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  • Source: Computational Optimization and Applications. Unidade: IME

    Assunto: OTIMIZAÇÃO NÃO LINEAR

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      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: 01 dez. 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 dez. 01 ] 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 dez. 01 ] 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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      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: 01 dez. 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 dez. 01 ] 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 dez. 01 ] 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: 01 dez. 2025.
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      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 dez. 01 ] 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 dez. 01 ] 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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      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: 01 dez. 2025.
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      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 dez. 01 ] 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 dez. 01 ] Available from: https://doi.org/10.1007/s10589-022-00389-5
  • 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: 01 dez. 2025. , 2020
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      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 dez. 01 ] 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 dez. 01 ] 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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    • ABNT

      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: 01 dez. 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 dez. 01 ] 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 dez. 01 ] 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: 01 dez. 2025.
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      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 dez. 01 ] 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 dez. 01 ] 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: 01 dez. 2025.
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      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 dez. 01 ] 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 dez. 01 ] 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: 01 dez. 2025.
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      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 dez. 01 ] 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 dez. 01 ] 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: 01 dez. 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 dez. 01 ] 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 dez. 01 ] 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: 01 dez. 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 dez. 01 ] 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 dez. 01 ] 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: 01 dez. 2025.
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      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 dez. 01 ] 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 dez. 01 ] 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: 01 dez. 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 dez. 01 ] 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 dez. 01 ] 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: 01 dez. 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 dez. 01 ] 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 dez. 01 ] Available from: https://doi.org/10.1023/A:1019928808826

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