We focus on the problem of minimizing the sum of smooth component functions (where the
sum is strongly convex) and a nonsmooth convex function, which arises in regularized …

Optimization has continued to expand in all directions at an astonishing rate. New
algorithmic and theoretical techniques are continually developing and the diffusion into …

In this paper, we focus on separable convex optimization problems with box constraints and
a specific set of linear constraints. The solution is given in closed-form as a function of some …

Efficient methods for convex resource allocation problems usually exploit algebraic
properties of the objective function. For problems with nested constraints, we show that …

The problem of decentralized sequential detection with conditionally independent
observations is studied. The sensors form a star topology with a central node called fusion …

We propose an exact polynomial algorithm for a resource allocation problem with convex
costs and constraints on partial sums of resource consumptions, in the presence of either …

Convex optimization with feedback is a framework where a learner relies on iterative queries
and feedback to arrive at the minimizer of a convex function. It has gained considerable …

In this paper, we propose two algorithms for solving convex optimization problems with
linear ascending constraints. When the objective function is separable, we propose a dual …

Rate-constrained power minimization (PMIN) over a code division multiple-access (CDMA)
channel with correlated noise is studied. PMIN is shown to be an instance of a separable …

The paper considers the minimization of a separable convex function subject to linear
ascending constraints. The problem arises as the core optimization in several resource …