Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms
and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical …

Motivated by structural properties of the Web graph that support efficient data structures for
in memory adjacency queries, we study the extent to which a large network can be …

Seriation seeks to reconstruct a linear order between variables using unsorted similarity
information. It has direct applications in archeology and shotgun gene sequencing for …

In this paper we discuss two particular layout problems, namely the Single-Row Equidistant
Facility Layout Problem (SREFLP) and the Single-Row Facility Layout Problem (SRFLP) …

Tensor network contractions are widely used in statistical physics, quantum computing, and
computer science. We introduce a method to efficiently approximate tensor network …

Balanced partitioning is often a crucial first step in solving large-scale graph optimization
problems: in some cases, a big graph is chopped into pieces that fit on one machine to be …

We propose a novel approach to iterated sparse matrix dense matrix multiplication, a
fundamental computational kernel in scientific computing and graph neural network training …

In this paper, we survey some results, conjectures and open problems dealing with the
combinatorial and algorithmic aspects of the linear ordering problem. This problem consists …

We consider the Minimum Linear Arrangement problem and the (Uniform) Sparsest Cut
problem. So far, these two notorious NP-hard graph problems have resisted all attempts to …

Arora, Rao and Vazirani [2] showed that the standard semi-definite programming (SDP)
relaxation of the Sparsest Cut problem with the triangle inequality constraints has an …