activity
20202022
most citedPermutation Invariant Representations with Applications to Graph Deep Learning

5 citations · 13 across the 6 of their papers we have counts for

collaborators

7 papers

math.FA20225 cited

Permutation Invariant Representations with Applications to Graph Deep Learning

Radu Balan, Naveed Haghani, Maneesh Singh

This paper presents primarily two Euclidean embeddings of the quotient space generated by matrices that are identified modulo arbitrary row permutations. The original application i…

math.OC2021

Graph Generation: A New Approach to Solving Expanded Linear Programming Relaxations

Julian Yarkony, Naveed Haghani, Amelia Regan

In this article we introduce Graph Generation, an enhanced Column Generation (CG) algorithm for solving expanded linear programming relaxations of mixed integer linear programs. To…

math.OC20211 cited

Detour Dual Optimal Inequalities for Column Generation with Application to Routing and Location

Julian Yarkony, Naveed Haghani, Amelia Regan

We consider the problem of accelerating column generation (CG) for logistics optimization problems using vehicle routing as an example. Without loss of generality, we focus on the…

cs.RO20211 cited

Multi-Robot Routing with Time Windows: A Column Generation Approach

Naveed Haghani, Jiaoyang Li, Sven Koenig +4

Robots performing tasks in warehouses provide the first example of wide-spread adoption of autonomous vehicles in transportation and logistics. The efficiency of these operations,…

cs.AI20202 cited

Integer Programming for Multi-Robot Planning: A Column Generation Approach

Naveed Haghani, Jiaoyang Li, Sven Koenig +3

We consider the problem of coordinating a fleet of robots in a warehouse so as to maximize the reward achieved within a time limit while respecting problem and robot specific const…

cs.AI20204 cited

Relaxed Dual Optimal Inequalities for Relaxed Columns: with Application to Vehicle Routing

Naveed Haghani, Claudio Contardo, Julian Yarkony

We address the problem of accelerating column generation for set cover problems in which we relax the state space of the columns to do efficient pricing. We achieve this by adaptin…