• Aucun résultat trouvé

Direct-Search for a Class of Stochastic Min-Max Problems

N/A
N/A
Protected

Academic year: 2021

Partager "Direct-Search for a Class of Stochastic Min-Max Problems"

Copied!
12
0
0

Texte intégral

Loading

Figure

Figure 1: Zero-one loss for each method across classes. The term ”lr” stands for different learning rates used
Figure 2: Learning a discretized mixture of Gaussian processes using direct-search methods

Références

Documents relatifs

Despite the fact that numerous nonparametric techniques were developed for mixtures models, the widely used approach in applications of nonlinear mixed effects models is quite rough

The leftmost solutions are 4 times equal or better than solutions of the CARP using a bi-objective approach for the solution cost and 17 times better or equal for the

The range of this electron is of the order of the µm , much greater than the nm range of nuclear recoils, and as a consequence, this electron will produce more ionization than a

We present the results for a direct search for light gluinos through the appearance of η → 3π 0 with high transverse momentum in the vacuum tank of the NA48 experiment at CERN.

The essential idea underlying the proposed approach is to decompose the original nonconvex stochastic problem into a sequence of (simpler) deterministic subproblems. In this case,

• During regime 1, the searcher undergoes a simple diffusion process (or Brownian motion) with diffusion coefficient D, modelling the phase of intensive search and slow

Thus the proposed line search methods (12) and (14) generally yields faster convergence than state-of-the- art decreasing stepsizes (6) and are easier to implement than

To solve the global stage, consuming the most CPU time, the mechanical fields are decomposed in a wavelet basis and the equilibrium is solved only for the largest