Results 81 to 90 of about 295,614 (312)
AnalyticsGlobal optimization is a fundamental tool for addressing complex and nonlinear problems across scientific and technological domains. The primary objective of this work is to enhance the efficiency, stability, and convergence speed of the Magnificent ...
Glykeria Kyrou +2 more
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A Stochastic Interpretation of the Parametrix Method
Ukrainian Mathematical Journal, 2023 UDC 519.21 We revisit, in a didactic manner and by using stochastic analysis, the parametrix method and its application to unbiased simulation. We consider, in particular, the case of one-dimensional diffusions without drift.
openaire +1 more source
Advanced Engineering Materials, EarlyView.This work studies the anisotropic behavior of circular notched tensile specimens of Ni‐base superalloy single crystals during high‐temperature tensile creep along [001], [110], and [111]. Correlative scale‐bridging imaging of specimens reveals early rupture along [110] because 1) plastic deformation proceeds faster at notch center; 2) more (brittle ...
Leonardo Agudo Jácome +6 more
wiley +1 more source
Exact Scenario Simulation for Selected Multi-dimensional Stochastic Processes [PDF]
Accurate scenario simulation methods for solutions of multi-dimensional stochastic differential equations find application in stochastic analysis, the statistics of stochastic processes and many other areas, for instance, in finance.
Eckhard Platen, Renata Rendek
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Exact scenario simulation for selected multi-dimensional stochastic processes [PDF]
, 2009 Accurate scenario simulation methods for solutions of multi - dimensional stochastic differential equations find application in stochastic analysis, the statistics of stochastic processes and many other areas, for instance, in finance.
Rendek, Renata +5 more
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FoundationsGlobal optimization represents a fundamental challenge in computer science and engineering, as it aims to identify high-quality solutions to problems spanning from moderate to extremely high dimensionality.
Glykeria Kyrou +2 more
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Semi-Stochastic Gradient Descent Methods
Frontiers in Applied Mathematics and Statistics, 2017 In this paper we study the problem of minimizing the average of a large number of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a single full gradient
Jakub Konečný, Peter Richtárik
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Advanced Functional Materials, EarlyView.An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone +11 more
wiley +1 more source
Inexact Tensor Methods and their Application to Stochastic Convex Optimization
, 2023 We propose general non-accelerated [The results for non-accelerated methods first appeared in December 2020 in the preprint (A. Agafonov, D. Kamzolov, P. Dvurechensky, and A.
Dvurechensky, Pavel +4 more
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Stochastic simulation in systems biology
Computational and Structural Biotechnology Journal, 2014 Natural systems are, almost by definition, heterogeneous: this can be either a boon or an obstacle to be overcome, depending on the situation. Traditionally, when constructing mathematical models of these systems, heterogeneity has typically been ignored,
Tamás Székely Jr., Kevin Burrage
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