Algorithm Analysis: In-depth discussion of the implemented algorithms, including time and space complexity analysis. Comparative Study: A comparison between the exact and approximation methods in ...

In the current era of technology, multipliers are integral to various hardware accelerators, making the design of efficient multipliers increasingly essential. This research introduces a novel ...

This paper introduces a heuristic algorithm that approximates the filter coefficients to the neighbouring values, which have a larger number of zeros in the canonical signed digit (CSD) representation ...

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 ...

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 ...

The expectation-maximization (EM) algorithm is a powerful computational technique for locating maxima of functions. It is widely used in statistics for maximum likelihood or maximum a posteriori ...

To design approximation algorithms, you need to consider the trade-off between the accuracy of the solution and the running time of the algorithm.

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 ...