Learn how to use online, approximation, parallel, randomized, streaming, and machine learning algorithms to optimize your performance and results.

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 ...

The algorithm we propose, IES, gives an approximate solution to the LHD problem regardless of its dimension and size with a theoretical performance guarantee. We introduce two upper bounds for the ...

Learn what an approximation ratio is, how to calculate and compare it for different problems and algorithms, and what are some examples, challenges, and limitations of approximation algorithms and ...

Id: 036940 Credits Min: 3 Credits Max: 3 Description This course covers advanced topics in approximation algorithms for NP-hard problems, including combinatorial algorithms and LP-based algorithms for ...

We extend the (1 — 1/e)-approximation algorithm to a constant-factor approximation algorithms for a nonseparable assignment problem with applications in maximizing revenue for budget-constrained ...

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 strongly ...

Dynamic programming track before detect (DP-TBD) is widely applied in the radar detection for weak targets. The algorithm selects the most advantageous state in each stage to integrate the merit ...