This paper studies the use of fast and exact unidimensional L2-L1 minimization as a line search for accelerating iterative reconstruction algorithms. In L2-L1 minimization reconstruction problems, the squared Euclidea...
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This paper studies the use of fast and exact unidimensional L2-L1 minimization as a line search for accelerating iterative reconstruction algorithms. In L2-L1 minimization reconstruction problems, the squared Euclidean, or 12 norm, measures signal-data discrepancy and the L1 norm stands for a sparsity preserving regularization term. Functionals as these arise in important applications such as compressed sensing and deconvolution. Optimal unidimensional L2-L1 minimization has only recently been studied by Li and Osher for denoising problems and by Wen et al. for line search. A fast L2-L1 optimization procedure can be adapted for line search and used in iterative algorithms, improving convergence speed with little increase in computational cost. This paper proposes a new method for exact L2-L1 line search and compares it with the Li and Osher's, Wen et al.'s, as well as with a standard line search algorithm, the method of false position. The use of the proposed line search improves convergence speed of different iterative algorithms for L2-L1 reconstruction such as iterative shrinkage, iteratively reweighted least squares, and nonlinear conjugate gradient This assertion is validated experimentally in applications to signal reconstruction in compressed sensing and sparse signal deblurring. (C) 2015 Elsevier Inc. All rights reserved.
In this paper we examine blind adaptive and iterative decision feedback (DF) receivers for direct sequence code division multiple access (DS-CDMA) systems in frequency selective channels. Code-constrained minimum vari...
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In this paper we examine blind adaptive and iterative decision feedback (DF) receivers for direct sequence code division multiple access (DS-CDMA) systems in frequency selective channels. Code-constrained minimum variance (CMV) and constant modulus (CCM) design criteria for DF receivers based on constrained optimization techniques are investigated for scenarios subject to multipath. Computationally efficient blind adaptive recursive least squares (RLS) algorithms are developed for estimating the parameters of DF detectors along with successive, parallel and iterative DF structures. A new successive parallel arbitrated DF scheme is presented and combined with iterative techniques for use with cascaded DF stages for mitigating the deleterious effects of error propagation. Simulations for an uplink scenario assess the new blind algorithms, DF structures and the effects of error propagation of the new techniques
Many iterative algorithms rely on operators which may be difficult or impossible to evaluate exactly, but for which approximations are available. Furthermore, a graduated range of approximations may be available, indu...
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ISBN:
(纸本)9781424414970;1424414970
Many iterative algorithms rely on operators which may be difficult or impossible to evaluate exactly, but for which approximations are available. Furthermore, a graduated range of approximations may be available, inducing a functional relationship between computational complexity and approximation tolerance. In such a case, a reasonable strategy would be to vary tolerance over iterations, starting with a cruder approximation, then gradually decreasing tolerance as the solution is approached. In this article, it is shown that under general conditions, for linearly convergent algorithms the optimal choice of approximation tolerance convergence rate is the same linear convergence rate as the exact algorithm itself, regardless of the tolerance/complexity relationship. We illustrate this result by presenting a partial information value iteration (PIVI) algorithm for discrete time dynamic programming problems. The proposed algorithm makes use of increasingly accurate partial model information in order to decrease the computational burden of the standard value iteration algorithm. The algorithm is applied to a stochastic network example and compared to value iteration for the purpose of benchmarking.
In this paper we employ fuzzified simulated evolution and stochastic evolution algorithms for VLSI. standard cell placement targeting low power dissipation and high performance. Due to the imprecise nature of design i...
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In this paper we employ fuzzified simulated evolution and stochastic evolution algorithms for VLSI. standard cell placement targeting low power dissipation and high performance. Due to the imprecise nature of design information at the placement stage, the various objectives and constraints are expressed in fuzzy domain. The search is made to evolve towards a vector of fuzzy goals. The proposed algorithms are compared with genetic algorithm.
A gradient-projection algorithm using iterative aggregation and disaggregation is proposed and analyzed for box-constrained minimization problems. In a distributed computation model, the algorithm is shown to converge...
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A gradient-projection algorithm using iterative aggregation and disaggregation is proposed and analyzed for box-constrained minimization problems. In a distributed computation model, the algorithm is shown to converge. The algorithm is applied to optimal routing in a large interconnected data communication network, resulting in a multilevel hierarchical clustering that naturally fits the hierarchical topological structure of such networks. An implementation of the algorithm for a 52-node network shows that the serial version of the algorithm has a saving of 35% of the computational time as compared to a path-formulated gradient-projection code that is among the fastest existing programs for path-formulated optimal routing.< >
We propose and prove convergence of parallel synchronous and asynchronous iterative algorithms for the solution of Markov systems. We present and analyze a computational experience using a distributed memory multiproc...
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We propose and prove convergence of parallel synchronous and asynchronous iterative algorithms for the solution of Markov systems. We present and analyze a computational experience using a distributed memory multiprocessor.< >
The subject of this paper is to show the very high power of asynchronism for iterative algorithms in the context of global computing, that is to say, with machines scattered all around the world. The question is wheth...
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The subject of this paper is to show the very high power of asynchronism for iterative algorithms in the context of global computing, that is to say, with machines scattered all around the world. The question is whether or not asynchronism helps to reduce the communication penalty and the overall computation time of a given parallel algorithm. The asynchronous programming model is applied to a given problem implemented with a multi-threaded environment and tested over two kinds of clusters of workstations; a homogeneous local cluster and a heterogeneous non-local one. The main features of this programming model are exhibited and the high efficiency and interest of such algorithms is pointed out.
We consider the convergence performance of the iterative algorithms proposed by Ding and Chen for the solution of coupled matrix equations. A stiffness property is given to describe equations where these algorithms co...
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We consider the convergence performance of the iterative algorithms proposed by Ding and Chen for the solution of coupled matrix equations. A stiffness property is given to describe equations where these algorithms converge only very slowly, and a stochastic analysis shows that very slow convergence is a generic feature of these algorithms. Lastly we consider the recently introduced modifications to these algorithms proposed by Zhou et al and discuss their potential to improve the convergence speed for such stiff equations.
Data-dependent control flow changes are typically implemented in complex general-purpose controllers. However, in medium to fine-grained iterative algorithms found in DSP and arithmetic, it is desirable for both cost ...
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ISBN:
(纸本)0818665173
Data-dependent control flow changes are typically implemented in complex general-purpose controllers. However, in medium to fine-grained iterative algorithms found in DSP and arithmetic, it is desirable for both cost and performance reasons to develop simplified and distributed control structures throughout the array architectures. We present a transformation technique to systematically convert an iterative algorithm whose loop bounds are data-dependent to an equivalent data-independent regular algorithm. We use the worst-case situation to define the structure of a given computation. Then, depending upon applications, various optimizations are performed to minimize cost overhead associated with data-dependent characteristics. To demonstrate the feasibility of our methodologies, we have designed and simulated parallel algorithms for two important applications using the proposed transformation. Based on the resulting algorithms, we have developed VLSI array architectures using conventional methods of regular array synthesis (S.Y. Kung, 1988). Compared with previously reported structures, we have achieved substantial improvements in terms of both performance and array costs.< >
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