The training phase is the most crucial stage during machine learning process. In case of labeled data and supervised learning, entails minimizing loss function under various constraints. We provide an innovative model for with numerous sets, resulting from application multicriteria optimization techniques to existing deep algorithms. Data fitting formulated as a in which each criterion measures...

Particular couplings between a scalar field and the Gauss-Bonnet invariant lead to spontaneous scalarization of black holes. Here we continue our work on simulating this phenomenon in context binary hole systems. We consider negative coupling for which black-hole spin plays major role process. find two main phenomena: (i) dynamical descalarization, initially scalarized holes form an unscalarize...

In this paper, by virtue of separation theorems convex sets and scalarization functions, some minimax inequalities are first considered. As applications, existence vector equilibrium problems with different order structures were also obtained.

Abstract A simple multi-physical system for the potential flow of a fluid through shroud, in which mechanical component, like turbine vane, is placed, modeled mathematically. We then consider multi-criteria shape optimization problem, where component allowed to vary under certain set second-order Hölder continuous differentiable transformations baseline with boundary same continuity class. As o...

Most evolutionary multi-objective optimization (EMO) methods use domination and nichepreserving principles in their selection operation to find a set of Pareto-optimal solutions in a single simulation run. However, classical generative multi-criterion optimization methods repeatedly solve a parameterized single-objective problem to achieve the same. Due to lack of parallelism in the classical g...

In this paper, we study risk-averse models for multicriteria optimization problems under uncertainty. We use a weighted sum-based scalarization and take a robust approach by considering a set of scalarization vectors to address the ambiguity and inconsistency in the relative weights of each criterion. We model the risk aversion of the decision makers via the concept of multivariate conditional ...

The spontaneous scalarization during the stellar core collapse in massive scalar-tensor theories of gravity introduces extra polarizations (on top plus and cross modes) gravitational waves, whose amplitudes are determined by several model parameters. Observations such scalarization-induced waveforms therefore offer valuable probes into these gravity. Considering a triple-scalar interactions the...