GPU accelerated Hungarian algorithm for traveling salesman problem

In this thesis, we present a model of the Traveling Salesman Problem (TSP) cast in a quadratic assignment problem framework with linearized objective function and constraints. This is referred to as Reformulation Linearization Technique at Level 2 (or RLT2). We apply dual ascent procedure for obtaining lower bounds that employs Linear Assignment Problem (LAP) solver recently developed by Date(2016). The solver is a parallelized Hungarian Algorithm that uses Compute Unified Device Architecture (CUDA) enabled NVIDIA Graphics Processing Units (GPU) as the parallel programming architecture. The aim of this thesis is to make use of a modified version of the Dual Ascent-LAP solver to solve the TSP. Though this procedure is computational expensive, the bounds obtained are tight and our experimental results confirm that the gap is within 2% for most problems. However, due to limitations in computational resources, we could only test problem sizes N < 30. Further work can be directed at theoretical and computational analysis to test the efficiency of our approach for larger problem instances.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Industrial Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2017

Author dc:creator

• Kaushik, Varsha Ravi Prakash

Contributors dc:contributor

• Nagi, Rakesh

• Compute Unified Device Architecture (CUDA)
• Linear assignment problem
• Traveling salesman problem
• Reformulation Linearization Technique (RLT)

Statement dc:rights

• Copyright 2017 Varsha Ravi Prakash Kaushik

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/97805

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/97805