Understand how approximation algorithms compute solutions that are guaranteed to be within some constant factor of the optimal solution. Develop a basic understanding of how linear and integer ...

In this study, we provide a direct comparison of the Stochastic Maximum Likelihood algorithm and Contrastive Divergence for training Restricted Boltzmann Machines using the MNIST data set. We ...

We give efficient approximation algorithms for embedding in this and several other cases. In particular, one type of extra information, an “extremum oracle,” can be guessed in quasipolynomial time, ...

A sequence of approximating convex programs are solved in each of these algorithms. Rosen's [7] inner approximation algorithm is a special case of the general inner approximation algorithm presented ...

When you need to solve a hard optimization problem, you might resort to using an approximation algorithm, which is a fast and efficient way to find a near-optimal solution. But how do you measure ...

Accurate and efficient solving algorithm is very necessary. Successive Convex Approximation Conversion to NLP Problem Linear Approximation In section Optimal Operation Model for ER-Based AC/DC HDN, ...

Abstract: In the past few years, quantum computers and quantum algorithms have shown advantages compared to their classical counterparts. In combinatorial optimization problems, quantum approximate ...

Wolfgang Jank, Implementing and Diagnosing the Stochastic Approximation EM Algorithm, Journal of Computational and Graphical Statistics, Vol. 15, No. 4 (Dec., 2006), pp. 803-829 ...