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  • Source: Computational Optimization and Applications. Unidade: IME

    Assunto: OTIMIZAÇÃO NÃO LINEAR

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    • ABNT

      BIRGIN, Ernesto Julian Goldberg e HAESER, Gabriel e MARTÍNEZ, José Mário. Safeguarded augmented Lagrangian algorithms with scaled stopping criterion for the subproblems. Computational Optimization and Applications, v. 91, p. 491-509, 2025Tradução . . Disponível em: https://doi.org/10.1007/s10589-024-00572-w. Acesso em: 08 nov. 2025.
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      Birgin, E. J. G., Haeser, G., & Martínez, J. M. (2025). Safeguarded augmented Lagrangian algorithms with scaled stopping criterion for the subproblems. Computational Optimization and Applications, 91, 491-509. doi:10.1007/s10589-024-00572-w
    • NLM

      Birgin EJG, Haeser G, Martínez JM. Safeguarded augmented Lagrangian algorithms with scaled stopping criterion for the subproblems [Internet]. Computational Optimization and Applications. 2025 ; 91 491-509.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-024-00572-w
    • Vancouver

      Birgin EJG, Haeser G, Martínez JM. Safeguarded augmented Lagrangian algorithms with scaled stopping criterion for the subproblems [Internet]. Computational Optimization and Applications. 2025 ; 91 491-509.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-024-00572-w
  • Source: Computational Optimization and Applications. Unidade: IME

    Subjects: ANÁLISE CONVEXA, ÁLGEBRAS DE JORDAN

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    • ABNT

      ANDREANI, Roberto et al. Strong global convergence properties of algorithms for nonlinear symmetric cone programming. Computational Optimization and Applications, v. 91, p. 397-421, 2025Tradução . . Disponível em: https://doi.org/10.1007/s10589-024-00642-z. Acesso em: 08 nov. 2025.
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      Andreani, R., Haeser, G., Ramos, A., Santos, D. O., Secchin, L. D., & Serranoni, A. (2025). Strong global convergence properties of algorithms for nonlinear symmetric cone programming. Computational Optimization and Applications, 91, 397-421. doi:10.1007/s10589-024-00642-z
    • NLM

      Andreani R, Haeser G, Ramos A, Santos DO, Secchin LD, Serranoni A. Strong global convergence properties of algorithms for nonlinear symmetric cone programming [Internet]. Computational Optimization and Applications. 2025 ;91 397-421.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-024-00642-z
    • Vancouver

      Andreani R, Haeser G, Ramos A, Santos DO, Secchin LD, Serranoni A. Strong global convergence properties of algorithms for nonlinear symmetric cone programming [Internet]. Computational Optimization and Applications. 2025 ;91 397-421.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-024-00642-z
  • Source: Computational Optimization and Applications. Unidade: IME

    Subjects: INTERPOLAÇÃO, MÉTODOS ITERATIVOS, APROXIMAÇÃO POR MÍNIMOS QUADRADOS, MÉTODOS NUMÉRICOS

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      BIRGIN, Ernesto Julian Goldberg e MARTÍNEZ, José Mário. Accelerated derivative-free nonlinear least-squares applied to the estimation of Manning coefficients. Computational Optimization and Applications, v. 81, p. 689–715, 2022Tradução . . Disponível em: https://doi.org/10.1007/s10589-021-00344-w. Acesso em: 08 nov. 2025.
    • APA

      Birgin, E. J. G., & Martínez, J. M. (2022). Accelerated derivative-free nonlinear least-squares applied to the estimation of Manning coefficients. Computational Optimization and Applications, 81, 689–715. doi:10.1007/s10589-021-00344-w
    • NLM

      Birgin EJG, Martínez JM. Accelerated derivative-free nonlinear least-squares applied to the estimation of Manning coefficients [Internet]. Computational Optimization and Applications. 2022 ; 81 689–715.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-021-00344-w
    • Vancouver

      Birgin EJG, Martínez JM. Accelerated derivative-free nonlinear least-squares applied to the estimation of Manning coefficients [Internet]. Computational Optimization and Applications. 2022 ; 81 689–715.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-021-00344-w
  • Source: Computational Optimization and Applications. Unidade: IME

    Subjects: PROGRAMAÇÃO NÃO LINEAR, MÉTODOS NUMÉRICOS, PROGRAMAÇÃO MATEMÁTICA

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    • ABNT

      BIRGIN, Ernesto Julian Goldberg e MARTÍNEZ, José Mário. Block coordinate descent for smooth nonconvex constrained minimization. Computational Optimization and Applications, v. 83, p. 1-27, 2022Tradução . . Disponível em: https://doi.org/10.1007/s10589-022-00389-5. Acesso em: 08 nov. 2025.
    • APA

      Birgin, E. J. G., & Martínez, J. M. (2022). Block coordinate descent for smooth nonconvex constrained minimization. Computational Optimization and Applications, 83, 1-27. doi:10.1007/s10589-022-00389-5
    • NLM

      Birgin EJG, Martínez JM. Block coordinate descent for smooth nonconvex constrained minimization [Internet]. Computational Optimization and Applications. 2022 ; 83 1-27.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-022-00389-5
    • Vancouver

      Birgin EJG, Martínez JM. Block coordinate descent for smooth nonconvex constrained minimization [Internet]. Computational Optimization and Applications. 2022 ; 83 1-27.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-022-00389-5
  • Source: Computational Optimization and Applications. Unidade: IME

    Subjects: PROGRAMAÇÃO NÃO LINEAR, PROGRAMAÇÃO MATEMÁTICA, MÉTODOS NUMÉRICOS

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    • ABNT

      ANDREANI, Roberto et al. On the use of Jordan Algebras for improving global convergence of an Augmented Lagrangian method in nonlinear semidefinite programming. Computational Optimization and Applications, v. 79, p. 633-648, 2021Tradução . . Disponível em: https://doi.org/10.1007/s10589-021-00281-8. Acesso em: 08 nov. 2025.
    • APA

      Andreani, R., Fukuda, E. H., Haeser, G., Santos, D. O., & Secchin, L. D. (2021). On the use of Jordan Algebras for improving global convergence of an Augmented Lagrangian method in nonlinear semidefinite programming. Computational Optimization and Applications, 79, 633-648. doi:10.1007/s10589-021-00281-8
    • NLM

      Andreani R, Fukuda EH, Haeser G, Santos DO, Secchin LD. On the use of Jordan Algebras for improving global convergence of an Augmented Lagrangian method in nonlinear semidefinite programming [Internet]. Computational Optimization and Applications. 2021 ; 79 633-648.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-021-00281-8
    • Vancouver

      Andreani R, Fukuda EH, Haeser G, Santos DO, Secchin LD. On the use of Jordan Algebras for improving global convergence of an Augmented Lagrangian method in nonlinear semidefinite programming [Internet]. Computational Optimization and Applications. 2021 ; 79 633-648.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-021-00281-8
  • Source: Computational Optimization and Applications. Conference titles: Brazilian Workshop on Continuous Optimization. Unidade: IME

    Assunto: PROGRAMAÇÃO MATEMÁTICA

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    • ABNT

      BUENO, L. F et al. An Augmented Lagrangian method for quasi-equilibrium problems. Computational Optimization and Applications. New York: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/s10589-020-00180-4. Acesso em: 08 nov. 2025. , 2020
    • APA

      Bueno, L. F., Haeser, G., Lara, F., & Rojas, F. N. (2020). An Augmented Lagrangian method for quasi-equilibrium problems. Computational Optimization and Applications. New York: Instituto de Matemática e Estatística, Universidade de São Paulo. doi:10.1007/s10589-020-00180-4
    • NLM

      Bueno LF, Haeser G, Lara F, Rojas FN. An Augmented Lagrangian method for quasi-equilibrium problems [Internet]. Computational Optimization and Applications. 2020 ; 76( 3): 737-766.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-020-00180-4
    • Vancouver

      Bueno LF, Haeser G, Lara F, Rojas FN. An Augmented Lagrangian method for quasi-equilibrium problems [Internet]. Computational Optimization and Applications. 2020 ; 76( 3): 737-766.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-020-00180-4
  • Source: Computational Optimization and Applications. Conference titles: Brazilian Workshop on Continuous Optimization. Unidade: IME

    Assunto: PROGRAMAÇÃO MATEMÁTICA

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      BUENO, Luís Felipe e HAESER, Gabriel e SANTOS, Luiz-Rafael. Towards an efficient augmented Lagrangian method for convex quadratic programming. Computational Optimization and Applications. New York: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/s10589-019-00161-2. Acesso em: 08 nov. 2025. , 2020
    • APA

      Bueno, L. F., Haeser, G., & Santos, L. -R. (2020). Towards an efficient augmented Lagrangian method for convex quadratic programming. Computational Optimization and Applications. New York: Instituto de Matemática e Estatística, Universidade de São Paulo. doi:10.1007/s10589-019-00161-2
    • NLM

      Bueno LF, Haeser G, Santos L-R. Towards an efficient augmented Lagrangian method for convex quadratic programming [Internet]. Computational Optimization and Applications. 2020 ; 76( 3): 767-800.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-019-00161-2
    • Vancouver

      Bueno LF, Haeser G, Santos L-R. Towards an efficient augmented Lagrangian method for convex quadratic programming [Internet]. Computational Optimization and Applications. 2020 ; 76( 3): 767-800.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-019-00161-2
  • Source: Computational Optimization and Applications. Unidade: IME

    Subjects: OTIMIZAÇÃO MATEMÁTICA, PROGRAMAÇÃO MATEMÁTICA

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      BIRGIN, Ernesto Julian Goldberg e MARTINEZ, José Mario. A Newton-like method with mixed factorizations and cubic regularization for unconstrained minimization. Computational Optimization and Applications, v. 73, n. 3, p. 707-753, 2019Tradução . . Disponível em: https://doi.org/10.1007/s10589-019-00089-7. Acesso em: 08 nov. 2025.
    • APA

      Birgin, E. J. G., & Martinez, J. M. (2019). A Newton-like method with mixed factorizations and cubic regularization for unconstrained minimization. Computational Optimization and Applications, 73( 3), 707-753. doi:10.1007/s10589-019-00089-7
    • NLM

      Birgin EJG, Martinez JM. A Newton-like method with mixed factorizations and cubic regularization for unconstrained minimization [Internet]. Computational Optimization and Applications. 2019 ; 73( 3): 707-753.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-019-00089-7
    • Vancouver

      Birgin EJG, Martinez JM. A Newton-like method with mixed factorizations and cubic regularization for unconstrained minimization [Internet]. Computational Optimization and Applications. 2019 ; 73( 3): 707-753.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-019-00089-7
  • Source: Computational Optimization and Applications. Unidade: ICMC

    Subjects: FUNÇÕES ESPECIAIS, APROXIMAÇÃO, MÉTODOS NUMÉRICOS DE OTIMIZAÇÃO, MÉTODOS ITERATIVOS, OTIMIZAÇÃO MATEMÁTICA, OTIMIZAÇÃO GLOBAL, OTIMIZAÇÃO IRRESTRITA, OTIMIZAÇÃO CONVEXA, OTIMIZAÇÃO ESTOCÁSTICA

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      HELOU, Elias Salomão e SANTOS, Sandra A. e SIMÕES, Lucas E. A. A fast gradient and function sampling method for finite-max functions. Computational Optimization and Applications, v. 71, n. 3, p. 673-717, 2018Tradução . . Disponível em: https://doi.org/10.1007/s10589-018-0030-2. Acesso em: 08 nov. 2025.
    • APA

      Helou, E. S., Santos, S. A., & Simões, L. E. A. (2018). A fast gradient and function sampling method for finite-max functions. Computational Optimization and Applications, 71( 3), 673-717. doi:10.1007/s10589-018-0030-2
    • NLM

      Helou ES, Santos SA, Simões LEA. A fast gradient and function sampling method for finite-max functions [Internet]. Computational Optimization and Applications. 2018 ; 71( 3): 673-717.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-018-0030-2
    • Vancouver

      Helou ES, Santos SA, Simões LEA. A fast gradient and function sampling method for finite-max functions [Internet]. Computational Optimization and Applications. 2018 ; 71( 3): 673-717.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-018-0030-2
  • Source: Computational Optimization and Applications. Unidade: IME

    Assunto: PROGRAMAÇÃO NÃO LINEAR

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      BIRGIN, Ernesto Julian Goldberg e HAESER, Gabriel e RAMOS, Alberto. Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points. Computational Optimization and Applications, v. 69, n. 1, p. 51–75, 2018Tradução . . Disponível em: https://doi.org/10.1007/s10589-017-9937-2. Acesso em: 08 nov. 2025.
    • APA

      Birgin, E. J. G., Haeser, G., & Ramos, A. (2018). Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points. Computational Optimization and Applications, 69( 1), 51–75. doi:10.1007/s10589-017-9937-2
    • NLM

      Birgin EJG, Haeser G, Ramos A. Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points [Internet]. Computational Optimization and Applications. 2018 ; 69( 1): 51–75.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-017-9937-2
    • Vancouver

      Birgin EJG, Haeser G, Ramos A. Augmented Lagrangians with constrained subproblems and convergence to second-order stationary points [Internet]. Computational Optimization and Applications. 2018 ; 69( 1): 51–75.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-017-9937-2
  • Source: Computational Optimization and Applications. Unidade: IME

    Subjects: PROGRAMAÇÃO NÃO LINEAR, OTIMIZAÇÃO MATEMÁTICA, PESQUISA OPERACIONAL, PROGRAMAÇÃO MATEMÁTICA

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      BIRGIN, Ernesto Julian Goldberg e BUENO, L. F e MARTINEZ, José Mario. Sequential equality-constrained optimization for nonlinear programming. Computational Optimization and Applications, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10589-016-9849-6. Acesso em: 08 nov. 2025.
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      Birgin, E. J. G., Bueno, L. F., & Martinez, J. M. (2016). Sequential equality-constrained optimization for nonlinear programming. Computational Optimization and Applications. doi:10.1007/s10589-016-9849-6
    • NLM

      Birgin EJG, Bueno LF, Martinez JM. Sequential equality-constrained optimization for nonlinear programming [Internet]. Computational Optimization and Applications. 2016 ;[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-016-9849-6
    • Vancouver

      Birgin EJG, Bueno LF, Martinez JM. Sequential equality-constrained optimization for nonlinear programming [Internet]. Computational Optimization and Applications. 2016 ;[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-016-9849-6
  • Source: Computational Optimization and Applications. Unidade: IME

    Assunto: PROGRAMAÇÃO NÃO LINEAR

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      BIRGIN, Ernesto Julian Goldberg e MARTINEZ, J. M. Augmented Lagrangian method with nonmonotone penalty parameters for constrained optimization. Computational Optimization and Applications, v. 51, n. 3, p. 941-965, 2012Tradução . . Disponível em: https://doi.org/10.1007/s10589-011-9396-0. Acesso em: 08 nov. 2025.
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      Birgin, E. J. G., & Martinez, J. M. (2012). Augmented Lagrangian method with nonmonotone penalty parameters for constrained optimization. Computational Optimization and Applications, 51( 3), 941-965. doi:10.1007/s10589-011-9396-0
    • NLM

      Birgin EJG, Martinez JM. Augmented Lagrangian method with nonmonotone penalty parameters for constrained optimization [Internet]. Computational Optimization and Applications. 2012 ; 51( 3): 941-965.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-011-9396-0
    • Vancouver

      Birgin EJG, Martinez JM. Augmented Lagrangian method with nonmonotone penalty parameters for constrained optimization [Internet]. Computational Optimization and Applications. 2012 ; 51( 3): 941-965.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-011-9396-0
  • Source: Computational Optimization and Applications. Unidade: IME

    Assunto: PROGRAMAÇÃO NÃO LINEAR

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      BIRGIN, Ernesto Julian Goldberg e MARTÍNEZ, José Mário. Structured minimal-memory inexact quasi-Newton method and secant preconditioners for augmented Lagrangian optimization. Computational Optimization and Applications, v. 39, n. 1, p. 1-16, 2008Tradução . . Disponível em: https://doi.org/10.1007/s10589-007-9050-z. Acesso em: 08 nov. 2025.
    • APA

      Birgin, E. J. G., & Martínez, J. M. (2008). Structured minimal-memory inexact quasi-Newton method and secant preconditioners for augmented Lagrangian optimization. Computational Optimization and Applications, 39( 1), 1-16. doi:10.1007/s10589-007-9050-z
    • NLM

      Birgin EJG, Martínez JM. Structured minimal-memory inexact quasi-Newton method and secant preconditioners for augmented Lagrangian optimization [Internet]. Computational Optimization and Applications. 2008 ; 39( 1): 1-16.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-007-9050-z
    • Vancouver

      Birgin EJG, Martínez JM. Structured minimal-memory inexact quasi-Newton method and secant preconditioners for augmented Lagrangian optimization [Internet]. Computational Optimization and Applications. 2008 ; 39( 1): 1-16.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-007-9050-z
  • Source: Computational Optimization and Applications. Unidade: IME

    Assunto: PROGRAMAÇÃO NÃO LINEAR

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      BIRGIN, Ernesto Julian Goldberg e CASTILLO, Romulo A e MARTINEZ, Jesus Manuel. Numerical comparison of Augmented Lagrangian algorithms for nonconvex problems. Computational Optimization and Applications, v. 31, n. 1, p. 31-55, 2005Tradução . . Disponível em: https://doi.org/10.1007/s10589-005-1066-7. Acesso em: 08 nov. 2025.
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      Birgin, E. J. G., Castillo, R. A., & Martinez, J. M. (2005). Numerical comparison of Augmented Lagrangian algorithms for nonconvex problems. Computational Optimization and Applications, 31( 1), 31-55. doi:10.1007/s10589-005-1066-7
    • NLM

      Birgin EJG, Castillo RA, Martinez JM. Numerical comparison of Augmented Lagrangian algorithms for nonconvex problems [Internet]. Computational Optimization and Applications. 2005 ; 31( 1): 31-55.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-005-1066-7
    • Vancouver

      Birgin EJG, Castillo RA, Martinez JM. Numerical comparison of Augmented Lagrangian algorithms for nonconvex problems [Internet]. Computational Optimization and Applications. 2005 ; 31( 1): 31-55.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1007/s10589-005-1066-7
  • Source: Computational Optimization and Applications. Unidade: IME

    Assunto: MÉTODOS NUMÉRICOS DE OTIMIZAÇÃO

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      BIRGIN, Ernesto Julian Goldberg e MARTÍNEZ, José Mário. Large-scale active-set box-constrained optimization method with spectral projected gradients. Computational Optimization and Applications, v. 23, n. 1, p. 101-125, 2002Tradução . . Disponível em: https://doi.org/10.1023/A:1019928808826. Acesso em: 08 nov. 2025.
    • APA

      Birgin, E. J. G., & Martínez, J. M. (2002). Large-scale active-set box-constrained optimization method with spectral projected gradients. Computational Optimization and Applications, 23( 1), 101-125. doi:10.1023/A:1019928808826
    • NLM

      Birgin EJG, Martínez JM. Large-scale active-set box-constrained optimization method with spectral projected gradients [Internet]. Computational Optimization and Applications. 2002 ; 23( 1): 101-125.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1023/A:1019928808826
    • Vancouver

      Birgin EJG, Martínez JM. Large-scale active-set box-constrained optimization method with spectral projected gradients [Internet]. Computational Optimization and Applications. 2002 ; 23( 1): 101-125.[citado 2025 nov. 08 ] Available from: https://doi.org/10.1023/A:1019928808826

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