The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains
Authors: Daron Anderson, Douglas Leith
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we present numerical simulations for Online Lazy Gradient Descent, using a range of strongly convex domains and i.i.d opponents. |
| Researcher Affiliation | Academia | Daron Anderson School of Computer Science and Statistics Trinity College Dublin Douglas Leith School of Computer Science and Statistics Trinity College Dublin |
| Pseudocode | Yes | Algorithm 1: Online Lazy Gradient Descent Data: Cost vectors a1, a2, . . . Rd. Parameter η > 0. Domain X Rd. 1 for n = 0, 1, . . . do 2 xn+1 =ΠX η n Pn i=1 ai 3 Receive an+1 and pay cost an+1 xn+1 |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper generates synthetic cost vectors for simulations ('cost vectors an = a + µn') but does not refer to a publicly available dataset or provide access information for one. |
| Dataset Splits | No | The paper does not mention using a validation set or describe any data splitting for training, validation, or testing. |
| Hardware Specification | No | The paper mentions 'The higher dimensional simulations ran on the order of minutes, due to use of an all-purpose Python package to compute minimisers.' but does not specify any hardware details like GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions 'an all-purpose Python package to compute minimisers' but does not specify any software names with version numbers. |
| Experiment Setup | Yes | For simplicity we use stepsize η = 1. When searching for worst-case performance it is enough, by symmetry of the domain, to only consider E[an] = a with nonnegative nondecreasing components. We consider a = b/ b for b(i) = (i/d)r and a range of parameters r 0. For example three degenerate cases are a = 1 d(1, . . . , 1) for r = 0; a = q 2 d(d+1)(1, 2, . . . , d) for r = 1 and a = (0, . . . , 0, 1) for r . We use cost vectors an = a + µn for two types of noise: (1) µn(j) = 1 d(Bn 1 , . . . , Bn d ) (2) µn(j) = (0, . . . , 0, Bn d ). |