Algorithms: their complexity and efficiency (2nd ed.)
Algorithms: their complexity and efficiency (2nd ed.)
More test examples for nonlinear programming codes
More test examples for nonlinear programming codes
Constrained global optimization: algorithms and applications
Constrained global optimization: algorithms and applications
New computer methods for global optimization
New computer methods for global optimization
Global optimization
Pure adaptive search in global optimization
Mathematical Programming: Series A and B
Global optimization requires global information
Journal of Optimization Theory and Applications
Hesitant adaptive search for global optimisation
Mathematical Programming: Series A and B
Testing Unconstrained Optimization Software
ACM Transactions on Mathematical Software (TOMS)
Stochastic Methods for Practical Global Optimization
Journal of Global Optimization
Stochastic Global Optimization: Problem Classes and Solution Techniques
Journal of Global Optimization
Journal of Global Optimization
Journal of Global Optimization
A Combined Global & Local Search (CGLS) Approach to Global Optimization
Journal of Global Optimization
Nonlinear optimization with GAMS /LGO
Journal of Global Optimization
Calibrating artificial neural networks by global optimization
Expert Systems with Applications: An International Journal
Using performance profiles to evaluate preconditioners for iterative methods
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Global search perspectives for multiobjective optimization
Journal of Global Optimization
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The thorough evaluation of optimization algorithms and software demands devotion, time, (code development and hardware) resources, in addition to professional objectivity. This general remark is particularly valid with respect to global optimization (GO) software since GO literally encompasses "all" mathematical programming models. It is easy not only to fabricate very challenging test problems, but also to find realistic GO problems that pose a formidable task for any algorithm of today and of tomorrow.A report on computational experiments should ideally cover a large number of aspects: a detailed description and practical background of the models; earlier related work; solution approaches; algorithm implementations and their parameterization; hardware platforms, operating systems, and software environments; an exact description of all performance measures; report of successes and failures; analysis of solver parameterization effects; statistical characteristics for randomized problem-classes; and a summary of results (in text, tabular and/or graphical forms).An extensive inventory of classical NLP and GO test problems, as well as more recent (and often much harder) test suites have been suggested. This paper reviews several prominent test collections, discusses comparison issues, and presents illustrative numerical results. A second paper will perform a comparative study using ideas presented here, drawing also on discussions at the Stochastic Global Optimization Workshop (held in New Zealand, June 2001).