osqp_benchmarks
avalanche
| osqp_benchmarks | avalanche | |
|---|---|---|
| 2 | 1 | |
| 90 | 1,683 | |
| - | 2.3% | |
| 0.0 | 9.4 | |
| 11 months ago | 7 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
osqp_benchmarks
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Optimization solvers: missing link for fully open-source energy system modeling
OSQP is fast, but only for QP, not LP. The "benchmarks" (https://github.com/osqp/osqp_benchmarks) include some important problem classes but are random so, for general QP, are not valid. On the industry standard benchmarks (http://plato.asu.edu/ftp/qpbench.html) OSQP doesn't look so good, and it's not even tested against commercial solvers (http://plato.asu.edu/ftp/cconvex.html). Our experience with it on general benchmarking problems is that it can struggle to get sufficiently accurate dual values to the extent that it fails to solve them. For certain classes of important QP problems, and when optimization to small tolerances is not required, it's undoubtedly a great solver - but it's not a general solver.
avalanche
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[R] Single-task Continual/Incremental/Online/Life-Long learning.
Lastly, there are several github repo, but the most popular one is ContinualAI/avalanche, which already implement some of above algorithm, for the purpose of reproducibility i.e. can be applied to your task (probably)
What are some alternatives?
osqp-eigen - Simple Eigen-C++ wrapper for OSQP library
evaluate - 🤗 Evaluate: A library for easily evaluating machine learning models and datasets.
l2rpn-baselines - L2RPN Baselines a repository to host baselines for l2rpn competitions.
torch-fidelity - High-fidelity performance metrics for generative models in PyTorch
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
pytorch-accelerated - A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal, but extensible training loop which is flexible enough to handle the majority of use cases, and capable of utilizing different hardware options with no code changes required. Docs: https://pytorch-accelerated.readthedocs.io/en/latest/
rexmex - A general purpose recommender metrics library for fair evaluation.
trajectopy - Trajectopy - Trajectory Evaluation in Python
continuum - A clean and simple data loading library for Continual Learning
trajectopy-core - Trajectopy - Trajectory Evaluation in Python
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
simpleeval - Simple Safe Sandboxed Extensible Expression Evaluator for Python