H.J. Kushner and D.S.Clark. Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer Verlag, 1978. Home/Search Document Not in Database Summary Related Articles Check |
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Accelerated Randomized Stochastic Optimization - Dippon (2002) (Correct)
.... which attains the rate O P (n (p 1) 2p) More generally, Chen [3] and Polyak and Tsybakov [14] showed that for the class of p times di#erentiable regression functions (p 2) the optimal rate in a minimax sense is O(n (p 1) 2p) To deal with high dimensional problems Kushner and Clark [11], Polyak and Tsybakov [14] Spall [17] and others suggested randomized gradient estimators which need only two observations per step. The iterates of these methods converge still with the rate n 1 3 , but with the expense of a possibly higher asymptotic squared error. We show that these ....
....adopted in Theorem 3. Both Theorems show how to obtain estimators with unbiased limit distribution. In Section 5 it is shown how to obtain methods discussed in the previous literature as special cases of the kernel function approach. This includes the random direction method of Kushner and Clark [11] and the simultaneous perturbation method of Spall [18] Subsection 5.1) These methods can be generalized in a natural way to higher order methods (Subsection 5.2) A special kernel method proposed by Polyak and Tsybakov [14] fits in this framework as well. In the case of random vectors # n ....
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H.J. Kushner and D.S. Clark. Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer, New York, 1978.
A New Class Of Incremental Gradient Methods For Least Squares.. - Bertsekas (1996) (4 citations) (Correct)
.... evaluated at the same vector x , that is, i=1, m, 4) so that the iteration consisting of a cycle over the entire data set starting from x has the form ) x #f(x ) 5) Incremental methods are supported by stochastic convergence analyses [PoT73] Lju77] [KuC78], TBA86] Pol87] BeT89] Whi89] Gai94] BeT96] as well as deterministic convergence analyses [Luo91] Gri94] LuT94] MaS94] Man93] Ber95a] BeT96] It has been experimentally observed that the incremental gradient method (2) 3) often converges much faster than the steepest ....
Kushner, H. J., and Clark, D. S., "Stochastic Approximation Methods for Constrained and Unconstrained Systems," Springer-Verlag, NY, 1978.
Opportunistic Fair Scheduling over Multiple Wireless Channels - Liu, Knightly (2003) (14 citations) (Correct)
....that the value of f i ( # w) depends on the output of the scheduler decision so that the updating algorithm should be dynamically adjusted according to scheduling decisions. Stochastic approximation is an effective technique for find ing zeros of a function f( which cannot be explicitly known [12]. If a noisy measurement is available, i.e. y = f(x ) e , where e denotes the observed noise, stochastic approximation recursively estimates the root for f( by x k 1 = x , 19) is the step size. If e is white noise and a converges to zero, x will converge with ....
....of no more than P # . With a scale factor of i=1 Y i on #,this equation can be further transformed into P # (25) Different stochastic algorithms require different conditions. General requirements include stationarity and a certain order differential. Readers are referred to [12] for a detailed discussion. We employ the same control parameter w i (k) as in MFS D and formulate the objective function of the scheduling block as w i (k)X i (k) 26) Together with the resource and rate constraints, this again is a Knapsack problem such that MFS P s scheduling block ....
H. Kushner and D. Clark, Stochastic Approximation Methods for Constrained and Unconstrained Systems, Springer-Verlag, 1978.
Reinforcement Learning for Long-Run Average Cost - Abhijit Gosavi Assistant (Correct)
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H.J. Kushner and D.S.Clark. Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer Verlag, 1978.
Stochasticsand - Statistics Reinforcement Learning (Correct)
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H.J. Kushner, D.S. Clark, Stochastic Approximation Method for Constrained and Unconstrained Systems, Springer-Verlag, Berlin, 1978.
A Reinforcement Learning Algorithm based on Policy Iteration for.. - Gosavi (2004) (Correct)
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Kushner, H. & Clark, D. (1978) Stochastic Approximation Methods for Constrained and Unconstrained Systems. New York, NY:Springer Verlag.
Slotted Aloha as a stochastic game with partial information - Altman, Azouzi.. (2003) (1 citation) (Correct)
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Kushner, H. J., and D. S. Clark. 1978. Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer-Verlag, New York.
Individual Equilibrium and Learning in a Processor Sharing.. - Altman, Shimkin (1996) (1 citation) (Correct)
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Kushner, H. J., and D. S. Clark. 1978. Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer-Verlag, New York.
Measurement Based Optimal Source Shaping with a.. - Modani, Dube, Kumar (2000) (3 citations) (Correct)
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Harold J. Kushner and Dean S. Clark, Stochastic Approximation Methods for Constrained and Unconstrained Systems, Springer-Verlag, 1978.
Planning Algorithms - LaValle (2004) (3 citations) (Correct)
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H. J. Kushner and D. S. Clark. Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer-Verlag, Berlin, 1978.
Measurement Based Optimal Multi-path Routing - Güven, Kommareddy, La.. (Correct)
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H. Kushner and D. Clark, Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer-Verlag, 1978.
Measurement-Based Multicast on Overlay Architecture - Güven, La, Shayman.. (2004) (Correct)
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H. Kushner and D. Clark, Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer-Verlag, 1978.
IMMC: Incremental Maximum Margin Criterion - Jun Yan Benyu (Correct)
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Kushner, H.J. and Clark, D.S. Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer-Verlag, New York, 1978.
Opportunistic Traffic Scheduling Over Multiple Network Paths - Knightly (2004) (Correct)
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H. Kushner and D. Clark, Stochastic Approximation Methods for Constrained and Unconstrained Systems, Springer-Verlag, 1978.
Measurement Based Optimal Multi-path Routing - Tuna Uven Chris (2004) (Correct)
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H. Kushner and D. Clark, Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer-Verlag, 1978.
A New Analysis of a Class of Continuous-Time Algorithms .. - Dehaene, Moonen.. (1995) (Correct)
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H.J. Kushner and D.S. Clark, Stochastic Approximation Methods for Constrained and Unconstrained Systems. New York: Springer-Verlag, 1978.
A Definition of General Weighted Fairness and its.. - Vandalore, Fahmy, .. (1998) (4 citations) (Correct)
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H. J. Kushner and D. S. Clark. Stochastic approximation methods for constrained and unconstrained systems. Springer-Verlag, 1978.
Learn Quickly, Adapt Slowly - Convergent Learning Algorithms.. - Leslie, Collins (2003) (Correct)
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H. J. Kushner and D. S. Clark. Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer--Verlag, New York, 1978.
Convergent Multiple-Timescales Reinforcement Learning.. - Leslie, Collins (2003) (Correct)
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KUSHNER,H.J.andCLARK, D. S. (1978). Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer, New York.
A General Framework for Unsupervised Processing of.. - Hammer, Micheli, al. (2002) (1 citation) (Correct)
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H. J. Kushner and D. S. Clark. Stochastic approximation methods for constrained and unconstrained systems. Springer, 1978.
A Game Theoretic Approach for Delay Minimization in Slotted .. - Altman, Azouzi, Jimenez (2004) (Correct)
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KUSHNER, H. J., AND D. S. CLARK. 1978. Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer-Verlag, New York.
Mathematical Aspects of Neural Networks - Hammer (2003) (Correct)
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H. Kushner and D. Clark. Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer, 1978.
General Weighted Fairness and Its Support in.. - Vandalore, Fahmy, .. (2000) (1 citation) (Correct)
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H.J. Kushner, D.S. Clark, Stochastic Approximation Methods for Constrained and Unconstrained Systems, Springer, Berlin, 1978.
Measurement Based Optimal Multi-path Routing - Güven, Kommareddy, La.. (Correct)
No context found.
H. Kushner and D. Clark, Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer-Verlag, 1978.
Convergent Multiple-Timescales Reinforcement Learning.. - Leslie, Collins (2002) (Correct)
No context found.
Kushner, H. J. and Clark, D. S. (1978). Stochastic Approximation Methods for Constrained and Unconstrained Systems. Springer-Verlag, New York-- Berlin.
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