Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO
•We review the recent advances in global optimization for Mixed Integer Nonlinear Programming, MINLP.•We review the recent advances in global optimization for Constrained Derivative-Free optimization, CDFO.•We present theoretical contributions, software implementations and applications for both MINL...
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| Vydáno v: | European journal of operational research Ročník 252; číslo 3; s. 701 - 727 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
01.08.2016
Elsevier Sequoia S.A |
| Témata: | |
| ISSN: | 0377-2217, 1872-6860 |
| On-line přístup: | Získat plný text |
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| Abstract | •We review the recent advances in global optimization for Mixed Integer Nonlinear Programming, MINLP.•We review the recent advances in global optimization for Constrained Derivative-Free optimization, CDFO.•We present theoretical contributions, software implementations and applications for both MINLP and CDFO.•We discuss possible interactions between the two areas of MINLP and CDFO.•We present a complete test suite for MINLP and CDFO algorithms.
This manuscript reviews recent advances in deterministic global optimization for Mixed-Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free Optimization (CDFO). This work provides a comprehensive and detailed literature review in terms of significant theoretical contributions, algorithmic developments, software implementations and applications for both MINLP and CDFO. Both research areas have experienced rapid growth, with a common aim to solve a wide range of real-world problems. We show their individual prerequisites, formulations and applicability, but also point out possible points of interaction in problems which contain hybrid characteristics. Finally, an inclusive and complete test suite is provided for both MINLP and CDFO algorithms, which is useful for future benchmarking. |
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| AbstractList | This manuscript reviews recent advances in deterministic global optimization for Mixed-Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free Optimization (CDFO). This work provides a comprehensive and detailed literature review in terms of significant theoretical contributions, algorithmic developments, software implementations and applications for both MINLP and CDFO. Both research areas have experienced rapid growth, with a common aim to solve a wide range of real-world problems. We show their individual prerequisites, formulations and applicability, but also point out possible points of interaction in problems which contain hybrid characteristics. Finally, an inclusive and complete test suite is provided for both MINLP and CDFO algorithms, which is useful for future benchmarking. •We review the recent advances in global optimization for Mixed Integer Nonlinear Programming, MINLP.•We review the recent advances in global optimization for Constrained Derivative-Free optimization, CDFO.•We present theoretical contributions, software implementations and applications for both MINLP and CDFO.•We discuss possible interactions between the two areas of MINLP and CDFO.•We present a complete test suite for MINLP and CDFO algorithms. This manuscript reviews recent advances in deterministic global optimization for Mixed-Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free Optimization (CDFO). This work provides a comprehensive and detailed literature review in terms of significant theoretical contributions, algorithmic developments, software implementations and applications for both MINLP and CDFO. Both research areas have experienced rapid growth, with a common aim to solve a wide range of real-world problems. We show their individual prerequisites, formulations and applicability, but also point out possible points of interaction in problems which contain hybrid characteristics. Finally, an inclusive and complete test suite is provided for both MINLP and CDFO algorithms, which is useful for future benchmarking. |
| Author | Floudas, Christodoulos A. Misener, Ruth Boukouvala, Fani |
| Author_xml | – sequence: 1 givenname: Fani surname: Boukouvala fullname: Boukouvala, Fani organization: Texas A & M Energy Institute, Texas A & M University, 302 Williams Administration Building, College Station, TX 77843-1372, USA – sequence: 2 givenname: Ruth surname: Misener fullname: Misener, Ruth organization: Department of Computing,Imperial College London, South Kensington SW7 2AZ, UK – sequence: 3 givenname: Christodoulos A. surname: Floudas fullname: Floudas, Christodoulos A. email: floudas@princeton.edu, floudas@titan.princeton.edu, floudas@tamu.edu organization: Texas A & M Energy Institute, Texas A & M University, 302 Williams Administration Building, College Station, TX 77843-1372, USA |
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| CODEN | EJORDT |
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