IEEE

Stochastic Approximation Approaches to the Stochastic Variational Inequality Problem

Abstract: Stochastic approximation methods have been extensively studied in the literature for solving systems of stochastic equations and stochastic optimization problems where function values and ...
Nature

Approximation Theory and Asymptotic Methods

Approximation theory and asymptotic methods form a foundational framework that bridges classical ideas with modern numerical analysis, enabling researchers to obtain practical, near‐optimal solutions ...
Frontiers

A Diffusion Approximation and Numerical Methods for Adaptive Neuron Models with Stochastic ...

Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA Characterizing the spiking statistics of neurons receiving noisy synaptic input is a central problem ...
IEEE

Stochastic Approximation Approaches to the Stochastic Variational Inequality Problem

Abstract: Stochastic approximation methods have been extensively studied in the literature for solving systems of stochastic equations and stochastic optimization problems where function values and ...
Nature

Approximation Algorithms for the Traveling Salesman Problem

The travelling salesman problem (TSP) remains one of the most challenging NP‐hard problems in combinatorial optimisation, with significant implications for logistics, network design and route planning ...
Frontiers

Approximation Methods and Analytical Modeling Using Partial Differential Equations

Adequate mathematical modeling is the key to success for many real-world projects in engineering, medicine, and other applied areas. As soon as an appropriate mathematical model is developed, it can ...
World Scientific

Approximation and Regularisation Methods for Operator-Functional Equations

This book presents an overview of the most recent research and findings in the field of approximation and regularisation methods for operator-functional equations, and explores their applications in ...
Microsoft

Approximation Methods for Gaussian Process Regression

A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following ...