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..
Evolutionary Gradient Descent for Non-convex Optimization
Authors: Ke Xue, Chao Qian, Ling Xu, Xudong Fei
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove that EGD can converge to a second-order stationary point by escaping the saddle points, and is more efficient than previous algorithms. Empirical results on non-convex synthetic functions as well as reinforcement learning (RL) tasks also show its superiority. |
| Researcher Affiliation | Collaboration | Ke Xue1 , Chao Qian1 , Ling Xu2 and Xudong Fei2 1State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 22012 Lab, Huawei Technologies, Shenzhen 518000, China |
| Pseudocode | Yes | Algorithm 1 PGD algorithm; Algorithm 2 EGD algorithm; Algorithm 3 Mutation and Selection |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | First, we compare these algorithms on three synthetic functions: Octopus, Ackley and Schwefel. ... Next, we examine the performance of EGD on four Mu Jo Co locomotion tasks: Swimmer-v2, Hopper-v2, Half Cheetah-v2 and Ant-v2 [Todorov et al., 2012]. |
| Dataset Splits | No | The paper mentions using synthetic functions and RL tasks for experiments but does not provide specific details on how datasets were split into training, validation, and test sets. |
| Hardware Specification | No | The paper does not specify any details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software libraries, frameworks, or environments used in the experiments. |
| Experiment Setup | Yes | We use identical random seeds (2017, 2018, 2019, 2020, 2021) for all tasks and algorithms. The population size N is always set to 5. To make fair comparisons on each task, Multi-GD, Multi-PGD, ESGD and EGD employ the same learning rate η; (N + N)-EA, Multi-PGD and ESGD employ the same mutation strength r, while the N mutation strengths {r(p)}N p=1 of EGD are set to uniformly discretized values between r and 1.2r (or 1.5r); the parameters L and ϵ of Multi PGD and EGD are set to the same. |