- Convex sets and functions, separation oracles and sub-gradients.
- Duality- Historical methods: Center-of-Mass, Ellipsoid.
- Interior Point Methods for small-to-medium scale problems.
- Conic constraints and semi definite programming.
- First-order, large scale optimization: mirror descent and gradient descent.
- Stochastic optimization, stochastic gradient descent, stochastic dual averaging.
- Stochastic optimization and machine learning.
- Dual view of stochastic gradient descent; stochastic dual coordinate ascent.
- Optimality of conjugate gradient descent and Nesterov's acceleration.
- Prox oracles, composite objective methods, and smoothing.
- Duality- Historical methods: Center-of-Mass, Ellipsoid.
- Interior Point Methods for small-to-medium scale problems.
- Conic constraints and semi definite programming.
- First-order, large scale optimization: mirror descent and gradient descent.
- Stochastic optimization, stochastic gradient descent, stochastic dual averaging.
- Stochastic optimization and machine learning.
- Dual view of stochastic gradient descent; stochastic dual coordinate ascent.
- Optimality of conjugate gradient descent and Nesterov's acceleration.
- Prox oracles, composite objective methods, and smoothing.