For random Gaussian matrices, no online algorithm can beat the incremental greedy procedure's dense-submatrix value, with tight factor 4/(3√2), and 2√p/(p+1) for p-tensors.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.PR 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Finding a dense submatrix of a random matrix. Sharp bounds for online algorithms
For random Gaussian matrices, no online algorithm can beat the incremental greedy procedure's dense-submatrix value, with tight factor 4/(3√2), and 2√p/(p+1) for p-tensors.