We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| garywei944/grab-sampler | pwc_unofficial | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| pretty_time | Ran | garywei944/grab-sampler/src/grabsampler/utils/EventTimer.py code served (permissive licence) · get_code("1d7531c00408358b") |
| probabilistic_balance | Ran | garywei944/grab-sampler/src/grabsampler/sorter/SorterBase.py code served (permissive licence) · get_code("54406a3eec6d5938") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We study convergence lower bounds of without-replacement stochastic gradient descent (SGD) for solving smooth (strongly-)convex finite-sum minimization problems. Unlike most existing results focusing on final iterate lower bounds in terms of the number of components $n$ and the number of epochs $K$, we seek bounds for arbitrary weighted average iterates that are tight in all factors including the condition number $κ$. For SGD with Random Reshuffling, we present lower bounds that have tighter $κ$ dependencies than existing bounds. Our results are the first to perfectly close the gap between lower and upper bounds for weighted average iterates in both strongly-convex and convex cases. We also prove weighted average iterate lower bounds for arbitrary permutation-based SGD, which apply to all variants that carefully choose the best permutation. Our bounds improve the existing bounds in factors of $n$ and $κ$ and thereby match the upper bounds shown for a recently proposed algorithm called GraB.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2303.07160")
get_code_for_paper("2303.07160")
have("2303.07160")
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