Some problems are so hard that finding an exact solution would take too long, even with the most powerful computers. These problems are called intractable, and they often arise in fields like ...

An approximation ratio is a function that compares the value or cost of the solution produced by an approximation algorithm with the value or cost of the optimal solution. The ratio depends on the ...

Abstract: In this paper, we propose the Priority Facility Location Problem with Outliers (PFLPO), which is a generalization of both the Facility Location Problem with Outliers (FLPO) and Priority ...

Abstract: In view of the existing polygonal approximation algorithm of digital curves can't effectively solve the problem of polygonal approximation constrained by the offset direction, this paper ...

The ATA algorithm provides a novel approximation framework for analytic functions that cannot be expressed in closed-form via elementary or algebraic functions. It introduces a hybrid approximation ...

Stochastic approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. Among many ...

The quantum approximate optimization algorithm (QAOA) generates an approximate solution to combinatorial optimization problems using a variational ansatz circuit defined by parameterized layers of ...

Mark Jerrum, Alistair Sinclair (UC Berkeley) and Eric Vigoda (Georgia Tech) received the Association for Computing Machinery (ACM) Test of Time Award at a virtual ceremony on Wednesday 23 June at the ...

This project aims to implement the 2-approximation algorithm for the densest subgraph problem so that it runs in a linear time. This algorithm can be easily implemented with a quadratic complexity, or ...