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Quadratic Maximization of Reachable Values of Affine Systems with Diagonalizable Matrix

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  • Assalé Adjé

    (Laboratoire de Mathématiques et Physique (LAMPS) Université de Perpignan Via Domitia)

Abstract

In this paper, we solve a maximization problem where the objective function is quadratic and convex or concave and the constraints set is the reachable values set of a convergent discrete-time affine system. Moreover, we assume that the matrix defining the system is diagonalizable. The difficulty of the problem lies in the treatment of infinite sequences belonging to the constraint set. Equivalently, the problem requires to solve an infinite number of quadratic programs. Therefore, the main idea is to extract a finite number of them and to guarantee that the resolution of the extracted problems provides the optimal value and a maximizer for the initial problem. The number of quadratic programs to solve has to be the smallest possible. Actually, we construct a family of integers that over-approximate the exact number of quadratic programs to solve using basic ideas of linear algebra. This family of integers is used in the final algorithm. A new computation of an integer of the family within the algorithm ensures a reduction of the number of iterations. The method proposed in the paper is illustrated on small academic examples. Finally, the algorithm is experimented on randomly generated instances of the problem.

Suggested Citation

  • Assalé Adjé, 2021. "Quadratic Maximization of Reachable Values of Affine Systems with Diagonalizable Matrix," Journal of Optimization Theory and Applications, Springer, vol. 189(1), pages 136-163, April.
    • Handle: RePEc:spr:joptap:v:189:y:2021:i:1:d:10.1007_s10957-021-01825-y
    DOI: 10.1007/s10957-021-01825-y
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    References listed on IDEAS

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    1. Hoang Tuy, 2016. "Nonconvex Quadratic Programming," Springer Optimization and Its Applications, in: Convex Analysis and Global Optimization, edition 2, chapter 0, pages 337-390, Springer.
    2. Waltraud Huyer & Arnold Neumaier, 2018. "MINQ8: general definite and bound constrained indefinite quadratic programming," Computational Optimization and Applications, Springer, vol. 69(2), pages 351-381, March.
    3. Da Tian, 2015. "An exterior point polynomial-time algorithm for convex quadratic programming," Computational Optimization and Applications, Springer, vol. 61(1), pages 51-78, May.
    4. Frank Curtis & Zheng Han & Daniel Robinson, 2015. "A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization," Computational Optimization and Applications, Springer, vol. 60(2), pages 311-341, March.
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