Quantum and Approximation Algorithms for Maximum Witnesses of Boolean Matrix Products
(2021) 7th International Conference on Algorithms and Discrete Applied Mathematics, CALDAM 2021 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12601 LNCS. p.440-451- Abstract
The problem of finding maximum (or minimum) witnesses of the Boolean product of two Boolean matrices (MW for short) has a number of important applications, in particular the all-pairs lowest common ancestor (LCA) problem in directed acyclic graphs (dags). The best known upper time-bound on the MW problem for n× n Boolean matrices of the form O(n2.575) has not been substantially improved since 2006. In order to obtain faster algorithms for this problem, we study quantum algorithms for MW and approximation algorithms for MW (in the standard computational model). Some of our quantum algorithms are input or output sensitive. Our fastest quantum algorithm for the MW problem, and consequently for the related problems, runs in time... (More)
The problem of finding maximum (or minimum) witnesses of the Boolean product of two Boolean matrices (MW for short) has a number of important applications, in particular the all-pairs lowest common ancestor (LCA) problem in directed acyclic graphs (dags). The best known upper time-bound on the MW problem for n× n Boolean matrices of the form O(n2.575) has not been substantially improved since 2006. In order to obtain faster algorithms for this problem, we study quantum algorithms for MW and approximation algorithms for MW (in the standard computational model). Some of our quantum algorithms are input or output sensitive. Our fastest quantum algorithm for the MW problem, and consequently for the related problems, runs in time O~ (n2 + λ / 2) = O~ (n2.434), where λ satisfies the equation ω(1,λ,1)=1+1.5λ and ω(1, λ, 1 ) is the exponent of the multiplication of an n× nλ matrix by an nλ× n matrix. Next, we consider a relaxed version of the MW problem (in the standard model) asking for reporting a witness of bounded rank (the maximum witness has rank 1) for each non-zero entry of the matrix product. First, by adapting the fastest known algorithm for maximum witnesses, we obtain an algorithm for the relaxed problem that reports for each non-zero entry of the product matrix a witness of rank at most ℓ in time O~((n/ℓ)nω(1,lognℓ,1)). Then, by reducing the relaxed problem to the so called k-witness problem, we provide an algorithm that reports for each non-zero entry C[i, j] of the product matrix C a witness of rank O(⌈ WC(i, j) / k⌉ ), where WC(i, j) is the number of witnesses for C[i, j], with high probability. The algorithm runs in O~ (nωk0.4653+ n2 + o ( 1 )k) time, where ω= ω(1, 1, 1 ).
(Less)
- author
- Kowaluk, Mirosław and Lingas, Andrzej LU
- organization
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Algorithms and Discrete Applied Mathematics - 7th International Conference, CALDAM 2021, Proceedings
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- editor
- Mudgal, Apurva and Subramanian, C. R.
- volume
- 12601 LNCS
- pages
- 12 pages
- publisher
- Springer Science and Business Media B.V.
- conference name
- 7th International Conference on Algorithms and Discrete Applied Mathematics, CALDAM 2021
- conference location
- Rupnagar, India
- conference dates
- 2021-02-11 - 2021-02-13
- external identifiers
-
- scopus:85101319470
- ISSN
- 1611-3349
- 0302-9743
- ISBN
- 9783030678982
- DOI
- 10.1007/978-3-030-67899-9_35
- language
- English
- LU publication?
- yes
- id
- 04535626-5c22-4a5a-b0d7-5e685b31ae1a
- date added to LUP
- 2021-03-10 13:52:42
- date last changed
- 2024-09-05 16:56:34
@inproceedings{04535626-5c22-4a5a-b0d7-5e685b31ae1a, abstract = {{<p>The problem of finding maximum (or minimum) witnesses of the Boolean product of two Boolean matrices (MW for short) has a number of important applications, in particular the all-pairs lowest common ancestor (LCA) problem in directed acyclic graphs (dags). The best known upper time-bound on the MW problem for n× n Boolean matrices of the form O(n<sup>2.575</sup>) has not been substantially improved since 2006. In order to obtain faster algorithms for this problem, we study quantum algorithms for MW and approximation algorithms for MW (in the standard computational model). Some of our quantum algorithms are input or output sensitive. Our fastest quantum algorithm for the MW problem, and consequently for the related problems, runs in time O~ (n<sup>2</sup> <sup>+</sup> <sup>λ</sup> <sup>/</sup> <sup>2</sup>) = O~ (n<sup>2.434</sup>), where λ satisfies the equation ω(1,λ,1)=1+1.5λ and ω(1, λ, 1 ) is the exponent of the multiplication of an n× n<sup>λ</sup> matrix by an n<sup>λ</sup>× n matrix. Next, we consider a relaxed version of the MW problem (in the standard model) asking for reporting a witness of bounded rank (the maximum witness has rank 1) for each non-zero entry of the matrix product. First, by adapting the fastest known algorithm for maximum witnesses, we obtain an algorithm for the relaxed problem that reports for each non-zero entry of the product matrix a witness of rank at most ℓ in time O~((n/ℓ)nω(1,lognℓ,1)). Then, by reducing the relaxed problem to the so called k-witness problem, we provide an algorithm that reports for each non-zero entry C[i, j] of the product matrix C a witness of rank O(⌈ W<sub>C</sub>(i, j) / k⌉ ), where W<sub>C</sub>(i, j) is the number of witnesses for C[i, j], with high probability. The algorithm runs in O~ (n<sup>ω</sup>k<sup>0.4653</sup>+ n<sup>2</sup> <sup>+</sup> <sup>o</sup> <sup>(</sup> <sup>1</sup> <sup>)</sup>k) time, where ω= ω(1, 1, 1 ).</p>}}, author = {{Kowaluk, Mirosław and Lingas, Andrzej}}, booktitle = {{Algorithms and Discrete Applied Mathematics - 7th International Conference, CALDAM 2021, Proceedings}}, editor = {{Mudgal, Apurva and Subramanian, C. R.}}, isbn = {{9783030678982}}, issn = {{1611-3349}}, language = {{eng}}, pages = {{440--451}}, publisher = {{Springer Science and Business Media B.V.}}, series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}}, title = {{Quantum and Approximation Algorithms for Maximum Witnesses of Boolean Matrix Products}}, url = {{http://dx.doi.org/10.1007/978-3-030-67899-9_35}}, doi = {{10.1007/978-3-030-67899-9_35}}, volume = {{12601 LNCS}}, year = {{2021}}, }