Sentence examples for general nonsmooth optimization from inspiring English sources

Such extensions allow us to express the KKT conditions and hence necessary optimality conditions for more general nonsmooth optimization problems with inequality and equality constraints.

Further, Soleimani-Damaneh considered Mond-Weir type and Wolfe type duals for a general nonsmooth optimization problem in Banach algebras.

Another approach is to try to solve the nonsmooth problem directly, using general nonsmooth optimization methods, for instance, the subgradient method [17].

Bundle methods [19 21] are among the most robust and reliable methods to solve general nonsmooth optimization problems, which can be considered stabilized variants of cutting-planes method [14, 15].

Our approach is to search for locally optimal solutions of a nonsmooth optimization problem that is built to incorporate minimization objectives and constraints for multiple plants.

Using smoothing approximate techniques, this kind of nonsmooth optimization problem can be transformed into a general unconstrained optimization problem, which can be solved by the proposed smoothing modified three-term conjugate gradient method.

Emphasis is put on how to i) formalize the related optimization problem: performance and robustness indicators, ii) perform an effective MIMO identification, and iii) fix an a priori control structure and directly optimize its parameters thanks to nonsmooth optimization solvers.

Novel existence and uniqueness results for the parametric sensitivity functions of a hybrid system show that parameter optimization of hybrid dynamic systems (including sequencing decisions) is in general nonsmooth, but also smooth in many important cases.

Solutions are derived by a nonsmooth optimization technique analytically and given in full generality.

If the sampling-projecting procedure fails to find a stabilizing controller, certain candidate controllers are then locally optimized by means of an iterative method of nonsmooth optimization.

We present a nonsmooth optimization method suited for the multidisk problem and more generally, for programs where the maximum of an infinite family of nonconvex maximum eigenvalue functions is minimized.