Topic:Data-Driven Optimization

Speaker:Yinyu Ye

Affiliation:Stanford University

Time:Wednesday, 14 May. 14:00-16:00pm

Location:Room K01 Guanghua Building 2


We present several optimization models and algorithms dealing with uncertain, massive and/or structured data. Specifically, we discuss

• Distributionally Robust Optimization Models, where many problems can be efficiently solved when the associated uncertain data possess no priori distributions;

• Near-Optimal Online Linear Programming, where the constraint matrix is revealed column by column along with the objective function and a decision has to be made as soon as a variable arrives;

• Least-squares with Nonconvex Regularization, where a sparse or low-rank solution is sought;

• Alternating Direction Method of Multipliers (ADMM), where an example is given to show that the direct extension of ADMM for three-block convex minimization problems is not necessarily convergent, and possible convergent variants are proposed.