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220,041
Optimization Flow Control, I: Basic Algorithm and Convergence
 IEEE/ACM TRANSACTIONS ON NETWORKING
, 1999
"... We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using gradient projection algorithm. In thi ..."
Abstract

Cited by 694 (64 self)
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We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using gradient projection algorithm
The Asymptotic Elasticity of Utility Functions and Optimal Investment in Incomplete Markets
 Annals of Applied Probability
, 1997
"... . The paper studies the problem of maximizing the expected utility of terminal wealth in the framework of a general incomplete semimartingale model of a financial market. We show that the necessary and sufficient condition on a utility function for the validity of several key assertions of the theor ..."
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Cited by 264 (19 self)
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. The paper studies the problem of maximizing the expected utility of terminal wealth in the framework of a general incomplete semimartingale model of a financial market. We show that the necessary and sufficient condition on a utility function for the validity of several key assertions
RMAX  A General Polynomial Time Algorithm for NearOptimal Reinforcement Learning
, 2001
"... Rmax is a very simple modelbased reinforcement learning algorithm which can attain nearoptimal average reward in polynomial time. In Rmax, the agent always maintains a complete, but possibly inaccurate model of its environment and acts based on the optimal policy derived from this model. The mod ..."
Abstract

Cited by 297 (10 self)
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Rmax is a very simple modelbased reinforcement learning algorithm which can attain nearoptimal average reward in polynomial time. In Rmax, the agent always maintains a complete, but possibly inaccurate model of its environment and acts based on the optimal policy derived from this model
Sensingthroughput tradeoff for cognitive radio networks
 in Proc. IEEE Int. Conf. Commun.(ICC
, 2006
"... Abstract—In a cognitive radio network, the secondary users are allowed to utilize the frequency bands of primary users when these bands are not currently being used. To support this spectrum reuse functionality, the secondary users are required to sense the radio frequency environment, and once the ..."
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Cited by 291 (19 self)
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Abstract—In a cognitive radio network, the secondary users are allowed to utilize the frequency bands of primary users when these bands are not currently being used. To support this spectrum reuse functionality, the secondary users are required to sense the radio frequency environment, and once
Decision field theory: A dynamiccognitive approach to decision making (Tech
, 1989
"... Decision field theory provides for a mathematical foundation leading to a dynamic, stochastic theory of decision behavior in an uncertain environment. This theory is used to explain (a) violations of stochastic dominance, (b) violations of strong stochastic transitivity, (c) violations of independ ..."
Abstract

Cited by 264 (14 self)
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Decision field theory provides for a mathematical foundation leading to a dynamic, stochastic theory of decision behavior in an uncertain environment. This theory is used to explain (a) violations of stochastic dominance, (b) violations of strong stochastic transitivity, (c) violations of inde
A model of reference‐dependent preferences
 Quarterly Journal of Economics
, 2006
"... We develop a model that fleshes out, extends, and modifies existing models of referencedependent preferences and loss aversion while accomodating most of the evidence motivating these models. Our approach makes referencedependent theory more broadly applicable by avoiding some of the ways that prev ..."
Abstract

Cited by 232 (8 self)
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person’s reference point is her recent expectations about outcomes (rather than the status quo), and assume that behavior accords to a personal equilibrium: The person maximizes utility given her rational expectations about outcomes, where these expectations depend on her own anticipated behavior. We
redesign
"... Participatory ergonomic intervention for prevention of low back pain: assembly line ..."
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Participatory ergonomic intervention for prevention of low back pain: assembly line
Robust utility maximization in a stochastic factor model
, 2006
"... We give an explicit PDE characterization for the solution of a robust utility maximization problem in an incomplete market model, whose volatility, interest rate process, and longterm trend are driven by an external stochastic factor process. The robust utility functional is defined in terms of a ..."
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Cited by 77 (6 self)
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We give an explicit PDE characterization for the solution of a robust utility maximization problem in an incomplete market model, whose volatility, interest rate process, and longterm trend are driven by an external stochastic factor process. The robust utility functional is defined in terms
Results 1  10
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220,041