ELE522: Large-Scale Optimization for Data Science

Yuxin Chen, Princeton University, Fall 2019
  1. Gradient methods

  2. Frank-Wolfe and Projected gradient methods

  3. Subgradient methods

  4. Mirror descent

  5. Block coordinate descent

  6. Proximal gradient methods

  7. Nesterov's accelerated methods

  8. Operator splitting

  9. Alternating direction methods of multiplier (ADMM)

  10. Primal-dual proximal methods

  11. Quasi-Newton methods / BFGS

  12. Stochastic optimization

  13. Variance reduction in stochastic optimization

  14. Distributed optimization

  15. Saddle-escaping algorithms