Sequential optimality conditions for optimization problems with additional abstract set constraints

Authors

  • Nadia Soledad Fazzio CONICET, Departamento de Matemática, FCE, Universidad Nacional de La Plata, C.C. 172, 1900 La Plata, Argentina
  • María Daniela Sánchez Departamento de Matemática, FCE, Universidad Nacional de La Plata, C.C. 172, 1900 La Plata, Argentina
  • María Laura Schuverdt Departamento de Matemática, FCE, Universidad Nacional de La Plata, C.C. 172, 1900 La Plata, Argentina

DOI:

https://doi.org/10.33044/revuma.2260

Abstract

The positive approximate Karush–Kuhn–Tucker sequential condition and the strict constraint qualification associated with this condition for general scalar problems with equality and inequality constraints have recently been introduced. In this paper, we extend them to optimization problems with additional abstract set constraints. We also present an extension of the approximate Karush–Kuhn–Tucker sequential condition and its related strict constraint qualification. Furthermore, we explore the relations between the new constraint qualification and other constraint qualifications known in the literature as Abadie, quasi-normality and the approximate Karush–Kuhn–Tucker regularity constraint qualification. Finally, we introduce an augmented Lagrangian method for solving the optimization problem with abstract set constraints and we show that it is possible to obtain global convergence under the new condition.

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References

J. Abadie, On the Kuhn-Tucker theorem, in Nonlinear Programming (NATO Summer School, Menton, 1964), North-Holland, Amsterdam, 1967, pp. 19–36.  MR  Zbl

P.-A. Absil, R. Mahony, and R. Sepulchre, Optimization algorithms on matrix manifolds, Princeton University Press, Princeton, NJ, 2008.  DOI  MR  Zbl

R. Andreani, E. G. Birgin, J. M. Martínez, and M. L. Schuverdt, On augmented Lagrangian methods with general lower-level constraints, SIAM J. Optim. 18 no. 4 (2007), 1286–1309.  DOI  MR  Zbl

R. Andreani, E. G. Birgin, J. M. Martínez, and M. L. Schuverdt, Augmented Lagrangian methods under the constant positive linear dependence constraint qualification, Math. Program. 111 no. 1-2, Ser. B (2008), 5–32.  DOI  MR  Zbl

R. Andreani, N. S. Fazzio, M. L. Schuverdt, and L. D. Secchin, A sequential optimality condition related to the quasi-normality constraint qualification and its algorithmic consequences, SIAM J. Optim. 29 no. 1 (2019), 743–766.  DOI  MR  Zbl

R. Andreani, W. Gómez, G. Haeser, L. M. Mito, and A. Ramos, On optimality conditions for nonlinear conic programming, Math. Oper. Res. 47 no. 3 (2022), 2160–2185.  DOI  MR  Zbl

R. Andreani, G. Haeser, M. L. Schuverdt, L. D. Secchin, and P. J. S. Silva, On scaled stopping criteria for a safeguarded augmented Lagrangian method with theoretical guarantees, Math. Program. Comput. 14 no. 1 (2022), 121–146.  DOI  MR  Zbl

R. Andreani, G. Haeser, L. D. Secchin, and P. J. S. Silva, New sequential optimality conditions for mathematical programs with complementarity constraints and algorithmic consequences, SIAM J. Optim. 29 no. 4 (2019), 3201–3230.  DOI  MR  Zbl

R. Andreani, G. Haeser, and J. M. Martínez, On sequential optimality conditions for smooth constrained optimization, Optimization 60 no. 5 (2011), 627–641.  DOI  MR  Zbl

R. Andreani, G. Haeser, M. L. Schuverdt, and P. J. S. Silva, A relaxed constant positive linear dependence constraint qualification and applications, Math. Program. 135 no. 1-2, Ser. A (2012), 255–273.  DOI  MR  Zbl

R. Andreani, J. M. Martínez, A. Ramos, and P. J. S. Silva, A cone-continuity constraint qualification and algorithmic consequences, SIAM J. Optim. 26 no. 1 (2016), 96–110.  DOI  MR  Zbl

R. Andreani, J. M. Martinez, and M. L. Schuverdt, On the relation between constant positive linear dependence condition and quasinormality constraint qualification, J. Optim. Theory Appl. 125 no. 2 (2005), 473–485.  DOI  MR  Zbl

R. Andreani, J. M. Martínez, and B. F. Svaiter, A new sequential optimality condition for constrained optimization and algorithmic consequences, SIAM J. Optim. 20 no. 6 (2010), 3533–3554.  DOI  MR  Zbl

R. Andreani, A. Ramos, A. A. Ribeiro, L. D. Secchin, and A. R. Velazco, On the convergence of augmented Lagrangian strategies for nonlinear programming, IMA J. Numer. Anal. 42 no. 2 (2022), 1735–1765.  DOI  MR  Zbl

R. Bergmann and R. Herzog, Intrinsic formulation of KKT conditions and constraint qualifications on smooth manifolds, SIAM J. Optim. 29 no. 4 (2019), 2423–2444.  DOI  MR  Zbl

D. P. Bertsekas, Nonlinear programming, second ed., Athena Scientific, Belmont, MA, 1999.  MR  Zbl

D. P. Bertsekas, A. Nedić, and A. E. Ozdaglar, Convex analysis and optimization, Athena Scientific, Belmont, MA, 2003.  MR  Zbl

D. P. Bertsekas and A. E. Ozdaglar, Pseudonormality and a Lagrange multiplier theory for constrained optimization, J. Optim. Theory Appl. 114 no. 2 (2002), 287–343.  DOI  MR  Zbl

E. G. Birgin and J. M. Martínez, A box-constrained optimization algorithm with negative curvature directions and spectral projected gradients, in Topics in numerical analysis, Comput. Suppl. 15, Springer, Vienna, 2001, pp. 49–60.  DOI  MR  Zbl

E. G. Birgin and J. M. Martínez, Large-scale active-set box-constrained optimization method with spectral projected gradients, Comput. Optim. Appl. 23 no. 1 (2002), 101–125.  DOI  MR  Zbl

E. G. Birgin and J. M. Martínez, Practical augmented Lagrangian methods for constrained optimization, Fundamentals of Algorithms 10, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2014.  DOI  MR  Zbl

E. G. Birgin, J. M. Martínez, and M. Raydan, Nonmonotone spectral projected gradient methods on convex sets, SIAM J. Optim. 10 no. 4 (2000), 1196–1211.  DOI  MR  Zbl

E. G. Birgin, J. M. Martínez, and M. Raydan, Algorithm 813: SPG – software for convex-constrained optimization, ACM Trans. Math. Softw. 27 no. 3 (2001), 340–349.  DOI  Zbl

E. G. Birgin, J. M. Martínez, and M. Raydan, Inexact spectral projected gradient methods on convex sets, IMA J. Numer. Anal. 23 no. 4 (2003), 539–559.  DOI  MR  Zbl

E. G. Birgin, J. M. Martínez, and M. Raydan, Spectral projected gradient methods, in Encyclopedia of optimization, Springer, Boston, 2009, pp. 3652–3659.  DOI

E. Börgens, C. Kanzow, P. Mehlitz, and G. Wachsmuth, New constraint qualifications for optimization problems in Banach spaces based on asymptotic KKT conditions, SIAM J. Optim. 30 no. 4 (2020), 2956–2982.  DOI  MR  Zbl

L. F. Bueno, G. Haeser, F. Lara, and F. N. Rojas, An augmented Lagrangian method for quasi-equilibrium problems, Comput. Optim. Appl. 76 no. 3 (2020), 737–766.  DOI  MR  Zbl

L. F. Bueno, G. Haeser, and F. N. Rojas, Optimality conditions and constraint qualifications for generalized Nash equilibrium problems and their practical implications, SIAM J. Optim. 29 no. 1 (2019), 31–54.  DOI  MR  Zbl

J. Dutta, K. Deb, R. Tulshyan, and R. Arora, Approximate KKT points and a proximity measure for termination, J. Global Optim. 56 no. 4 (2013), 1463–1499.  DOI  MR  Zbl

W. Fenchel, Convex cones, sets and functions, Logistic Project Report, Department of Mathematics, Princeton University, 1953.  Zbl

M. L. Flegel, C. Kanzow, and J. V. Outrata, Optimality conditions for disjunctive programs with application to mathematical programs with equilibrium constraints, Set-Valued Anal. 15 no. 2 (2007), 139–162.  DOI  MR  Zbl

G. Giorgi, B. Jiménez, and V. Novo, Approximate Karush–Kuhn–Tucker condition in multiobjective optimization, J. Optim. Theory Appl. 171 no. 1 (2016), 70–89.  DOI  MR  Zbl

G. Haeser and M. L. Schuverdt, On approximate KKT condition and its extension to continuous variational inequalities, J. Optim. Theory Appl. 149 no. 3 (2011), 528–539.  DOI  MR  Zbl

M. R. Hestenes, Calculus of variations and optimal control theory, John Wiley & Sons, New York-London-Sydney, 1966.  MR  Zbl

M. R. Hestenes, Optimization theory: The finite dimensional case, Pure and Applied Mathematics, John Wiley & Sons, New York, 1975.  MR  Zbl

C. Kanzow, D. Steck, and D. Wachsmuth, An augmented Lagrangian method for optimization problems in Banach spaces, SIAM J. Control Optim. 56 no. 1 (2018), 272–291.  DOI  MR  Zbl

A. E. Ozdaglar and D. P. Bertsekas, The relation between pseudonormality and quasiregularity in constrained optimization, Optim. Methods Softw. 19 no. 5 (2004), 493–506.  DOI  MR  Zbl

L. Qi and Z. Wei, On the constant positive linear dependence condition and its application to SQP methods, SIAM J. Optim. 10 no. 4 (2000), 963–981.  DOI  MR  Zbl

A. Ramos, Two new weak constraint qualifications for mathematical programs with equilibrium constraints and applications, J. Optim. Theory Appl. 183 no. 2 (2019), 566–591.  DOI  MR  Zbl

A. Ramos, Mathematical programs with equilibrium constraints: a sequential optimality condition, new constraint qualifications and algorithmic consequences, Optim. Methods Softw. 36 no. 1 (2021), 45–81.  DOI  MR  Zbl

R. T. Rockafellar, Convex analysis, Princeton Mathematical Series, No. 28, Princeton University Press, Princeton, NJ, 1970.  MR  Zbl

R. T. Rockafellar and R. J.-B. Wets, Variational analysis, Grundlehren der mathematischen Wissenschaften 317, Springer-Verlag, Berlin, 1998.  DOI  MR  Zbl

N. V. Tuyen, Y.-B. Xiao, and T. Q. Son, On approximate KKT optimality conditions for cone-constrained vector optimization problems, J. Nonlinear Convex Anal. 21 no. 1 (2020), 105–117.  MR  Zbl

W. H. Yang, L.-H. Zhang, and R. Song, Optimality conditions for the nonlinear programming problems on Riemannian manifolds, Pac. J. Optim. 10 no. 2 (2014), 415–434.  MR  Zbl

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Published

2024-05-21

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