AidinHamedi/Optimizer-Benchmark
A benchmarking suite for evaluating PyTorch optimization algorithms on 2D mathematical functions (optimizer benchmark)
This tool helps machine learning engineers and researchers evaluate how different optimization algorithms perform. You provide various PyTorch optimizers, and it generates visual trajectories and performance rankings based on how well they navigate a set of 2D mathematical functions. This helps practitioners understand the characteristics of optimizers outside of real-world neural network training scenarios.
Use this if you are a machine learning researcher or engineer interested in the theoretical performance and behavior of PyTorch optimization algorithms on synthetic landscapes.
Not ideal if you need to choose an optimizer for a specific real-world neural network training task, as these results may not directly translate to practical applications.
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Language
Python
License
MIT
Category
Last pushed
Feb 03, 2026
Commits (30d)
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