The Complexity of Making the Gradient Small in Stochastic Convex Optimization.
Dylan J. FosterAyush SekhariOhad ShamirNathan SrebroKarthik SridharanBlake E. WoodworthPublished in: COLT (2019)
Keyphrases
- convex optimization
- interior point methods
- low rank
- norm minimization
- convex optimization problems
- total variation
- augmented lagrangian
- primal dual
- convex relaxation
- operator splitting
- computational complexity
- computer vision
- worst case
- distance metric
- semidefinite program
- alternating direction method of multipliers