of our algorithm is that we use as a building block a linear program whose integrality gap is Ω ( √ m). We bypass this obstacle by iteratively using the solutions produced by the LP to construct new instances with significantly smaller integrality gaps, eventually obtaining the desired approximation. We

approximation algorithms, our main focus is to improve our understanding of the stronger configuration LP relaxation. In this direction, we prove that the integrality gap of the configuration LP is strictly better than 34, and provide corresponding polynomial time roundings, in the following restrictions

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the receiver and the transmitter. We restrict our attention to a general class of periodic sub-Nyquist samplers, which subsumes as special cases sampling with periodic modulation and filter banks. We evaluate the loss due to channel-independent (universal) sub-Nyquist design through a sampled capacity loss

conditions. Flush is scalable; its effective bandwidth over a 48-hop wireless network is approximately one-third of the rate achievable over one hop. The design of Flush is sim-plified by assuming that different flows do not interfere with each other, a reasonable restriction for many sensornet ap

nonconvex factorable objective function over a feasible region that is restricted by a set of constraints, each of which is defined in terms of nonconvex factorable functions. Such problems find widespread applications in production planning, location and allocation, chemical process design and control

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parts of the calculations will certainly be helpful in future investigations. In this article we present a framework for such an automatization based on a C++ code that can deal with a large number of Green functions. Since also the creation of the expressions for the integrals of the Dyson

(MMOGs) can include millions of concurrent players spread across the world and interacting with each other within a single session. Faced with high resource demand variability and with misfit resource renting policies, the current industry practice is to over-provision for each game tens of self-owned data centres, making the market entry affordable only for big companies. Focusing on the reduction of entry and operational costs, we investigate a new dynamic resource provisioning method for MMOG operation using external data centres as low-cost resource providers. First, we identify in the various types of player interaction a source of short-term load variability, which complements the long-term load variability due to the size of the player population. Then, we introduce a combined MMOG processor, network, and memory load model that takes into account both the player interaction type and the population

This paper presents the first theoretical results showing that stable identification of overcomplete µ-coherent dictionaries Φ ∈ Rd×K is locally possible from training signals with sparsity levels S up to the order O(µ−2) and signal to noise ratios up to O( d). In particular the dictionary is recoverable as the local maximum of a new maximisation criterion that generalises the K-means criterion. For this maximisation criterion results for asymptotic exact recovery for sparsity levels up toO(µ−1) and stable recovery for sparsity levels up to O(µ−2) as well as signal to noise ratios up to O( d) are provided. These asymptotic results translate to finite sample size recovery results with high probability as long as the sample size N scales as O(K3dSε̃−2), where the recovery precision ε ̃ can go down to the asymptotically achievable precision. Further to actually find the local maxima of the new criterion, a very simple Iterative Thresholding & K (signed) Means algorithm (ITKM), which has complexity O(dKN) in each iteration, is presented and its local efficiency is demonstrated in several experiments. Index Terms dictionary learning, dictionary identification, sparse coding, sparse component analysis, vector quanti-sation, K-means, finite sample size, sampling complexity, maximisation criterion, sparse representation 1