# Parameterized Approximation Algorithms Workshop (PAAW)

## Scope

Two standard approaches to handle hard (typically NP-hard) optimization problems are to develop *approximation* and *parameterized* algorithms. For the former, the runtime should be polynomial in the input size, but the computed solution may deviate from the optimum. For the latter, the optimum solution should be computed, but any super-polynomial runtime should be isolated to some parameter of the input. Some problems however are hard to approximate on one hand, and on the other it is also hard to obtain parameterized algorithms for some given parameter. In this case one may still hope to obtain *parameterized approximation* algorithms, which combine the two paradigms, i.e. the computed solution may deviate from the optimum and the runtime should have super-polynomial dependence only in some given parameter. Recently there has been a great deal of development in proving the existence or non-existence of parameterized approximation algorithms, and the aim of this workshop is to bring together active researchers of this emerging field, so that they may share their results and insights.

## Editions

## Topics of interest

Parameterized approximation algorithms

Lossy kernelization

Parameterized inapproximability

Fine-grained complexity of approximation

Subexponential time approximation

Efficient polynomial-time approximation schemes