The maximum-cut problem is one of the fundamental problems in combinatorial
optimization. With the advent of quantum computers, both the maximum-cut and the …

Over the last two decades, significant advances have been made in the design and analysis
of fixed-parameter algorithms for a wide variety of graphtheoretic problems. This has …

The Maximum Minimal Cut Problem (MMCP), a NP-hard combinatorial optimization (CO)
problem, has not received much attention due to the demanding and challenging bi …

We propose a highly parallel primal-dual algorithm for the multicut (aka correlation
clustering) problem, a classical graph clustering problem widely used in machine learning …

Quantum annealing (QA) that encodes optimization problems into Hamiltonians remains the
only near-term quantum computing paradigm that provides sufficient many qubits for real …

Over the last two decades, significant advances have been made in the design and analysis
of fixed-parameter algorithms for a wide variety of graph-theoretic problems. This has …

We propose a theoretical framework that generalizes simple and fast algorithms for
hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive …

The linear ordering problem consists in finding a linear order $< $ on a finite set $ A $ so as
to minimize the sum of costs associated with pairs of elements $ a, b $ for which $ a< b …

The higher-order correlation clustering problem is an expressive model, and recently, local
search heuristics have been proposed for several applications. Certifying optimality …

Preprocessing is an important ingredient for solving the maximum cut problem to optimality
on real-world graphs. In our work, we derive a new framework for data reduction rules based …