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Volumn , Issue , 2004, Pages

Tapenade: A tool for automatic differentiation of program

Author keywords

Adjoint algorithm; Adjoint code; Automatic differentiation; Gradient; Optimization

Indexed keywords

ADJOINT CODES; ADJOINTS; AUTOMATIC DIFFERENTIATIONS; DEVELOPMENT COSTS; DIRECTIONAL DERIVATIVE; INTERNAL ALGORITHMS; PROGRAM ANALYSIS; VECTOR FUNCTIONS;

EID: 84893474647     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (36)

References (11)
  • 4
    • 0242720588 scopus 로고    scopus 로고
    • Reverse automatic differentiation for optimum design: From adjoint state assembly to gradient computation
    • F. Courty, A. Dervieux, B. Koobus, and L. Hascoët. Reverse automatic differentiation for optimum design: From adjoint state assembly to gradient computation. Optimization Methods and Software, 18(5):615-627, 2003.
    • (2003) Optimization Methods and Software , vol.18 , Issue.5 , pp. 615-627
    • Courty, F.1    Dervieux, A.2    Koobus, B.3    Hascoët, L.4
  • 9
    • 33645491759 scopus 로고    scopus 로고
    • Automatic differentiation for optimum design, applied to sonic boom reduction
    • V. Kumar et al., editors Montreal, Canada, LNCS 2668 Springer
    • L. Hascoët, M. Vázquez, and A. Dervieux. Automatic differentiation for optimum design, applied to sonic boom reduction. In V. Kumar et al., editors, Proceedings of ICCSA '03, Montreal, Canada, LNCS 2668, pages 85-94. Springer, 2003.
    • (2003) Proceedings of ICCSA '03 , pp. 85-94
    • Hascoët, L.1    Vázquez, M.2    Dervieux, A.3
  • 10
    • 0022842584 scopus 로고
    • Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects
    • F.-X. Le Dimet and O. Talagrand. Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus, 38A:97-110, 1986.
    • (1986) Tellus , vol.38 A , pp. 97-110
    • Le Dimet, F.-X.1    Talagrand, O.2
  • 11
    • 84886853228 scopus 로고    scopus 로고
    • Reducing the memory requirement in reverse mode automatic differentiation by solving TBR flow equations
    • LNCS. Springer
    • U. Naumann. Reducing the memory requirement in reverse mode automatic differentiation by solving TBR flow equations. In Proceedings of the ICCS 2000 Conference on Computational Science, Part II, LNCS. Springer, 2002.
    • (2002) Proceedings of the ICCS 2000 Conference on Computational Science, Part II
    • Naumann, U.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.