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352,508
A scaled conjugate gradient algorithm for fast supervised learning
 NEURAL NETWORKS
, 1993
"... A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural netwo ..."
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Cited by 441 (0 self)
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A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural
The Multiparameter Conjugate Gradient Algorithm
, 2001
"... The multiparameter conjugate gradient algorithm is a generalization of the conjugate gradient algorithm for the solution of systems of linear equations with a symmetric positive de nite matrix. Some algebraic properties of this algorithm are proved and its convergence is studied. 1 ..."
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Cited by 1 (0 self)
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The multiparameter conjugate gradient algorithm is a generalization of the conjugate gradient algorithm for the solution of systems of linear equations with a symmetric positive de nite matrix. Some algebraic properties of this algorithm are proved and its convergence is studied. 1
The proximalproximal gradient algorithm
, 2013
"... We consider the problem of minimizing a convex objective which is the sum of a smooth part, with Lipschitz continuous gradient, and a nonsmooth part. Inspired by various applications, we focus on the case when the nonsmooth part is a composition of a proper closed convex function P and a nonzero aff ..."
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affine map, with the proximal mappings of τP, τ> 0, easy to compute. In this case, a direct application of the widely used proximal gradient algorithm does not necessarily lead to easy subproblems. In view of this, we propose a new algorithm, the proximalproximal gradient algorithm, which admits easy
Convergence of Exponentiated Gradient Algorithms
, 1999
"... This paper studies three related algorithms: the (traditional) Gradient Descent (GD) Algorithm, the Exponentiated Gradient Algorithm with Positive and Negative weights (EG algorithm) and the Exponentiated Gradient Algorithm with Unnormalized Positive and Negative weights (EGU algorithm). These algor ..."
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Cited by 9 (2 self)
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This paper studies three related algorithms: the (traditional) Gradient Descent (GD) Algorithm, the Exponentiated Gradient Algorithm with Positive and Negative weights (EG algorithm) and the Exponentiated Gradient Algorithm with Unnormalized Positive and Negative weights (EGU algorithm
Hierarchical policy gradient algorithms
 in Marine Environments
, 2003
"... Hierarchical reinforcement learning is a general framework which attempts to accelerate policy learning in large domains. On the other hand, policy gradient reinforcement learning (PGRL) methods have received recent attention as a means to solve problems with continuous state spaces. However, they s ..."
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Cited by 14 (5 self)
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, they suffer from slow convergence. In this paper, we combine these two approaches and propose a family of hierarchical policy gradient algorithms for problems with continuous state and/or action spaces. We also introduce a class of hierarchical hybrid algorithms, in which a group of subtasks, usually
The geometry of algorithms with orthogonality constraints
 SIAM J. MATRIX ANAL. APPL
, 1998
"... In this paper we develop new Newton and conjugate gradient algorithms on the Grassmann and Stiefel manifolds. These manifolds represent the constraints that arise in such areas as the symmetric eigenvalue problem, nonlinear eigenvalue problems, electronic structures computations, and signal proces ..."
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Cited by 642 (1 self)
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In this paper we develop new Newton and conjugate gradient algorithms on the Grassmann and Stiefel manifolds. These manifolds represent the constraints that arise in such areas as the symmetric eigenvalue problem, nonlinear eigenvalue problems, electronic structures computations, and signal
Comparing PolicyGradient Algorithms
 IEEE Trans. on Systems, Man, and Cybernetics
, 1983
"... We present a series of formal and empirical results comparing the efficiency of various policygradient methods—methods for reinforcement learning that directly update a parameterized policy according to an approximation of the gradient of performance with respect to the policy parameter. Such metho ..."
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Cited by 1 (0 self)
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We present a series of formal and empirical results comparing the efficiency of various policygradient methods—methods for reinforcement learning that directly update a parameterized policy according to an approximation of the gradient of performance with respect to the policy parameter
Bayesian policy gradient algorithms
 Advances in Neural Information Processing Systems 19
, 2007
"... Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a performance gradient estimate. Conventional policy gradient methods use MonteCarlo techniques to estimate this gradient. Since Monte Carlo methods tend to have high variance, a large numbe ..."
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Cited by 19 (2 self)
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Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a performance gradient estimate. Conventional policy gradient methods use MonteCarlo techniques to estimate this gradient. Since Monte Carlo methods tend to have high variance, a large
Learning to rank using gradient descent
 In ICML
, 2005
"... We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data f ..."
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Cited by 510 (17 self)
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We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
 ACM Trans. Math. Software
, 1982
"... An iterative method is given for solving Ax ~ffi b and minU Ax b 112, where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytically equivalent to the standard method of conjugate gradients, but possesses more favorable numerica ..."
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Cited by 649 (21 self)
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An iterative method is given for solving Ax ~ffi b and minU Ax b 112, where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytically equivalent to the standard method of conjugate gradients, but possesses more favorable
Results 1  10
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352,508