT, QPOPT and LSSOL. There are four ways to solve a problem: by a direct call to the solver routine or a call to amultisolver driver routine, or interactively, using the Graphical
Steering Exact Penalty Methods for Nonlinear Programming
, 2007
"... This paper reviews, extends and analyzes a new class of penalty methods for nonlinear optimization. These methods adjust the penalty parameter dynamically; by controlling the degree of linear feasibility achieved at every iteration, they promote balanced progress toward optimality and feasibility. I ..."
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Cited by 10 (0 self)
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This paper reviews, extends and analyzes a new class of penalty methods for nonlinear optimization. These methods adjust the penalty parameter dynamically; by controlling the degree of linear feasibility achieved at every iteration, they promote balanced progress toward optimality and feasibility. In contrast with classical approaches, the choice of the penalty parameter ceases to be a heuristic and is determined, instead, by a subproblem with clearly defined objectives. The new penalty update strategy is presented in the context of sequential quadratic programming (SQP) and sequential linearquadratic programming (SLQP) methods that use trust regions to promote convergence. The paper concludes with a discussion of penalty parameters for merit functions used in line search methods.
The TOMLAB OPERA Toolbox for Linear and Discrete Optimization. Advanced Modeling and Optimization
, 1999
"... The Matlab toolbox OPERA TB is a set of Matlab m les, which solves basic linear and discrete optimization problems in operations research and mathematical programming. Included are routines for linear programming (LP), network programming (NP), integer programming (IP) and dynamic programming (DP). ..."
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Cited by 9 (8 self)
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The Matlab toolbox OPERA TB is a set of Matlab m les, which solves basic linear and discrete optimization problems in operations research and mathematical programming. Included are routines for linear programming (LP), network programming (NP), integer programming (IP) and dynamic programming (DP). OPERA TB, like the nonlinear programming toolbox NLPLIB TB, is a part of TOMLAB � an environment in Matlab for research and teaching in optimization. Linear programs are solved either by direct call to a solver routine or to a multisolver driver routine, or interactively, using the Graphical User Interface (GUI) or a menu system. From OPERA TB it is possible to call solvers in the Math Works Optimization Toolbox and, using a MEX le interface, generalpurpose solvers implemented in Fortran or C. The focus is on dense problems, but sparse linear programs may be solved using the commercial solver MINOS. Presently, OPERA TB implements about thirty algorithms and includes a set of test examples and demonstration les. This paper gives an overview of OPERA TB and presents test results for medium size LP problems. The tests show that the OPERA TB solver converges as fast as commercial Fortran solvers and is at least ve times faster than the simplex LP solver in the Optimization Toolbox 2.0andtwice as fast as the primaldual interiorpointLP solver in the same toolbox. Running the commercial Fortran solvers using MEX le interfaces gives a speedup factor of ve to thirty ve.
Multihour Design of MultiHop Virtual Path based WideArea ATM Networks
 IN 15TH INTERNATIONAL TELETRAFFIC CONGRESS  ITC 15
, 1997
"... The cost efficient design of ATM networks is considered to be a challenging problem due to the heterogenity of services, the statistical muliplexing gain resulting from the resource sharing of variable bitrate (VBR) connections and the possibility of resource separation through virtual paths (VPs) ..."
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Cited by 8 (0 self)
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The cost efficient design of ATM networks is considered to be a challenging problem due to the heterogenity of services, the statistical muliplexing gain resulting from the resource sharing of variable bitrate (VBR) connections and the possibility of resource separation through virtual paths (VPs). Most proposals in literature are well suited for small problems only. For that before developing a new algorithm we take a look at large scale design techniques known from telephone network dimensioning. It turns out that the well known Unified Algorithm (UA) of G. Ash [1] is a good basis for an ATM network design method. So we extend and improve the UA in order to cope with the following multihour multiservice ATM network design problem: Find the minimum cost VP network structure on top of a given physical network and the design hour individual optimal VC routing sequences according to endtoend call blocking constraints. Beside transmission costs also virtual path/virtual channe...
Assessing the Potential of Interior Methods for Nonlinear Optimization
, 2002
"... A series of numerical experiments with interior point (LOQO, KNITRO) and activeset sequential quadratic programming (SNOPT, filterSQP) codes are reported and analyzed. The tests were performed with small, mediumsize and moderately large problems, and are examined by problem classes. Detailed obser ..."
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Cited by 7 (1 self)
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A series of numerical experiments with interior point (LOQO, KNITRO) and activeset sequential quadratic programming (SNOPT, filterSQP) codes are reported and analyzed. The tests were performed with small, mediumsize and moderately large problems, and are examined by problem classes. Detailed observations on the performance of the codes, and several suggestions on how to improve them are presented. Overall, interior methods appear to be strong competitors of activeset SQP methods, but all codes show much room for improvement. 1
Computable General Equilibrium Analysis: Opening a Black Box
"... neueren Forschungsarbeiten des ZEW. Die Beiträge liegen in alleiniger Verantwortung der Autoren und stellen nicht notwendigerweise die Meinung des ZEW dar. Discussion Papers are intended to make results of ZEW research promptly available to other economists in order to encourage discussion and sugge ..."
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Cited by 7 (2 self)
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neueren Forschungsarbeiten des ZEW. Die Beiträge liegen in alleiniger Verantwortung der Autoren und stellen nicht notwendigerweise die Meinung des ZEW dar. Discussion Papers are intended to make results of ZEW research promptly available to other economists in order to encourage discussion and suggestions for revisions. The authors are solely responsible for the contents which do not necessarily represent the opinion of the ZEW. NonTechnical Summary Quantitative simulations to evaluate alternative policy measures play a key role in applied economic research. Numerical models accommodate the systematic analysis of economic problems where analytical solutions are either not available or do not provide adequate information. Compared to analytical models, the numerical approach facilitates the analysis of complex economic interactions and the impact assessment of structural policy changes. Among numerical methods, computable general equilibrium (CGE) models are widely employed by various national and international organizations (EU Commission, IMF, World Bank, OECD, etc.) for economic policy analysis at the sectorlevel as well as the economywide level. CGE analysis constitutes a powerful scientific method for the comprehensive exante simulation of adjustment effects induced by exogenous policy interference. The main virtue of the CGE approach is its
Newton methods for largescale linear inequalityconstrained minimization
 SIAM Journal on Optimization
, 1997
"... Abstract. Newton methods of the linesearch type for largescale minimization subject to linear inequality constraints are discussed. The purpose of the paper is twofold: (i) to give an active–settype method with the ability to delete multiple constraints simultaneously and (ii) to give a relatively ..."
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Cited by 7 (0 self)
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Abstract. Newton methods of the linesearch type for largescale minimization subject to linear inequality constraints are discussed. The purpose of the paper is twofold: (i) to give an active–settype method with the ability to delete multiple constraints simultaneously and (ii) to give a relatively short general convergence proof for such a method. It is also discussed how multiple constraints can be added simultaneously. The approach is an extension of a previous work by the same authors for equalityconstrained problems. It is shown how the search directions can be computed without the need to compute the reduced Hessian of the objective function. The convergence analysis states that every limit point of a sequence of iterates satisfies the secondorder necessary optimality conditions. Key words. linear inequalityconstrained minimization, negative curvature, modified Newton method, symmetric indefinite factorization, largescale minimization, linesearch method