LGO, an implementation of a Lipschitzian global optimization procedure: user's guide
Abstract: "Decision problems are frequently modelled by optimizing the value of a primary objective function under stated feasibility constraints. Specifically, we shall consider here the following global optimization problem: min f(x) subject to x [element of] D [subset of] R[superscript n]. W...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
1995
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| Schriftenreihe: | Centrum voor Wiskunde en Informatica <Amsterdam> / Afdeling Numerieke Wiskunde: Report NM
1995,22 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "Decision problems are frequently modelled by optimizing the value of a primary objective function under stated feasibility constraints. Specifically, we shall consider here the following global optimization problem: min f(x) subject to x [element of] D [subset of] R[superscript n]. We shall assume that in (GOP) f : D -> R is a continuous function, and D is a bounded, robust subset ('body') in the Euclidean n-space. In addition, the Lipschitz-continuity of f on D will also be postulated, when necessary. The above assumptions define a fairly general class of optimization problems, and typically reflect a paradigm in which a rather vaguely defined, 'large' search region is given on which a (potentially) multiextremal function f is minimized. It will also be assumed that the set of global solutions x* [subset of] D is, at most, countable. To solve (GOP), a general family of adaptive partition strategies can be introduced: consult Pintér (1992a, 1995) and references therein. Necessary and sufficient convergence conditions can be established: these lead to a unified view of numerous GO algorithms, permitting their straightforward generalization and various extensions to handle specific cases of (GOP). The present report discusses a Lipschitzian global optimization program system, for use in the workstation environment at CWI. Implementation aspects are detailed, numerical experience, existing and prospective applications are also highlighted. Application areas include, e.g., the following (Pintér, 1992b, 1995): general (Lipschitzian) nonlinear approximation, systems of nonlinear equations and inequalities, calibration (parameterization) of descriptive system models, data classification, general configuration design, aggregation of negotiated expert opinions, product/mixture design, 'black box' design and operation of engineering/environmental systems." |
| Beschreibung: | 20 S. |
Internformat
MARC
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| 520 | 3 | |a Abstract: "Decision problems are frequently modelled by optimizing the value of a primary objective function under stated feasibility constraints. Specifically, we shall consider here the following global optimization problem: min f(x) subject to x [element of] D [subset of] R[superscript n]. We shall assume that in (GOP) f : D -> R is a continuous function, and D is a bounded, robust subset ('body') in the Euclidean n-space. In addition, the Lipschitz-continuity of f on D will also be postulated, when necessary. The above assumptions define a fairly general class of optimization problems, and typically reflect a paradigm in which a rather vaguely defined, 'large' search region is given on which a (potentially) multiextremal function f is minimized. It will also be assumed that the set of global solutions x* [subset of] D is, at most, countable. To solve (GOP), a general family of adaptive partition strategies can be introduced: consult Pintér (1992a, 1995) and references therein. Necessary and sufficient convergence conditions can be established: these lead to a unified view of numerous GO algorithms, permitting their straightforward generalization and various extensions to handle specific cases of (GOP). The present report discusses a Lipschitzian global optimization program system, for use in the workstation environment at CWI. Implementation aspects are detailed, numerical experience, existing and prospective applications are also highlighted. Application areas include, e.g., the following (Pintér, 1992b, 1995): general (Lipschitzian) nonlinear approximation, systems of nonlinear equations and inequalities, calibration (parameterization) of descriptive system models, data classification, general configuration design, aggregation of negotiated expert opinions, product/mixture design, 'black box' design and operation of engineering/environmental systems." | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Computer simulation | |
| 650 | 4 | |a Mathematical optimization |x Data processing | |
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Datensatz im Suchindex
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| any_adam_object | |
| author | Pintér, János D. |
| author_GND | (DE-588)1192712978 |
| author_facet | Pintér, János D. |
| author_role | aut |
| author_sort | Pintér, János D. |
| author_variant | j d p jd jdp |
| building | Verbundindex |
| bvnumber | BV011033505 |
| ctrlnum | (OCoLC)35799548 (DE-599)BVBBV011033505 |
| format | Book |
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| id | DE-604.BV011033505 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T18:02:55Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007388407 |
| oclc_num | 35799548 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM |
| owner_facet | DE-91G DE-BY-TUM |
| physical | 20 S. |
| publishDate | 1995 |
| publishDateSearch | 1995 |
| publishDateSort | 1995 |
| record_format | marc |
| series2 | Centrum voor Wiskunde en Informatica <Amsterdam> / Afdeling Numerieke Wiskunde: Report NM |
| spelling | Pintér, János D. Verfasser (DE-588)1192712978 aut LGO, an implementation of a Lipschitzian global optimization procedure user's guide J. D. Pinter Amsterdam 1995 20 S. txt rdacontent n rdamedia nc rdacarrier Centrum voor Wiskunde en Informatica <Amsterdam> / Afdeling Numerieke Wiskunde: Report NM 1995,22 Abstract: "Decision problems are frequently modelled by optimizing the value of a primary objective function under stated feasibility constraints. Specifically, we shall consider here the following global optimization problem: min f(x) subject to x [element of] D [subset of] R[superscript n]. We shall assume that in (GOP) f : D -> R is a continuous function, and D is a bounded, robust subset ('body') in the Euclidean n-space. In addition, the Lipschitz-continuity of f on D will also be postulated, when necessary. The above assumptions define a fairly general class of optimization problems, and typically reflect a paradigm in which a rather vaguely defined, 'large' search region is given on which a (potentially) multiextremal function f is minimized. It will also be assumed that the set of global solutions x* [subset of] D is, at most, countable. To solve (GOP), a general family of adaptive partition strategies can be introduced: consult Pintér (1992a, 1995) and references therein. Necessary and sufficient convergence conditions can be established: these lead to a unified view of numerous GO algorithms, permitting their straightforward generalization and various extensions to handle specific cases of (GOP). The present report discusses a Lipschitzian global optimization program system, for use in the workstation environment at CWI. Implementation aspects are detailed, numerical experience, existing and prospective applications are also highlighted. Application areas include, e.g., the following (Pintér, 1992b, 1995): general (Lipschitzian) nonlinear approximation, systems of nonlinear equations and inequalities, calibration (parameterization) of descriptive system models, data classification, general configuration design, aggregation of negotiated expert opinions, product/mixture design, 'black box' design and operation of engineering/environmental systems." Datenverarbeitung Computer simulation Mathematical optimization Data processing Afdeling Numerieke Wiskunde: Report NM Centrum voor Wiskunde en Informatica <Amsterdam> 1995,22 (DE-604)BV010177152 1995,22 |
| spellingShingle | Pintér, János D. LGO, an implementation of a Lipschitzian global optimization procedure user's guide Datenverarbeitung Computer simulation Mathematical optimization Data processing |
| title | LGO, an implementation of a Lipschitzian global optimization procedure user's guide |
| title_auth | LGO, an implementation of a Lipschitzian global optimization procedure user's guide |
| title_exact_search | LGO, an implementation of a Lipschitzian global optimization procedure user's guide |
| title_full | LGO, an implementation of a Lipschitzian global optimization procedure user's guide J. D. Pinter |
| title_fullStr | LGO, an implementation of a Lipschitzian global optimization procedure user's guide J. D. Pinter |
| title_full_unstemmed | LGO, an implementation of a Lipschitzian global optimization procedure user's guide J. D. Pinter |
| title_short | LGO, an implementation of a Lipschitzian global optimization procedure |
| title_sort | lgo an implementation of a lipschitzian global optimization procedure user s guide |
| title_sub | user's guide |
| topic | Datenverarbeitung Computer simulation Mathematical optimization Data processing |
| topic_facet | Datenverarbeitung Computer simulation Mathematical optimization Data processing |
| volume_link | (DE-604)BV010177152 |
| work_keys_str_mv | AT pinterjanosd lgoanimplementationofalipschitzianglobaloptimizationprocedureusersguide |