Constrained convex minimization via model-based excessive gap
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We present a novel approach to the reconstruction of depth from light field data. Our method uses dictionary representations and group sparsity constraints to derive a convex formulation. Although our solution results in an increase of the problem dimensio ...
We introduce an analysis framework for constructing optimal first-order primal-dual methods for the prototypical constrained convex optimization template. While this class of methods offers scalability advantages in obtaining numerical solutions, they have ...
In a previous work we developed a convex infinite dimensional linear programming (LP) approach to approximating the region of attraction (ROA) of polynomial dynamical systems subject to compact basic semialgebraic state constraints. Finite dimensional rela ...
In this work, we study the task of distributed optimization over a network of learners in which each learner possesses a convex cost function, a set of affine equality constraints, and a set of convex inequality constraints. We propose a distributed diffus ...
FETI-DP is a dual iterative, nonoverlapping domain decomposition method. By a Schur complement procedure, the solution of a boundary value problem is reduced to solving a symmetric and positive definite dual problem in which the variables are directly rela ...
We present a primal-dual algorithmic framework to obtain approximate solutions to a prototypical constrained convex optimization problem, and rigorously characterize how common structural assumptions affect the numerical efficiency. Our main analysis techn ...
This paper proposes a smoothing technique for nonsmooth convex minimization using self-concordant barriers. To illustrate the main ideas, we compare our technique and the proximity smoothing approach (Nesterov2005) via the classical gradient method on both ...
We consider a Gaussian diamond network where a source communicates with the destination through n non-interfering half-duplex relays. Using simple approximations to the capacity of the network, we show that simple relaying strategies involving two relays a ...
We present a novel approach to the reconstruction of depth from light field data. Our method uses dictionary representations and group sparsity constraints to derive a convex formulation. Although our solution results in an increase of the problem dimensio ...
We propose a new primal-dual algorithmic framework for a prototypical con- strained convex optimization template. The algorithmic instances of our frame- work are universal since they can automatically adapt to the unknown Ho ̈lder con- tinuity properties ...