sparseness-inducing (ℓ1) regularization term.Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution, and compressed sensing are a few well-known examples of this approach. This paper proposes gradient projection (GP) algorithms for the bound

♠ HW3 will be released later today on bSpace ♠ Midterm to be out sometime on 18th ♠ HW2 solutions to be out before midterm released ♠ 19th March — review session to recap important material ♠ 21st March, 2013 — midterm due beginning of class.

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Abstract-This paper considers some aspects of a gradient projection method proposed by Goldstein [l], Levitin and Polyak [3], and more recently, in a less general context, by McCormick [lo]. We propose and analyze some convergent step-size rules to be used in conjunction with the method

the gradient-projection method. This amounts to solving a time dependent partial differential equation on a manifold determined by the constraints. As t--- ~ 0o the solution converges to a steady state which is the denoised image. The numerical algorithm is simple and relatively fast. The results appear

Abstract-The gradient projection anti-windup (GPAW) scheme was recently proposed as an anti-windup method for nonlinear multi-input-multi-output systems/controllers, the solution of which was recognized as a largely open problem in a recent survey paper. This paper analyzes the properties

(AT) where Q is a rotation criterion, for example varimax, and A is an initial loading matrix. The object is to minimize f(T) over all orthogonal matrices T. In this case m = k. A variety of other applications may be found in Jennrich (2001). 2 The gradient projection algorithm The gradient projection (GP

We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using gradient projection algorithm

We study an equivalent optimization problem with an inequality constraint and boundary conditions, whose necessary condition for the optimality is the variational inequality presentation of American options. To solve the problem, we use the gradient projection method, with discretizations both

Abstract-A new practical method for decoding Low-Density Parity Check (LDPC) codes is presented. The followed approach involves reformulating the parity check equations using nonlinear functions of a specific form, defined over ρ , where ρ denotes the check node degree. By constraining the inputs to these functions in the closed convex subset [0,