Global Optimization: Software, Test Problems, and Applications

Pintér, János D.

doi:10.1007/978-1-4757-5362-2_15

Cited by 85 publications

(54 citation statements)

References 40 publications

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“…For examples of benchmarking studies, in the ANN context we refer to Prechelt (1994), Hamm et al (2007); in the GO context, see e.g. Ali et al (2005), Khompatraporn et al (2005), Pintér (2002Pintér ( , 2007; with further topical references therein.…”

Section: Umerical Examples and Discussionmentioning

confidence: 99%

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Calibrating artificial neural networks by global optimization

Pintér

2012

Expert Systems with Applications

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An artificial neural network (ANN) is a computational model − implemented as a computer program − that is aimed at emulating the key features and operations of biological neural networks. ANNs are extensively used to model unknown or unspecified functional relationships between the input and output of a "black box" system. In order to apply such a generic procedure to actual decision problems, a key requirement is ANN training to minimize the discrepancy between modeled and measured system output. In this work, we consider ANN training as a (potentially) multi-modal optimization problem. To address this issue, we introduce a global optimization (GO) framework and corresponding GO software. The practical viability of the GO based approach is illustrated by finding close numerical approximations of (one-dimensional, but non-trivial) functions.

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Section: Umerical Examples and Discussionmentioning

confidence: 99%

“…For theoretical, algorithmic and implementation details not discussed here, we refer e.g. to Pintér (1996Pintér ( , 1997Pintér ( , 2002Pintér ( , 2005Pintér ( , 2007Pintér ( , 2009Pintér ( , 2010aPintér ( , 2010b, Pintér and Kampas (2005), Pintér et al (2006).…”

Section: The Lgo Software Package For Constrained Global-local Optimimentioning

confidence: 99%

Calibrating artificial neural networks by global optimization

Pintér

2012

Expert Systems with Applications

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“…It is currently available to use in conjunction with C and Fortran compiler platforms, several optimization modeling languages (AIMMS, AMPL, GAMS, MPL), and ISTC systems (Maple, Mathematica, and MATLAB via Tomlab). For details, consult for example Pintér (2001Pintér ( , 2002Pintér ( , 2005Pintér ( , 2007, as well as the platform-specific user manuals not cited here.…”

Section: Global Optimization Toolbox For Maplementioning

confidence: 99%

“…Hence, the need for global scope search immediately becomes clear (e.g., in a classroom environment, or for non-mathematician members of an interdisciplinary R&D team). Parameterized model-classes similar to (4)-and optionally equipped with a new randomized solution vector for each instance-can be put to good use also in testing global optimization software as suggested and done in Pintér (2002). …”

Section: Parameterized Optimization Problemsmentioning

confidence: 99%

Model development and optimization in interactive computing environments

Pintér

2008

cent.eur.j.oper.res.

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Integrated computing systems-such as Maple, Mathematica, and MATLAB-enable the development of "live" electronic documents that include explanatory text, calculations, custom programming (code development), visualization, and other features. As a result, teachers and students, researchers and practitioners can develop applications in a completely interactive format. Such e-documents can be put to good use in developing textbooks, lecture notes, assignments and presentations, as well as in the context of research and development (R&D) projects. The interactive approach accelerates and enhances the process of learning and research. To illustrate this approach, we discuss a nonlinear (global and local) optimization software product and a topical electronic book that support interactive model development and optimization in Maple. We highlight the key features of the e-book and the software, present illustrative examples, and point towards a range of scientific and engineering applications. Mathematics Subject Classification 65K30 · 90C05 · 90C31Keywords Advanced systems modeling and optimization · Interactive electronic documents for research and education · Integrated computing systems · Maple · Global Optimization Toolbox · Illustrative examples and applications

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“…Platform-specific software implementation versions, numerous illustrative applications and case studies are discussed e.g. in [18][19][20][21][22][23].…”

Section: A Brief Introduction To Lgomentioning

confidence: 99%

Benchmarking the LGO Solver Suite within the COCO Test Environment

Hatwagner

Pintér²

2014

Acta Tech Jaur

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Optimization problems often arise in the context of scientific-engineering research and practice. In many situations that require optimization, there is no need to develop new, highly customized software for a new problem, because there are readily usable optimization packages to choose from. Benchmarking and testing software environments can greatly assist the process of choosing an appropriate optimization tool. The benchmarking environments typically include a substantial collection of well-known and widely used test functions; they also offer a properly defined methodology to compare the solvers considered. One of these benchmarking environments is called COCO (an abbreviation that stands for "COmparing Continuous Optimizers"). COCO has been used at the annual BBOB (Black-Box Optimization Benchmarking) workshops. COCO assesses the capabilities of optimization solvers based on a given collection of test problems and evaluation criteria, under identical and fully reproducible circumstances. The goal of our present study is to benchmark the LGO (Lipschitz Global Optimizer) solver suite and to compare it to several other solvers using COCO.

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Global Optimization: Software, Test Problems, and Applications

Cited by 85 publications

References 40 publications

Calibrating artificial neural networks by global optimization

Calibrating artificial neural networks by global optimization

Model development and optimization in interactive computing environments

Benchmarking the LGO Solver Suite within the COCO Test Environment

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