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We proposed earlier an optimization approach to reactive flow control where the objective of the control is to maximize the total source utility over their transmission rates. The source utility functions model their valuation of bandwidth and can be different for different sources. The control mechanism is derived as a gradient projection algorithm to solve the dual problem. In this paper we generalize the algorithm and the convergence result to an asynchronous setting where the computations at and the communications among the links and sources are uncoordinated and based on possibly outdated information.

This chapter introduces regularity properties of GVIP(F, u, X) under which a CA algorithm will finitely identify the optimal active constraints at a point in SOL(F, u, X), and, given stronger properties, finitely identify a point in SOL(F, u, X). The results obtained generalize and improve upon several previous ones for instances of the class of CA algorithms.

In this chapter we propose a multichannel recovery approach to ameliorate coding artifacts in compressed video. The main shortcomings of previously proposed recovery algorithms for this problem was that only spatial smoothness was explicitly enforced. In this chapter we attempt to ameliorate the above problem. According to the proposed approach, prior knowledge is enforced by introducing regularization operators which complement the transmitted data. The term multichannel implies that both the spatial (within-channel) and temporal (across-channel) properties of the image sequences are used in the recovery process. More specifically, regularization operators are defined that in addition to spatial smoothness explicitly enforce smoothness along the motion trajectories. Since the compressed images due to quantization belong to known convex sets, iterative gradient projection algorithms are proposed to minimize the regularized functional and simultaneously guarantee membership to these sets. Numerical experiments are shown using H.261 and H.263 coded sequences. These experiments demonstrate that introduction of temporal regularization offers a significant improvement both visually and from a peak signal-to-noise ratio point of view.

A new interior point algorithm is presented for linear complementarity problems. Unlike the homotopy method that follows the path of centers or the potential reduction method that minimizes the potential function, our method minimizes the corresponding equivalent quadratic programming problem directly, using the scaled gradient projection technique.