Evolutionary Gradient Descent for Non-convex Optimization
Authors: Ke Xue, Chao Qian, Ling Xu, Xudong Fei
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | 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. |