Numerical experiments with partially separable optimization problems View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1984

AUTHORS

A. Griewank , Ph. L. Toint

ABSTRACT

In this paper, we present some numerical experiments with an algorithm that uses the partial separability of an optimization problem. This research is motivated by the very large number of minimization problems in many variables having that particular property. The results discussed in the paper cover both unconstrained and bound constrained cases, as well as numerical estimation of gradient vectors. It is shown that exploiting the present underlying structure can lead to efficient algorithms, especially when the problem dimension is large. More... »

PAGES

203-220

References to SciGraph publications

Book

TITLE

Numerical Analysis

ISBN

978-3-540-13344-5
978-3-540-38881-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bfb0099526

DOI

http://dx.doi.org/10.1007/bfb0099526

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1022656138


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Numerical and Computational Mathematics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "familyName": "Griewank", 
        "givenName": "A.", 
        "id": "sg:person.0730474155.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0730474155.53"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Toint", 
        "givenName": "Ph. L.", 
        "id": "sg:person.011563043555.63", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011563043555.63"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf00934450", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013025894", 
          "https://doi.org/10.1007/bf00934450"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1015376096", 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-48320-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015376096", 
          "https://doi.org/10.1007/978-3-642-48320-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-48320-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015376096", 
          "https://doi.org/10.1007/978-3-642-48320-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00927440", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016331971", 
          "https://doi.org/10.1007/bf00927440"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00927440", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016331971", 
          "https://doi.org/10.1007/bf00927440"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01583777", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017260290", 
          "https://doi.org/10.1007/bf01583777"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01407874", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020894237", 
          "https://doi.org/10.1007/bf01407874"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1090/s0025-5718-1980-0559198-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026375948"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1090/s0025-5718-1977-0455338-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031059106"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01399316", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039339277", 
          "https://doi.org/10.1007/bf01399316"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01399316", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039339277", 
          "https://doi.org/10.1007/bf01399316"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00932218", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045975351", 
          "https://doi.org/10.1007/bf00932218"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/321371.321377", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049549026"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/imanum/1.4.403", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059688516"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/0320018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062843621"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/0716076", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062852637"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1984", 
    "datePublishedReg": "1984-01-01", 
    "description": "In this paper, we present some numerical experiments with an algorithm that uses the partial separability of an optimization problem. This research is motivated by the very large number of minimization problems in many variables having that particular property. The results discussed in the paper cover both unconstrained and bound constrained cases, as well as numerical estimation of gradient vectors. It is shown that exploiting the present underlying structure can lead to efficient algorithms, especially when the problem dimension is large.", 
    "editor": [
      {
        "familyName": "Griffiths", 
        "givenName": "David F.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/bfb0099526", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-13344-5", 
        "978-3-540-38881-4"
      ], 
      "name": "Numerical Analysis", 
      "type": "Book"
    }, 
    "name": "Numerical experiments with partially separable optimization problems", 
    "pagination": "203-220", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bfb0099526"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "6befb440c1658e42fff588c2c3135a7251a657d74e9e8c414a54865d2a170048"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1022656138"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/bfb0099526", 
      "https://app.dimensions.ai/details/publication/pub.1022656138"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T10:33", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8659_00000257.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/BFb0099526"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/bfb0099526'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/bfb0099526'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bfb0099526'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bfb0099526'


 

This table displays all metadata directly associated to this object as RDF triples.

115 TRIPLES      23 PREDICATES      41 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bfb0099526 schema:about anzsrc-for:01
2 anzsrc-for:0103
3 schema:author N1e87184eaf90439dbbabdf0c23283d51
4 schema:citation sg:pub.10.1007/978-3-642-48320-2
5 sg:pub.10.1007/bf00927440
6 sg:pub.10.1007/bf00932218
7 sg:pub.10.1007/bf00934450
8 sg:pub.10.1007/bf01399316
9 sg:pub.10.1007/bf01407874
10 sg:pub.10.1007/bf01583777
11 https://app.dimensions.ai/details/publication/pub.1015376096
12 https://doi.org/10.1090/s0025-5718-1977-0455338-4
13 https://doi.org/10.1090/s0025-5718-1980-0559198-2
14 https://doi.org/10.1093/imanum/1.4.403
15 https://doi.org/10.1137/0320018
16 https://doi.org/10.1137/0716076
17 https://doi.org/10.1145/321371.321377
18 schema:datePublished 1984
19 schema:datePublishedReg 1984-01-01
20 schema:description In this paper, we present some numerical experiments with an algorithm that uses the partial separability of an optimization problem. This research is motivated by the very large number of minimization problems in many variables having that particular property. The results discussed in the paper cover both unconstrained and bound constrained cases, as well as numerical estimation of gradient vectors. It is shown that exploiting the present underlying structure can lead to efficient algorithms, especially when the problem dimension is large.
21 schema:editor N63c281b2403b43c8a35aefdd6fc84dae
22 schema:genre chapter
23 schema:inLanguage en
24 schema:isAccessibleForFree false
25 schema:isPartOf N3c4a1cff5f0f451b89bc995c6febeb07
26 schema:name Numerical experiments with partially separable optimization problems
27 schema:pagination 203-220
28 schema:productId N2791e51c583143a5ad5da31a89225af1
29 N6071ac374b6c40c1beeead65e7f90a8f
30 Nc7e768cae5764cfeb774fdf6b6c66412
31 schema:publisher N331c7b4ac14a48e08d0a2109b076049b
32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022656138
33 https://doi.org/10.1007/bfb0099526
34 schema:sdDatePublished 2019-04-15T10:33
35 schema:sdLicense https://scigraph.springernature.com/explorer/license/
36 schema:sdPublisher Ncb2684bd36cb437ebd04c5d0e46ac5ea
37 schema:url http://link.springer.com/10.1007/BFb0099526
38 sgo:license sg:explorer/license/
39 sgo:sdDataset chapters
40 rdf:type schema:Chapter
41 N1e87184eaf90439dbbabdf0c23283d51 rdf:first sg:person.0730474155.53
42 rdf:rest Nf6e6b86555ef401d9fc50d4a5a9d7768
43 N2791e51c583143a5ad5da31a89225af1 schema:name doi
44 schema:value 10.1007/bfb0099526
45 rdf:type schema:PropertyValue
46 N331c7b4ac14a48e08d0a2109b076049b schema:location Berlin, Heidelberg
47 schema:name Springer Berlin Heidelberg
48 rdf:type schema:Organisation
49 N3c4a1cff5f0f451b89bc995c6febeb07 schema:isbn 978-3-540-13344-5
50 978-3-540-38881-4
51 schema:name Numerical Analysis
52 rdf:type schema:Book
53 N6071ac374b6c40c1beeead65e7f90a8f schema:name dimensions_id
54 schema:value pub.1022656138
55 rdf:type schema:PropertyValue
56 N63c281b2403b43c8a35aefdd6fc84dae rdf:first Nae064e3fe3cd4e9a8e3e655cdadea378
57 rdf:rest rdf:nil
58 Nae064e3fe3cd4e9a8e3e655cdadea378 schema:familyName Griffiths
59 schema:givenName David F.
60 rdf:type schema:Person
61 Nc7e768cae5764cfeb774fdf6b6c66412 schema:name readcube_id
62 schema:value 6befb440c1658e42fff588c2c3135a7251a657d74e9e8c414a54865d2a170048
63 rdf:type schema:PropertyValue
64 Ncb2684bd36cb437ebd04c5d0e46ac5ea schema:name Springer Nature - SN SciGraph project
65 rdf:type schema:Organization
66 Nf6e6b86555ef401d9fc50d4a5a9d7768 rdf:first sg:person.011563043555.63
67 rdf:rest rdf:nil
68 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
69 schema:name Mathematical Sciences
70 rdf:type schema:DefinedTerm
71 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
72 schema:name Numerical and Computational Mathematics
73 rdf:type schema:DefinedTerm
74 sg:person.011563043555.63 schema:familyName Toint
75 schema:givenName Ph. L.
76 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011563043555.63
77 rdf:type schema:Person
78 sg:person.0730474155.53 schema:familyName Griewank
79 schema:givenName A.
80 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0730474155.53
81 rdf:type schema:Person
82 sg:pub.10.1007/978-3-642-48320-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015376096
83 https://doi.org/10.1007/978-3-642-48320-2
84 rdf:type schema:CreativeWork
85 sg:pub.10.1007/bf00927440 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016331971
86 https://doi.org/10.1007/bf00927440
87 rdf:type schema:CreativeWork
88 sg:pub.10.1007/bf00932218 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045975351
89 https://doi.org/10.1007/bf00932218
90 rdf:type schema:CreativeWork
91 sg:pub.10.1007/bf00934450 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013025894
92 https://doi.org/10.1007/bf00934450
93 rdf:type schema:CreativeWork
94 sg:pub.10.1007/bf01399316 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039339277
95 https://doi.org/10.1007/bf01399316
96 rdf:type schema:CreativeWork
97 sg:pub.10.1007/bf01407874 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020894237
98 https://doi.org/10.1007/bf01407874
99 rdf:type schema:CreativeWork
100 sg:pub.10.1007/bf01583777 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017260290
101 https://doi.org/10.1007/bf01583777
102 rdf:type schema:CreativeWork
103 https://app.dimensions.ai/details/publication/pub.1015376096 schema:CreativeWork
104 https://doi.org/10.1090/s0025-5718-1977-0455338-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031059106
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1090/s0025-5718-1980-0559198-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026375948
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1093/imanum/1.4.403 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059688516
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1137/0320018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062843621
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1137/0716076 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062852637
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1145/321371.321377 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049549026
115 rdf:type schema:CreativeWork
 




Preview window. Press ESC to close (or click here)


...