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We propose, analyze and test an alternating minimization algorithm for recovering images from blurry and noisy observations with total variation (TV) regularization. This algorithm arises from a new half-quadratic model applicable to not only the anisotropic but also isotropic forms of total variation discretizations. The per-iteration computational(More)
In this paper, we propose and study the use of alternating direction algorithms for several 1-norm minimization problems arising from sparse solution recovery in compressive sensing, including the basis pursuit problem, the basis-pursuit denoising problems of both unconstrained and constrained forms, as well as others. We present and investigate two classes(More)
We develop a general model to estimate the throughput and goodput between arbitrary pairs of nodes in the presence of interference from other nodes in a wireless network. Our model is based on measurements from the underlying network itself and is thus more accurate than abstract models of RF propagation such as those based on distance. The seed(More)
A matrix giving the traffic volumes between origin and destination in a network has tremendously potential utility for network capacity planning and management. Unfortunately, traffic matrices are generally unavailable in large operational IP networks. On the other hand, link load measurements are readily available in IP networks. In this paper, we propose(More)
We consider solving minimization problems with 1-regularization: min x 1 + µf (x), particularly for f (x) = 1 2 Ax − b 2 M , where A ∈ R m×n and m < n. Our goal is to construct efficient and robust algorithms for solving large-scale problems with dense data, and our approach is based on two powerful algorithmic ideas: operator-splitting and continuation.(More)
This work concerns primal-dual interior-point methods for semideenite programming (SDP) that use a search direction originally proposed by Helmberg-Rendl-Vanderbei-Wolkowicz 5] and Kojima-Shindoh-Hara 11], and recently rediscovered by Monteiro 15] in a more explicit form. In analyzing these methods, a number of basic equalities and inequalities were(More)
We present a framework for solving the large-scale 1-regularized convex minimization problem: min x 1 + μf (x). Our approach is based on two powerful algorithmic ideas: operator-splitting and continuation. Operator-splitting results in a fixed-point algorithm for any given scalar μ; continuation refers to approximately following the path traced by the(More)
This paper considers the distribution of the rates at which flows transmit data, and the causes of these rates. First, using packet level traces from several Internet links, and summary flow statistics from an ISP backbone, we examine Internet flow rates and the relationship between the rate and other flow characteristics such as size and duration. We find,(More)
We present a unified analysis for a class of long-step primal-dual path-following algorithms for semidefinite programming whose search directions are obtained through linearization of the symmetrized equation of the central path Hp(XS)-[PXSP-~ + (PXSP 1)TI/2 = #I, introduced by Zhang. At an iterate (X, S), we choose a scaling matrix P from the class of(More)
—Recent compressive sensing results show that it is possible to accurately reconstruct certain compressible signals from relatively few linear measurements via solving nonsmooth convex optimization problems. In this paper, we propose the use of the alternating direction method—a classic approach for optimization problems with separable variables (D. Gabay(More)