عنوان

Revisiting Sparse Dynamic Programming for the 0/1 Knapsack Problem

Number

TLpq2268337630

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Revisiting Sparse Dynamic Programming for the 0/1 Knapsack Problem

General Material Designation

[Thesis]

First Statement of Responsibility

Sifat, Tarequl Islam

Subsequent Statement of Responsibility

Rajopadhye, Sanjay

Name of Publisher, Distributor, etc.

Colorado State University

Date of Publication, Distribution, etc.

2019

Specific Material Designation and Extent of Item

42

Dissertation or thesis details and type of degree

M.S.

Body granting the degree

Colorado State University

Text preceding or following the note

2019

Text of Note

The 0/1-Knapsack Problem is a classic NP-hard problem. There are two common approaches to obtain the exact solution: branch-and-bound (BB) and dynamic programming (DP). A socalled, "sparse" DP algorithm (SKPDP) that performs fewer operations than the standard algorithm (KPDP) is well known. To the best of our knowledge, there has been no quantitative analysis of the benefits of sparsity. We provide a careful empirical evaluation of SKPDP and observe that for a "large enough" capacity, C, the number of operations performed by SKPDP is invariant with respect to C for many problem instances. This leads to the possibility of an exponential improvement over the conventional KPDP. We experimentally explore SKPDP over a large range of knapsack problem instances and provide a detailed study of the attributes that impact the performance. DP algorithms have a nice regular structure and are amenable to highly parallel implementations. However, due to the dependence structure, parallelizing SKPDP is challenging. We propose two parallelization strategies (fine-grain and coarse-grain) for SKPDP on modern multi-core processors and demonstrate a scalable improvement in the performance.

Computer science

Rajopadhye, Sanjay

Sifat, Tarequl Islam

Electronic name

p

[Thesis]

276903

a

Y