Algorithms for nonlinear constraints that use lagrangian functions View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1978-12

AUTHORS

M. J. D. Powell

ABSTRACT

Lagrangian functions are the basis of many of the more successful methods for nonlinear constraints in optimization calculations. Sometimes they are used in conjunction with linear approximations to the constraints and sometimes penalty terms are included to allow the use of algorithms for unconstrained optimization. Much has been discovered about these techniques during the last eight years and this paper gives a view of the progress and understanding that has been achieved and its relevance to practical algorithms. A particular method is recommended that seems to be more powerful than the author believed to be possible at the beginning of 1976. More... »

PAGES

224-248

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf01588967

DOI

http://dx.doi.org/10.1007/bf01588967

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029148347


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": [
      {
        "affiliation": {
          "alternateName": "University of Cambridge", 
          "id": "https://www.grid.ac/institutes/grid.5335.0", 
          "name": [
            "University of Cambridge, Cambridge, United Kingdom"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Powell", 
        "givenName": "M. J. D.", 
        "id": "sg:person.07731545105.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07731545105.07"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/bf01584986", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003936565", 
          "https://doi.org/10.1007/bf01584986"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/comjnl/13.3.317", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007597686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01580366", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011263804", 
          "https://doi.org/10.1007/bf01580366"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01580366", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011263804", 
          "https://doi.org/10.1007/bf01580366"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00927673", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018609722", 
          "https://doi.org/10.1007/bf00927673"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00927673", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018609722", 
          "https://doi.org/10.1007/bf00927673"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0005-1098(76)90077-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018823760"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0005-1098(76)90077-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018823760"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01580443", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025428808", 
          "https://doi.org/10.1007/bf01580443"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01580443", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025428808", 
          "https://doi.org/10.1007/bf01580443"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01580138", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042457178", 
          "https://doi.org/10.1007/bf01580138"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01580138", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042457178", 
          "https://doi.org/10.1007/bf01580138"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01580395", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053302753", 
          "https://doi.org/10.1007/bf01580395"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf01580395", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053302753", 
          "https://doi.org/10.1007/bf01580395"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/imamat/15.3.319", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059684459"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/imamat/6.3.222", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059685647"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/0313030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062843188"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/1019005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062861067"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1978-12", 
    "datePublishedReg": "1978-12-01", 
    "description": "Lagrangian functions are the basis of many of the more successful methods for nonlinear constraints in optimization calculations. Sometimes they are used in conjunction with linear approximations to the constraints and sometimes penalty terms are included to allow the use of algorithms for unconstrained optimization. Much has been discovered about these techniques during the last eight years and this paper gives a view of the progress and understanding that has been achieved and its relevance to practical algorithms. A particular method is recommended that seems to be more powerful than the author believed to be possible at the beginning of 1976.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf01588967", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1047630", 
        "issn": [
          "0025-5610", 
          "1436-4646"
        ], 
        "name": "Mathematical Programming", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "14"
      }
    ], 
    "name": "Algorithms for nonlinear constraints that use lagrangian functions", 
    "pagination": "224-248", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "8adbc9cb9d0e32e6a3dd7b8862afed2951af213e0d3cfa68d1d2770ef7acd5a6"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf01588967"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1029148347"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf01588967", 
      "https://app.dimensions.ai/details/publication/pub.1029148347"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T20:08", 
    "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_8681_00000588.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007%2FBF01588967"
  }
]
 

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/bf01588967'

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/bf01588967'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf01588967'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf01588967'


 

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

103 TRIPLES      21 PREDICATES      39 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf01588967 schema:about anzsrc-for:01
2 anzsrc-for:0103
3 schema:author Nf7e10978a97e4174b9b12bc74f1160b4
4 schema:citation sg:pub.10.1007/bf00927673
5 sg:pub.10.1007/bf01580138
6 sg:pub.10.1007/bf01580366
7 sg:pub.10.1007/bf01580395
8 sg:pub.10.1007/bf01580443
9 sg:pub.10.1007/bf01584986
10 https://doi.org/10.1016/0005-1098(76)90077-7
11 https://doi.org/10.1093/comjnl/13.3.317
12 https://doi.org/10.1093/imamat/15.3.319
13 https://doi.org/10.1093/imamat/6.3.222
14 https://doi.org/10.1137/0313030
15 https://doi.org/10.1137/1019005
16 schema:datePublished 1978-12
17 schema:datePublishedReg 1978-12-01
18 schema:description Lagrangian functions are the basis of many of the more successful methods for nonlinear constraints in optimization calculations. Sometimes they are used in conjunction with linear approximations to the constraints and sometimes penalty terms are included to allow the use of algorithms for unconstrained optimization. Much has been discovered about these techniques during the last eight years and this paper gives a view of the progress and understanding that has been achieved and its relevance to practical algorithms. A particular method is recommended that seems to be more powerful than the author believed to be possible at the beginning of 1976.
19 schema:genre research_article
20 schema:inLanguage en
21 schema:isAccessibleForFree false
22 schema:isPartOf N6c26a4975a60474f9b68c73dab42bf88
23 Nba67da93077d46e58f57a31dc56f0cd6
24 sg:journal.1047630
25 schema:name Algorithms for nonlinear constraints that use lagrangian functions
26 schema:pagination 224-248
27 schema:productId N806cee6c4df14d17952c47303e6ebc1b
28 Ndae0222c27a34884991cbcadd04bbf6f
29 Nf18d1e421c914123b52a24eede306055
30 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029148347
31 https://doi.org/10.1007/bf01588967
32 schema:sdDatePublished 2019-04-10T20:08
33 schema:sdLicense https://scigraph.springernature.com/explorer/license/
34 schema:sdPublisher N14f2b4da48f1433d85f126b3dacf55e8
35 schema:url http://link.springer.com/10.1007%2FBF01588967
36 sgo:license sg:explorer/license/
37 sgo:sdDataset articles
38 rdf:type schema:ScholarlyArticle
39 N14f2b4da48f1433d85f126b3dacf55e8 schema:name Springer Nature - SN SciGraph project
40 rdf:type schema:Organization
41 N6c26a4975a60474f9b68c73dab42bf88 schema:issueNumber 1
42 rdf:type schema:PublicationIssue
43 N806cee6c4df14d17952c47303e6ebc1b schema:name dimensions_id
44 schema:value pub.1029148347
45 rdf:type schema:PropertyValue
46 Nba67da93077d46e58f57a31dc56f0cd6 schema:volumeNumber 14
47 rdf:type schema:PublicationVolume
48 Ndae0222c27a34884991cbcadd04bbf6f schema:name doi
49 schema:value 10.1007/bf01588967
50 rdf:type schema:PropertyValue
51 Nf18d1e421c914123b52a24eede306055 schema:name readcube_id
52 schema:value 8adbc9cb9d0e32e6a3dd7b8862afed2951af213e0d3cfa68d1d2770ef7acd5a6
53 rdf:type schema:PropertyValue
54 Nf7e10978a97e4174b9b12bc74f1160b4 rdf:first sg:person.07731545105.07
55 rdf:rest rdf:nil
56 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
57 schema:name Mathematical Sciences
58 rdf:type schema:DefinedTerm
59 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
60 schema:name Numerical and Computational Mathematics
61 rdf:type schema:DefinedTerm
62 sg:journal.1047630 schema:issn 0025-5610
63 1436-4646
64 schema:name Mathematical Programming
65 rdf:type schema:Periodical
66 sg:person.07731545105.07 schema:affiliation https://www.grid.ac/institutes/grid.5335.0
67 schema:familyName Powell
68 schema:givenName M. J. D.
69 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07731545105.07
70 rdf:type schema:Person
71 sg:pub.10.1007/bf00927673 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018609722
72 https://doi.org/10.1007/bf00927673
73 rdf:type schema:CreativeWork
74 sg:pub.10.1007/bf01580138 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042457178
75 https://doi.org/10.1007/bf01580138
76 rdf:type schema:CreativeWork
77 sg:pub.10.1007/bf01580366 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011263804
78 https://doi.org/10.1007/bf01580366
79 rdf:type schema:CreativeWork
80 sg:pub.10.1007/bf01580395 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053302753
81 https://doi.org/10.1007/bf01580395
82 rdf:type schema:CreativeWork
83 sg:pub.10.1007/bf01580443 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025428808
84 https://doi.org/10.1007/bf01580443
85 rdf:type schema:CreativeWork
86 sg:pub.10.1007/bf01584986 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003936565
87 https://doi.org/10.1007/bf01584986
88 rdf:type schema:CreativeWork
89 https://doi.org/10.1016/0005-1098(76)90077-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018823760
90 rdf:type schema:CreativeWork
91 https://doi.org/10.1093/comjnl/13.3.317 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007597686
92 rdf:type schema:CreativeWork
93 https://doi.org/10.1093/imamat/15.3.319 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059684459
94 rdf:type schema:CreativeWork
95 https://doi.org/10.1093/imamat/6.3.222 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059685647
96 rdf:type schema:CreativeWork
97 https://doi.org/10.1137/0313030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062843188
98 rdf:type schema:CreativeWork
99 https://doi.org/10.1137/1019005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062861067
100 rdf:type schema:CreativeWork
101 https://www.grid.ac/institutes/grid.5335.0 schema:alternateName University of Cambridge
102 schema:name University of Cambridge, Cambridge, United Kingdom
103 rdf:type schema:Organization
 




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


...