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SIAMCO
2000
117views more  SIAMCO 2000»
13 years 6 months ago
The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
It is shown here that stability of the stochastic approximation algorithm is implied by the asymptotic stability of the origin for an associated ODE. This in turn implies convergen...
Vivek S. Borkar, Sean P. Meyn
GECCO
2007
Springer
192views Optimization» more  GECCO 2007»
14 years 15 days ago
Convergence of stochastic search algorithms to gap-free pareto front approximations
Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The...
Oliver Schütze, Marco Laumanns, Emilia Tantar...
ICASSP
2010
IEEE
13 years 6 months ago
Stochastic cross-layer resource allocation for wireless networks using orthogonal access: Optimality and delay analysis
Efficient design of wireless networks requires implementation of cross-layer algorithms that exploit channel state information. Capitalizing on convex optimization and stochastic...
Antonio G. Marqués, Georgios B. Giannakis, ...
WSC
2001
13 years 7 months ago
Global random optimization by simultaneous perturbation stochastic approximation
We examine the theoretical and numerical global convergence properties of a certain "gradient free" stochastic approximation algorithm called the "simultaneous pertu...
John L. Maryak, Daniel C. Chin
SIAMJO
2008
212views more  SIAMJO 2008»
13 years 6 months ago
Convergence Rate of an Optimization Algorithm for Minimizing Quadratic Functions with Separable Convex Constraints
A new active set algorithm for minimizing quadratic functions with separable convex constraints is proposed by combining the conjugate gradient method with the projected gradient. ...
Radek Kucera