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- W2790453436 abstract "In multiobjective differentiable optimization under constraints, we choose to formulateall type of contraint as an equality constraint, usually nonlinear, possibly by the introduction ofa slack variable. Then a predictor-corrector method is proposed. At the predictor, the descentdirection is determined by the Multiple-Gradient Descent Algorithm (MGDA) applied to the costfunctiongradients projected onto the subspace locally tangent to all constraint surfaces. The stepsizeis controled to limit the violation of the nonlinear constraints and insure that all cost functionsdiminish. The corrector permits to restore the nonlinear constraints by a quasi-Newton-typemethod applied to a function agglomerating all the contraints in which the Hessian is approximatedby the sole terms in constraint gradients. This corrector constitutes a quasi-Riemannian approachthat reveals very efficient. Thus the predictor-corrector sequence constitutes one iteration of areduced-gradient descent method for constrained multiobjective optimization. Three classical testcasesare solved for illustration by means of the Inria MGDA software Platform." @default.
- W2790453436 created "2018-03-29" @default.
- W2790453436 creator A5058755589 @default.
- W2790453436 date "2018-03-21" @default.
- W2790453436 modified "2023-09-25" @default.
- W2790453436 title "Quasi-Riemannian Multiple Gradient Descent Algorithm for constrained multiobjective differential optimization" @default.
- W2790453436 hasPublicationYear "2018" @default.
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