GitHub - amydn16/optimization: Machine learning flavored optimization algorithms I wrote in grad school.

These are the algorithms I wrote over two semesters of graduate courses in computational and stochastic optimization at RPI.

Name		Name	Last commit message	Last commit date
Latest commit History 33 Commits
Adaptive PDSG algorithm.m		Adaptive PDSG algorithm.m
Dynamic programming algorithm for optimal parking policy.m		Dynamic programming algorithm for optimal parking policy.m
Error-compensated SGD with b-bit quantization for FashionMNIST.py		Error-compensated SGD with b-bit quantization for FashionMNIST.py
Error-compensated compressed SGD with top-s sparsification for FashionMNIST.py		Error-compensated compressed SGD with top-s sparsification for FashionMNIST.py
Expectation-Constrained Cooperative Stochastic Approximation algorithm.m		Expectation-Constrained Cooperative Stochastic Approximation algorithm.m
Mirror SA for minimax.m		Mirror SA for minimax.m
Neyman Pearson Classification.m		Neyman Pearson Classification.m
README.md		README.md
Robust Principal Component Analysis by Alternating Direction Method of Multipliers.m		Robust Principal Component Analysis by Alternating Direction Method of Multipliers.m
Robust Principal Component Analysis by augmented Lagrangian.m		Robust Principal Component Analysis by augmented Lagrangian.m
SpiderBoost, PStorm, Vanilla & Hybrid SGD for FashionMNIST.py		SpiderBoost, PStorm, Vanilla & Hybrid SGD for FashionMNIST.py
Stochastic rollout algorithm for finite-horizons problem.m		Stochastic rollout algorithm for finite-horizons problem.m

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