220 citations · 930 across the 74 of their papers we have counts for
48 papers · 1 filter
Projection-Free Non-Smooth Convex Programming
Kamiar Asgari, Michael J. Neely
We study nonsmooth convex minimization over a compact convex set without projections. For a globally -Lipschitz objective, optimistic primal-dual updates give error at most $2LD…
Fast Learning for Renewal Optimization in Online Task Scheduling
Michael J. Neely
This paper considers online optimization of a renewal-reward system. A controller performs a sequence of tasks back-to-back. Each task has a random vector of parameters, called the…
A Converse Result on Convergence Time for Opportunistic Wireless Scheduling
Michael J. Neely
This paper proves an impossibility result for stochastic network utility maximization for multi-user wireless systems, including multiple access and broadcast systems. Every time s…
Online Primal-Dual Mirror Descent under Stochastic Constraints
Xiaohan Wei, Hao Yu, Michael J. Neely
We consider online convex optimization with stochastic constraints where the objective functions are arbitrarily time-varying and the constraint functions are independent and ident…
Solving Non-smooth Constrained Programs with Lower Complexity than : A Primal-Dual Homotopy Smoothing Approach
Xiaohan Wei, Hao Yu, Qing Ling +1
We propose a new primal-dual homotopy smoothing algorithm for a linearly constrained convex program, where neither the primal nor the dual function has to be smooth or strongly con…
Primal-Dual Frank-Wolfe for Constrained Stochastic Programs with Convex and Non-convex Objectives
Xiaohan Wei, Michael J. Neely
We study constrained stochastic programs where the decision vector at each time slot cannot be chosen freely but is tied to the realization of an underlying random state vector. Th…