Approximation algorithms for the TSP endeavour to provide efficient, near‐optimal solutions where exact methods prove computationally prohibitive.

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 ...

As a result, fast parallel algorithms for reconstructing a function from its truncated trigonometric Fourier series are proposed. The presented numerical experiments confirm the high efficiency of ...

The area of approximation algorithms is aimed at giving provable guarantees on the performance of heuristics for hard problems. The course will present general techniques (such as convex ...

We propose a stochastic approximation expectation maximization (SAEM) algorithm to estimate the RC-3PNO model with non-normal latent trait distributions.

Implements a traveling salesperson problem (TSP) approximation algorithm in order to optimize routes for package deliveries. Written in Python. Supports multiple delivery vehicles, real time changes ...

Approximation Algorithm for the NP-Complete problem of finding a vertex cover of minimum weight in a graph with weighted vertices. Guarantees an answers at most 2 times the optimal minimum weighted ...