Implemented Theoretical Results Theorem 1.1: Subspace Approximation Algorithm A randomized algorithm that computes k-subspace approximations in O (ndk/ε log (1/ε)) time complexity while providing a ...

The Traveling Salesman Problem (TSP) is a well-known problem in optimization, where the objective is to find the shortest route to visit all cities and return to the starting point. This project ...

Although efficient in a strictly theoretical sense (i.e., in the sense of taking polynomial versus exponential time), this algorithm for the permanent is not practical. Indeed, to date, no practical ...

Therefore, for all examples of SAP that admit an approximation scheme for the single-bin problem, we obtain an LP-based algorithm with (1 — 1/e — ε)-approximation and a local search algorithm with (½ ...

CSCA 5424: Approximation Algorithms and Linear Programming CSCA 5424: Approximation Algorithms and Linear Programming Get a head start on program admission Preview this course in the non-credit ...

By treating random effects in the models as hypothetical missing data and applying the Metropolis-Hastings (M-H) algorithm, this paper develops the stochastic approximation (SA) algorithm with Markov ...

Gaussian mixture models are a very useful tool for modeling data distribution. While estimating parameters using the expectation-maximization algorithm, this approach does not scale well with big ...

Abstract: In scheduling theory, the non-preemptive scheduling on a single machine of jobs with increasing processing times and release dates for total completion time minimization is known to be a ...