A first-order regularized approach to the order-value optimization problem.
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| Title: | A first-order regularized approach to the order-value optimization problem. |
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| Authors: | Álvarez, G. Q.1 (AUTHOR), Birgin, E. G.2 (AUTHOR) egbirgin@ime.usp.br |
| Source: | Optimization Methods & Software. Jun2025, Vol. 40 Issue 3, p650-674. 25p. |
| Subjects: | Optimization algorithms, Set functions, Parameter estimation, Problem solving |
| Abstract: | Minimization of the order-value function is part of a large family of problems involving functions whose value is calculated by sorting values from a set or subset of other functions. The order-value function has as particular cases the minimum and maximum functions of a set of functions and is well suited for applications involving robust estimation. In this paper, a first-order method with quadratic regularization to solve the problem of minimizing the order-value function is proposed. An optimality condition for the problem and theoretical results of iteration complexity and evaluation complexity for the proposed method are presented. The applicability of the problem and method to parameter estimation problems with outliers is illustrated. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Minimization of the order-value function is part of a large family of problems involving functions whose value is calculated by sorting values from a set or subset of other functions. The order-value function has as particular cases the minimum and maximum functions of a set of functions and is well suited for applications involving robust estimation. In this paper, a first-order method with quadratic regularization to solve the problem of minimizing the order-value function is proposed. An optimality condition for the problem and theoretical results of iteration complexity and evaluation complexity for the proposed method are presented. The applicability of the problem and method to parameter estimation problems with outliers is illustrated. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10556788 |
| DOI: | 10.1080/10556788.2025.2453111 |
