Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On the Iteration Complexity of Oblivious First-Order Optimization Algorithms
Authors: Yossi Arjevani, Ohad Shamir
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider a broad class of first-order optimization algorithms which are oblivious, in the sense that their step sizes are scheduled regardless of the function under consideration, except for limited side-information such as smoothness or strong convexity parameters. With the knowledge of these two parameters, we show that any such algorithm attains an iteration complexity lower bound of Ω( p L/ϵ) for L-smooth convex functions, and Ω( p L/µ ln(1/ϵ)) for Lsmooth µ-strongly convex functions. |
| Researcher Affiliation | Academia | Yossi Arjevani EMAIL Weizmann Institute of Science, Rehovot 7610001, Israel Ohad Shamir EMAIL Weizmann Institute of Science, Rehovot 7610001, Israel |
| Pseudocode | Yes | SCHEME 4.2 RESTARTING SCHEME PARAMETERS SMOOTHNESS PARAMETER L > 0 STRONG CONVEXITY PARAMETER µ > 0 CONVERGENCE PARAMETERS α > 0, C > 0 GIVEN A p-CLI OVER L-SMOOTH FUNCTIONS P WITH f(xk) f CL x0 x 2 FOR ANY INITIALIZATION VECTOR x0 ITERATE FOR t = 1, 2, . . . RESTART THE STEP SIZE SCHEDULE OF P INITIALIZE P AT x0 RUN P FOR αp 4CL/µ ITERATIONS SET x0 TO BE THE LAST ITERATE OF THIS EXECUTION END |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code for its described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments using datasets, thus no information on dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments, so it does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments, and therefore does not specify hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementation details or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experimental setup, hyperparameters, or training configurations. |