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828
Random Walks on Weighted Graphs, and Applications to Online Algorithms (Extended
 Journal of the ACM
, 1990
"... We study the design and analysis of randomized online algorithms. ..."
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Cited by 75 (2 self)
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We study the design and analysis of randomized online algorithms.
On Deterministic Traffic Regulation and Service Guarantees: A Systematic Approach by Filtering
 IEEE Transactions on Information Theory
, 1997
"... In this paper, we develop a filtering theory for deterministic traffic regulation and service guarantees under the (min; +)algebra. We show that traffic regulators that generate fupper constrained outputs can be implemented optimally by a linear time invariant filter with the impulse response f ..."
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Cited by 71 (4 self)
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In this paper, we develop a filtering theory for deterministic traffic regulation and service guarantees under the (min; +)algebra. We show that traffic regulators that generate fupper constrained outputs can be implemented optimally by a linear time invariant filter with the impulse response f under the (min; +)algebra, where f is the subadditive closure defined in the paper. Analogous to the classical filtering theory, there is an associate calculus, including feedback, concatenation, "filter bank summation" and performance bounds. The calculus is also applicable to the recently developed concept of service curves that can be used for deriving deterministic service guarantees. Our filtering approach not only yields easier proofs for more general results than those in the literature, but also allows us to design traffic regulators via systematic methods such as concatenation, filter bank summation, linear system realization, and FIRIIR realization. We illustrate the use of ...
Asymptotically Optimal Importance Sampling and Stratification for Pricing PathDependent Options
 Mathematical Finance
, 1999
"... This paper develops a variance reduction technique for Monte Carlo simulations of pathdependent options driven by highdimensional Gaussian vectors. The method combines importance sampling based on a change of drift with stratified sampling along a small number of key dimensions. The change of dri ..."
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Cited by 67 (13 self)
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This paper develops a variance reduction technique for Monte Carlo simulations of pathdependent options driven by highdimensional Gaussian vectors. The method combines importance sampling based on a change of drift with stratified sampling along a small number of key dimensions. The change of drift is selected through a large deviations analysis and is shown to be optimal in an asymptotic sense. The drift selected has an interpretation as the path of the underlying state variables which maximizes the product of probability and payoffthe most important path. The directions used for stratified sampling are optimal for a quadratic approximation to the integrand or payoff function. Indeed, under differentiability assumptions our importance sampling method eliminates variability due to the linear part of the payoff function, and stratification eliminates much of the variability due to the quadratic part of the payoff. The two parts of the method are linked because the asymptotically optimal drift vector frequently provides a particularly effective direction for stratification. We illustrate the use of the method with pathdependent options, a stochastic volatility model, and interest rate derivatives. The method reveals novel features of the structure of their payoffs. KEY WORDS: Monte Carlo methods, variance reduction, large deviations, Laplace principle 1. INTRODUCTION This paper develops a variance reduction technique for Monte Carlo simulations driven by highdimensional Gaussian vectors, with particular emphasis on the pricing of pathdependent options. The method combines importance sampling based on a change of drift with stratified sampling along a small number of key dimensions. The change of drift is selected through a large deviations analysis and is shown to...
Optimal Robot Scheduling for Web Search Engines
, 1997
"... A robot is deployed by a Web search engine in order to maintain the currency of its data base of Web pages. This paper studies robot scheduling policies that minimize the fractions r i of time pages spend outofdate, assuming independent Poisson pagechange processes, and a general distribution fo ..."
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Cited by 66 (1 self)
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A robot is deployed by a Web search engine in order to maintain the currency of its data base of Web pages. This paper studies robot scheduling policies that minimize the fractions r i of time pages spend outofdate, assuming independent Poisson pagechange processes, and a general distribution for the page access time X . We show that, if X is decreased in the increasingconvex ordering sense, then r i is decreased for all i under any scheduling policy, and that, in order to minimize expected total obsolescence time of any page, the accesses to that page should be as evenly spaced in time as possible. We then investigate the problem of scheduling to minimize the cost function P c i r i ; where the c i are given weights proportional to the pagechange rates ¯ i . We give a tight bound on the performance of such a policy and prove that the optimal frequency at which the robot should access page i is proportional to ln(h i ) \Gamma1 , where h i := Ee \Gamma¯ i X : Note that this...
Channel capacity and beamforming for multiple transmit and receive antennas with covariance feedback
, 2001
"... Abstract—We consider the capacity of a narrowband point to point communication system employing multipleelement antenna arrays at both the transmitter and the receiver with covariance feedback. Under covariance feedback the receiver is assumed to have perfect Channel State Information (CSI) while a ..."
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Cited by 63 (5 self)
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Abstract—We consider the capacity of a narrowband point to point communication system employing multipleelement antenna arrays at both the transmitter and the receiver with covariance feedback. Under covariance feedback the receiver is assumed to have perfect Channel State Information (CSI) while at the transmitter the channel matrix is modeled as consisting of zero mean complex jointly Gaussian random variables with known covariances. Specifically we assume a channel matrix with i.i.d. rows and correlated columns, a common model for downlink transmission. We determine the optimal transmit precoding strategy to maximize the Shannon capacity of such a system. We also derive closed form necessary and sufficient conditions on the spatial covariance for when the maximum capacity is achieved by beamforming. The conditions for optimality of beamforming agree with the notion of waterfilling over multiple degrees of freedom. I.
Wornell, “Secure transmission with multiple antennas II: The MIMOME wiretap channel
 IEEE Trans. Inf. Theory
"... Abstract—The role of multiple antennas for secure communication is investigated within the framework of Wyner’s wiretap channel. We characterize the secrecy capacity in terms of generalized eigenvalues when the sender and eavesdropper have multiple antennas, the intended receiver has a single antenn ..."
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Cited by 57 (7 self)
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Abstract—The role of multiple antennas for secure communication is investigated within the framework of Wyner’s wiretap channel. We characterize the secrecy capacity in terms of generalized eigenvalues when the sender and eavesdropper have multiple antennas, the intended receiver has a single antenna, and the channel matrices are fixed and known to all the terminals, and show that a beamforming strategy is capacityachieving. In addition, we study a masked beamforming scheme that radiates power isotropically in all directions and show that it attains nearoptimal performance in the high SNR regime. Insights into the scaling behavior of the capacity in the large antenna regime as well as extensions to ergodic fading channels are also provided. Index Terms—Artificial noise, broadcast channel, cryptography, generalized eigenvalues, masked beamforming, MIMO systems, multiple antennas, secrecy capacity, secure spacetime codes, wiretap channel. I.
Wireless systems and interference avoidance
 IEEE Trans. Wireless Commun
, 2002
"... Abstract—Motivated by the emergence of programmable radios, we seek to understand a new class of communication system where pairs of transmitters and receivers can adapt their modulation/demodulation method in the presence of interference to achieve better performance. Using signal to interference r ..."
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Cited by 56 (12 self)
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Abstract—Motivated by the emergence of programmable radios, we seek to understand a new class of communication system where pairs of transmitters and receivers can adapt their modulation/demodulation method in the presence of interference to achieve better performance. Using signal to interference ratio as a metric and a general signal space approach, we present a class of iterative distributed algorithms for synchronous systems which results in an ensemble of optimal waveforms for multiple users connected to a common receiver (or colocated independent receivers). That is, the waveform ensemble meets the Welch Bound with equality and, therefore, achieves minimum average interference over the ensemble of signature waveforms. We derive fixed points for a number of scenarios, provide examples, look briefly at ensemble stability under user addition and deletion as well as provide a simplistic comparison to synchronous codedivision multipleaccess. We close with suggestions for future work. Index Terms—Adaptive modulation, codedivision multipleaccess systems, codeword optimization, interference avoidance, multiuser
Strong duality for semidefinite programming
 SIAM J. Optim
, 1997
"... Abstract. It is well known that the duality theory for linear programming (LP) is powerful and elegant and lies behind algorithms such as simplex and interiorpoint methods. However, the standard Lagrangian for nonlinear programs requires constraint qualifications to avoid duality gaps. Semidefinite ..."
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Cited by 53 (19 self)
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Abstract. It is well known that the duality theory for linear programming (LP) is powerful and elegant and lies behind algorithms such as simplex and interiorpoint methods. However, the standard Lagrangian for nonlinear programs requires constraint qualifications to avoid duality gaps. Semidefinite linear programming (SDP) is a generalization of LP where the nonnegativity constraints are replaced by a semidefiniteness constraint on the matrix variables. There are many applications, e.g., in systems and control theory and combinatorial optimization. However, the Lagrangian dual for SDP can have a duality gap. We discuss the relationships among various duals and give a unified treatment for strong duality in semidefinite programming. These duals guarantee strong duality, i.e., a zero duality gap and dual attainment. This paper is motivated by the recent paper by Ramana where one of these duals is introduced.
CDMA Codeword Optimization: interference avoidance and convergence via class warfare
 IEEE Transactions on Information Theory
, 2001
"... Interference avoidance has been shown to reduce total square correlation (TSC) for given ensembles of user signature waveforms (codewords) in a synchronous CDMA system. In all experiments we have conducted, sequential application of interference avoidance produces an optimal codeword set when starti ..."
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Cited by 53 (17 self)
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Interference avoidance has been shown to reduce total square correlation (TSC) for given ensembles of user signature waveforms (codewords) in a synchronous CDMA system. In all experiments we have conducted, sequential application of interference avoidance produces an optimal codeword set when starting from randomly chosen initial codewords. Here we provide the rst formal proof of convergence to optimal codeword ensembles for greedy interference avoidance algorithms augmented by a technique called \class warfare" whereby users which reside in more heavily loaded areas of the signal space purposely interfere with (attack) the reception of users in less crowded areas. Coordination of deliberate interference by a complete class of aggrieved user is also sometimes necessary. Such \attacks" and subsequent codeword adjustment by attacked users are shown to strictly decrease TSC. Along the way we also show, using linear algebra and a variant of stochastic ordering, equivalence between minimiz...
Derivatives of Spectral Functions
, 1996
"... A spectral function of a Hermitian matrix X is a function which depends only on the eigenvalues of X , 1 (X) 2 (X) : : : n (X), and hence may be written f( 1 (X); 2 (X); : : : ; n (X)) for some symmetric function f . Such functions appear in a wide variety of matrix optimization problems. We ..."
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Cited by 50 (12 self)
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A spectral function of a Hermitian matrix X is a function which depends only on the eigenvalues of X , 1 (X) 2 (X) : : : n (X), and hence may be written f( 1 (X); 2 (X); : : : ; n (X)) for some symmetric function f . Such functions appear in a wide variety of matrix optimization problems. We give a simple proof that this spectral function is differentiable at X if and only if the function f is differentiable at the vector (X), and we give a concise formula for the derivative. We then apply this formula to deduce an analogous expression for the Clarke generalized gradient of the spectral function. A similar result holds for real symmetric matrices. 1 Introduction and notation Optimization problems involving a symmetric matrix variable, X say, frequently involve symmetric functions of the eigenvalues of X in the objective or constraints. Examples include the maximum eigenvalue of X, or log(det X) (for positive definite X), or eigenvalue constraints such as positive semidefinit...