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64
On Allocating Goods to Maximize Fairness
, 2009
"... Given a set A of m agents and a set I of n items, where agent A ∈ A has utility uA,i for item i ∈ I, our goal is to allocate items to agents to maximize fairness. Specifically, the utility of an agent is the sum of the utilities for items it receives, and we seek to maximize the minimum utility of a ..."
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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
On the Configuration LP for Maximum Budgeted Allocation
, 2014
"... We study the Maximum Budgeted Allocation problem, i.e., the problem of selling a set of m indivisible goods to n players, each with a separate budget, such that we maximize the collected revenue. Since the natural assignment LP is known to have an integrality gap of 34, which matches the best known ..."
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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
1Minimax Capacity Loss under SubNyquist Universal Sampling
"... This paper considers the capacity of subsampled analog channels when the sampler is designed to operate independent of instantaneous channel realizations. A compound multiband Gaussian channel with unknown subband occupancy is considered, with perfect channel state information available at both the ..."
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the receiver and the transmitter. We restrict our attention to a general class of periodic subNyquist samplers, which subsumes as special cases sampling with periodic modulation and filter banks. We evaluate the loss due to channelindependent (universal) subNyquist design through a sampled capacity loss
Flush: A Reliable Bulk Transport Protocol for Multihop Wireless Networks
"... We present Flush, a reliable, high goodput bulk data transport protocol for wireless sensor networks. Flush provides endtoend reliability, reduces transfer time, and adapts to timevarying network conditions. It achieves these properties using endtoend acknowledgments, implicit snooping of con ..."
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conditions. Flush is scalable; its effective bandwidth over a 48hop wireless network is approximately onethird of the rate achievable over one hop. The design of Flush is simplified by assuming that different flows do not interfere with each other, a reasonable restriction for many sensornet ap
Global Optimization of Nonconvex . . .
 MATHEMATICAL PROGRAMMING
, 1998
"... The primary objective of this thesis is to develop and implement a global optimization algorithm to solve a class of nonconvex programming problems, and to test it using a collection of engineering design problem applications. The class of problems we consider involves the optimization of a general ..."
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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
permission. Structured Codes in Information Theory: MIMO and Network Applications
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CrasyDSE: A framework for solving DysonSchwinger equations
"... DysonSchwinger equations are important tools for nonperturbative analyses of quantum field theories. For example, they are very useful for investigations in quantum chromodynamics and related theories. However, sometimes progress is impeded by the complexity of the equations. Thus automatizing pa ..."
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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
Transactions on Parallel and Distributed Systems
"... (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 overprovision for each game tens of selfown ..."
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(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 overprovision for each game tens of selfowned 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 lowcost resource providers. First, we identify in the various types of player interaction a source of shortterm load variability, which complements the longterm 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
1Local Identification of Overcomplete Dictionaries
"... This paper presents the first theoretical results showing that stable identification of overcomplete µcoherent 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 recove ..."
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This paper presents the first theoretical results showing that stable identification of overcomplete µcoherent 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 Kmeans 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 quantisation, Kmeans, finite sample size, sampling complexity, maximisation criterion, sparse representation 1
Results 1  10
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