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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 ...

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 ...

This paper presents a short (and not exhaustive) introduction to the most used exact, approximation, and metaheuristic algorithms for solving hard combinatorial optimization problems. After ...

This paper demonstrates a Remez exchange algorithm applicable to approximation of real-valued continuous functions of a real variable by polynomials of degree smaller than n with various linear ...