Next-generation 3G/4G wireless data networks allow multiple codes (or channels) to be allocated to a single user, where each code can support multiple data rates. Providing fine-grained QoS to users in such networks p...
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Next-generation 3G/4G wireless data networks allow multiple codes (or channels) to be allocated to a single user, where each code can support multiple data rates. Providing fine-grained QoS to users in such networks poses the two-dimensional challenge of assigning both power (rate) and codes to every user. This gives rise to a new class of parallel scheduling problems. We abstract general downlink scheduling problems suitable for proposed next-generation wireless data systems. Our contribution includes a communication-theoretic model for multirate wireless channels. In addition, while conventional focus has been on throughput maximization, we attempt to optimize the maximum response time of jobs, which is more suitable for streams of user requests. We present provable results on the algorithmic complexity of these scheduling problems. In particular, we are able to provide very simple, on-line algorithms for approximating the optimal maximum response time. We also perform an experimental study with realistic data of channel conditions and user requests that strengthens our theoretical results. (C) 2004 Wiley Periodicals, Inc.
Multiuser detectors for direct-sequence code-division multiple-access (DS-CDMA) systems based on a recursive convex programming (RCP) relaxation approach are proposed. In these detectors, maximum likelihood (ML) detec...
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Multiuser detectors for direct-sequence code-division multiple-access (DS-CDMA) systems based on a recursive convex programming (RCP) relaxation approach are proposed. In these detectors, maximum likelihood (ML) detection is carried out in two steps: first, the combinatorial problem associated with ML detection is relaxed into a convex programming problem and then a recursive approach is applied to get an approximate solution for NIL detection. Computer simulations demonstrate that the bit-error rate performance offered by the new detectors is near-optimal and superior to that offered by many existing suboptimal detectors including some recently proposed semidefinite-programming relaxation (SDPR) detectors. In addition, the amount of computation required by the RCP detectors is much less than that required by SDPR detectors.
By modifying the multipliers associated with inequality constraints, we can directly solve convex programming problem without nonnegative constraints of the multipliers associated with inequality constraints, hence it...
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By modifying the multipliers associated with inequality constraints, we can directly solve convex programming problem without nonnegative constraints of the multipliers associated with inequality constraints, hence it is no longer necessary to convert the inequality constraints into the equality constraints by using the 'slack variables'. With this technique, the neural network to solve convex programming problem is constructed, and its stability is analyzed rigorously. Simulation shows that this method is feasible.
Given a data instance of a convex program, we provide a collection of conic linear systems such that the data instance is ill-posed if and only if at least one of those systems is satisfied. This collection of conic l...
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Given a data instance of a convex program, we provide a collection of conic linear systems such that the data instance is ill-posed if and only if at least one of those systems is satisfied. This collection of conic linear systems is derived from a characterization of the boundary of the set of primal and dual feasible data instances associated with the given convex program.
Proximal point methods have been used by the optimization community to analyze different algorithms like multiplier methods for constrained optimization, and bundle methods for nonsmooth problems. This paper aims to b...
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Proximal point methods have been used by the optimization community to analyze different algorithms like multiplier methods for constrained optimization, and bundle methods for nonsmooth problems. This paper aims to be an introduction to the theory of proximal algorithms borrowing ideas from descent methods for unconstrained optimization. This new viewpoint allows Lis to present a simple and natural convergence proof. We also improve slightly the results from Solodov and Svaiter (1999).
We consider the problems of computing maximal points and the convex hull of a set of points in two dimensions, when the points are "in motion." We assume that the point locations (or trajectories) are not kn...
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We consider the problems of computing maximal points and the convex hull of a set of points in two dimensions, when the points are "in motion." We assume that the point locations (or trajectories) are not known precisely and determining these values exactly is feasible, but expensive. In our model the algorithm only knows areas within which each of the input points lie, and is required to identify the maximal points or points on the convex hull correctly by updating some points (i.e., determining their location exactly). We compare the number of points updated by the algorithm on a given instance to the minimum number of points that must be updated by a nondeterministic strategy in order to compute the answer provably correctly. We give algorithms for both of the above problems that always update at most three times as many points as the nondeterministic strategy, and show that this is the best possible. Our model is similar to that in [3] and [5].
This paper studies the possibility of combining interior point strategy with a steepest descent method when solving convex programming problems, in such a way that the convergence property of the interior point method...
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This paper studies the possibility of combining interior point strategy with a steepest descent method when solving convex programming problems, in such a way that the convergence property of the interior point method remains valid but many iterations do not request the solution of a system of equations. Motivated by this general idea, we propose a hybrid algorithm which combines a primal-dual potential reduction algorithm with the use of the steepest descent direction of the potential function. The O(rootn\ In epsilon\) complexity, of the potential reduction algorithm remains valid but the overall computational cost can be reduced. Our numerical experiments are also reported. Copyright (C) 2002 John Wiley Sons, Ltd.
In this paper, we give a sufficient condition for the asymptotic convergence of penalty trajectories in convex programming with multiple solutions. We show that, for a wide class of penalty methods, the associated opt...
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In this paper, we give a sufficient condition for the asymptotic convergence of penalty trajectories in convex programming with multiple solutions. We show that, for a wide class of penalty methods, the associated optimal trajectory converges to a particular solution of the original problem, characterized through a minimization selection principle. Our main assumption for this convergence result is that all the functions involved in the convex program are tubular. This new notion of regularity, weaker than that of quasianalyticity, is defined and studied in detail.
In high performance systems, process variations and fluctuations of operating environments have significant impact on the clock skew. Recently, hybrid structures of H-tree and mesh [2,15,18,19] were proposed to distri...
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ISBN:
(纸本)0780387368
In high performance systems, process variations and fluctuations of operating environments have significant impact on the clock skew. Recently, hybrid structures of H-tree and mesh [2,15,18,19] were proposed to distribute the clock signal with a balanced H-tree and lock the skew using the shunt effect of the mesh. However, in multi-giga hertz regime, the RC model [15] of the mesh is no longer valid. The inductance effect of the mesh can even make the skew worse. In this paper, we investigate the use of a novel architecture which incorporates multiple level transmission line shunts to distribute global clock signal. We derive the analytical expression of the skew reduction contributed by the shunt of a transmission line with the length of an integral multiple of clock wavelength. Based on the analytical skew expression, we adopt convex programming techniques to optimize the wire widths of the multi-level transmission line network. Simulation results show that the multilevel network achieves below 4ps skew for 10GHz clock rate.
A convex programming approach to binary tomographic image reconstruction in noisy environments is proposed. Conventional constraints are mixed with new constraints on the sinogram. A convex objective is then minimized...
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