The present paper is the result of a contemplative study of a multi-time control problem (MCP) by considering its associated equivalent auxiliary control problem (MCP)$ _{\varsigma} $ via the exact $ l_{1} $ penalty method. Further study reveals that the solution set of the considered problem and the auxiliary problem exhibits an equivalence under the KT-pseudoinvexity hypothesis. Moreover, the study is extended towards the saddle point defined for (MCP) to establish the relationship between the solution set of multi-time control problem (MCP) and its associated equivalent auxiliary control problem (MCP)$ _{\varsigma} $. Finally, we present an illustrative application to authenticate the results presented in this paper.
Citation: Preeti, Poonam Agarwal, Savin Treanţă, Kamsing Nonlaopon. Penalty approach for KT-pseudoinvex multidimensional variational control problems[J]. AIMS Mathematics, 2023, 8(3): 5687-5702. doi: 10.3934/math.2023286
The present paper is the result of a contemplative study of a multi-time control problem (MCP) by considering its associated equivalent auxiliary control problem (MCP)$ _{\varsigma} $ via the exact $ l_{1} $ penalty method. Further study reveals that the solution set of the considered problem and the auxiliary problem exhibits an equivalence under the KT-pseudoinvexity hypothesis. Moreover, the study is extended towards the saddle point defined for (MCP) to establish the relationship between the solution set of multi-time control problem (MCP) and its associated equivalent auxiliary control problem (MCP)$ _{\varsigma} $. Finally, we present an illustrative application to authenticate the results presented in this paper.
[1] | M. Arana-Jim$\acute{e}$nez, G. Ruiz-Garz$\acute{o}$n, A. Rufi$\acute{a}$n-Lizana, R. Osuna-G$\acute{o}$mez, Weak efficiency in multiobjective variational problems under generalized convexity, J. Global Optim., 52 (2012), 109–121. https://doi.org/10.1007/s10898-011-9689-y doi: 10.1007/s10898-011-9689-y |
[2] | T. Antczak, Exact penalty function method for mathematical programming problems involving invex functions, Eur. J. Oper. Res., 198 (2009), 29–36. https://doi.org/10.1016/j.ejor.2008.07.031 doi: 10.1016/j.ejor.2008.07.031 |
[3] | T. Antczak, The $l_{1}$ penalty function method for nonconvex differentiable optimization problems with inequality constraints, Asia-Pac. J. Oper. Res., 27 (2010), 1–18. https://doi.org/10.1142/S0217595910002855 doi: 10.1142/S0217595910002855 |
[4] | T. Antczak, The exact $l_{1}$ penalty function method for nonsmooth invex optimization problems, System Modelling and Optimization, 25th IFIP TC 7 Conference, CSMO 2011 Berlin, Germany, 2011. |
[5] | I. I. Eremin, The penalty method in convex programming, Doklady Akad. Nauk SSSR, 143 (1967), 748–751. https://doi.org/10.1007/BF01071708 doi: 10.1007/BF01071708 |
[6] | M. A. Hanson, On sufficiency of the Kuhn-Tucker conditions, J. Math. Anal. Appl., 80 (1981), 545–550. https://doi.org/10.1016/0022-247X(81)90123-2 doi: 10.1016/0022-247X(81)90123-2 |
[7] | A. Jayswal, Preeti, An exact $l_{1}$ penalty function method for multi-dimensional first-order PDE constrained control optimisation problem, Eur. J. Control, 52 (2020), 34–41. https://doi.org/10.1016/j.ejcon.2019.07.004 doi: 10.1016/j.ejcon.2019.07.004 |
[8] | A. Jayswal, Preeti, Saddle point criteria for multi-dimensional control optimisation problem involving first-order PDE constraints Int. J. Control, 94 (2021), 1567–1576. https://doi.org/10.1080/00207179.2019.1661523 doi: 10.1080/00207179.2019.1661523 |
[9] | V. Jeyakumar, Strong and weak invexity in mathematical programming, Math. Oper. Res., 55 (1985), 109–125. |
[10] | X. Jiang, S. Qin, X. Xue, A subgradient-based continuous-time algorithm for constrained distributed quadratic programming, J. Franklin I., 357 (2020), 5570–5590. https://doi.org/10.1016/j.jfranklin.2020.02.057 doi: 10.1016/j.jfranklin.2020.02.057 |
[11] | Y. Lin, Linear quadratic open-loop Stackelberg game for stochastic systems with Poisson jumps, J. Franklin I., 358 (2021), 5262–5280. https://doi.org/10.1016/j.jfranklin.2021.04.048 doi: 10.1016/j.jfranklin.2021.04.048 |
[12] | Y. Lin, W. Zhang, Pareto efficiency in the infinite horizon mean-field type cooperative stochastic differential game, J. Franklin I., 358 (2021), 5532–5551. https://doi.org/10.1016/j.jfranklin.2021.05.013 doi: 10.1016/j.jfranklin.2021.05.013 |
[13] | Ş. Mititelu, Optimality and duality for invex multi-time control problems with mixed constraints, J. Adv. Math. Stud., 2 (2009), 25–35. |
[14] | C. Nahak, S. Nanda, Duality for variational problems with pseudo-invexity, Optimization, 34 (1995), 365–371. https://doi.org/10.1080/02331939508844120 doi: 10.1080/02331939508844120 |
[15] | M. A. Noor, K. I. Noor, Some characterizations of strongly preinvex functions, J. Math. Anal. Appl., 316 (2006), 697–706. https://doi.org/10.1016/j.jmaa.2005.05.014 doi: 10.1016/j.jmaa.2005.05.014 |
[16] | V. A. de Oliveira, L. B. dos Santos, R. Osuna-G$\acute{o}$mez, M. A. Rojas-Medar, Optimality conditions for nonlinear infinite programming problems, Optim. Lett., 9 (2015), 1131–1147. https://doi.org/10.1007/s11590-014-0808-9 doi: 10.1007/s11590-014-0808-9 |
[17] | S. Treanţă, M. Arana-Jim$\acute{e}$nez, KT-pseudoinvex multi-time control problem, Optim. Control Appl. Meth., 39 (2018), 1291–1300. https://doi.org/10.1002/oca.2410 doi: 10.1002/oca.2410 |
[18] | S. Treanţă, M. Arana-Jiménez, On generalized KT-pseudoinvex control problems involving multiple integral functionals, Eur. J. Control, 43 (2018), 39–45. https://doi.org/10.1016/j.ejcon.2018.05.004 doi: 10.1016/j.ejcon.2018.05.004 |
[19] | S. Treanţă, Efficiency in generalized V-KT-pseudoinvex control problems, Int. J. Control, 93 (2020), 611–618. https://doi.org/10.1080/00207179.2018.1483082 doi: 10.1080/00207179.2018.1483082 |
[20] | T. Weir, B. Mond, Pre-invex functions in multiple objective optimization, J. Math. Anal. Appl., 136 (1988), 29–38. https://doi.org/10.1016/0022-247X(88)90113-8 doi: 10.1016/0022-247X(88)90113-8 |
[21] | W. I. Zangwill, Nonlinear programming via penalty functions, Manag. Sci., 13 (1967), 344–358. |