Demo Data from EDBT 2026 / reviewers in the wild / expert
Elaye Karstadt
dblp:203/3289
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Computational complexity · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational complexity
algebraic complexity |
0.4 | 1 | 2020 | Matrix Multiplication, a Little Faster · J. ACM 2020 |
Computational complexity › algebraic complexity › matrix multiplication
fast matrix multiplication |
0.4 | 1 | 2020 | Matrix Multiplication, a Little Faster · J. ACM 2020 |
Computational complexity › algebraic complexity
matrix multiplication |
0.4 | 1 | 2020 | Matrix Multiplication, a Little Faster · J. ACM 2020 |
Computational complexity › algebraic complexity › matrix multiplication
matrix multiplication lower bounds |
0.4 | 1 | 2020 | Matrix Multiplication, a Little Faster · J. ACM 2020 |
Methods — techniques the papers use, named apart from their topics
matrix squaring · 0.4change of basis · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Matrix Multiplication, a Little FasterabstractStrassen’s algorithm (1969) was the first sub-cubic matrix multiplication algorithm. Winograd (1971) improved the leading coefficient of its complexity from 6 to 7. There have been many subsequent asymptotic improvements. Unfortunately, most of these have the disadvantage of very large, often gigantic, hidden constants. Consequently, Strassen-Winograd’s O ( n log 2 7 ) algorithm often outperforms other fast matrix multiplication algorithms for all feasible matrix dimensions. The leading coefficient of Strassen-Winograd’s algorithm has been generally believed to be optimal for matrix multiplication algorithms with a 2 × 2 base case, due to the lower bounds by Probert (1976) and Bshouty (1995). Surprisingly, we obtain a faster matrix multiplication algorithm, with the same base case size and asymptotic complexity as Strassen-Winograd’s algorithm, but with the leading coefficient reduced from 6 to 5. To this end, we extend Bodrato’s (2010) method for matrix squaring, and transform matrices to an alternative basis. We also prove a generalization of Probert’s and Bshouty’s lower bounds that holds under change of basis, showing that for matrix multiplication algorithms with a 2 × 2 base case, the leading coefficient of our algorithm cannot be further reduced, and is therefore optimal. We apply our method to other fast matrix multiplication algorithms, improving their arithmetic and communication costs by significant constant factors. Elaye Karstadt, Oded Schwartz |
J. ACM | 1 |
| 2017 | Matrix Multiplication, a Little FasterabstractStrassen's algorithm (1969) was the first sub-cubic matrix multiplication algorithm. Winograd (1971) improved its complexity by a constant factor. Many asymptotic improvements followed. Unfortunately, most of them have done so at the cost of very large, often gigantic, hidden constants. Consequently, Strassen-Winograd's O(nlog27) algorithm often outperforms other matrix multiplication algorithms for all feasible matrix dimensions. The leading coefficient of Strassen-Winograd's algorithm was believed to be optimal for matrix multiplication algorithms with 2x2 base case, due to a lower bound of Probert (1976). Elaye Karstadt, Oded Schwartz |
SPAA | 1 |