We present a massively parallel Lagrange decomposition method for solving 0--1 integer
linear programs occurring in structured prediction. We propose a new iterative update …

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 …

We consider the incomplete multi-graph matching problem, which is a generalization of the
NP-hard quadratic assignment problem for matching multiple finite sets. Multi-graph …

We propose a graph clustering formulation based on multicut (aka weighted correlation
clustering) on the complete graph. Our formulation does not need specification of the graph …

In this work we propose to combine the advantages of learning-based and combinatorial
formalisms for 3D shape matching. While learning-based shape matching solutions lead to …

We present a fast, scalable, data-driven approach for solving relaxations of 0-1 integer linear
programs. We use a combination of graph neural networks (GNN) and the Lagrange …

Many visual computing tasks involve reasoning over structured domains and discrete
objects which can be modeled as combinatorial optimization (CO) problems. Tremendous …