An Introduction To Optimization Techniques In Machine Learning
- From linear programming to non-convex smooth optimization
- Gradient descent
- Polyak's Heavy ball: accelerated gradient descent
- Stochastic gradient descent (SGD)
- SGD and Markov chains
- Variance reduced SGD
- Computational efficiency of SGD for statistical inference
- Computational statistical tradeoff through convex relaxation
Modules
