Distributed Asynchronous Incremental Subgradient Methods

[1]  D. Bertsekas,et al.  Convergen e Rate of In remental Subgradient Algorithms , 2000 .

[2]  Dimitri P. Bertsekas,et al.  Incremental Subgradient Methods for Nondifferentiable Optimization , 2001, SIAM J. Optim..

[3]  Arkadi Nemirovski,et al.  The Ordered Subsets Mirror Descent Optimization Method with Applications to Tomography , 2001, SIAM J. Optim..

[4]  John N. Tsitsiklis,et al.  Gradient Convergence in Gradient methods with Errors , 1999, SIAM J. Optim..

[5]  X. Zhao,et al.  Surrogate Gradient Algorithm for Lagrangian Relaxation , 1999 .

[6]  M. Caramanis,et al.  Efficient Lagrangian relaxation algorithms for industry size job-shop scheduling problems , 1998 .

[7]  M. Solodov,et al.  Error Stability Properties of Generalized Gradient-Type Algorithms , 1998 .

[8]  V. Borkar Asynchronous Stochastic Approximations , 1998 .

[9]  Paul Tseng,et al.  An Incremental Gradient(-Projection) Method with Momentum Term and Adaptive Stepsize Rule , 1998, SIAM J. Optim..

[10]  Dimitri P. Bertsekas,et al.  A New Class of Incremental Gradient Methods for Least Squares Problems , 1997, SIAM J. Optim..

[11]  D. Bertsekas Gradient convergence in gradient methods , 1997 .

[12]  Harold J. Kushner,et al.  Stochastic Approximation Algorithms and Applications , 1997, Applications of Mathematics.

[13]  Robert G. Gallager,et al.  Discrete Stochastic Processes , 1995 .

[14]  O. Mangasarian,et al.  Serial and parallel backpropagation convergence via nonmonotone perturbed minimization , 1994 .

[15]  Luo Zhi-quan,et al.  Analysis of an approximate gradient projection method with applications to the backpropagation algorithm , 1994 .

[16]  Alexei A. Gaivoronski,et al.  Convergence properties of backpropagation for neural nets via theory of stochastic gradient methods. Part 1 , 1994 .

[17]  Luigi Grippo,et al.  A class of unconstrained minimization methods for neural network training , 1994 .

[18]  Zhi-Quan Luo,et al.  On the Convergence of the LMS Algorithm with Adaptive Learning Rate for Linear Feedforward Networks , 1991, Neural Computation.

[19]  Pierre Priouret,et al.  Adaptive Algorithms and Stochastic Approximations , 1990, Applications of Mathematics.

[20]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[21]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[22]  John N. Tsitsiklis,et al.  Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms , 1984, 1984 American Control Conference.