Three Recent Papers
"Accelerating Greedy Coordinate Descent Methods." Lu, Haihao, Robert M. Freund, and Vahab Mirrokni, to appear at International Conference on Machine Learning (ICML) 2018, Stockholm.
"Relatively-Smooth Convex Optimization by First-Order Methods, and Applications," Lu, Haihao, Robert M. Freund, and Yurii Nesterov, SIAM Journal on Optimization (2018), vol. 28 (1), pp. 333-354.
"New Computational Guarantees for Solving Convex Optimization Problems with First Order Methods, via a Function Growth Condition Measure." Freund, Robert M., and Haihao Lu., forthcoming in Mathematical Programming.
- Nonlinear optimization theory, applications, and computation -- current focus on algorithmic theory and practice of first-order methods
- Computational complexity of nonlinear optimization
- Interior-point methods in convex optimization
- Computational science
- Related mathematical systems
- Applied optimization in management and engineering
- Linear optimization
- Fixed-point methods