There are a number of algorithms that can be categorized as gradient based. One such algorithm is the Dynamic Integrated Systems Optimization and Parameter Estimation algorithm meant for solving nonlinear optimal control problems. A common trait to gradient based algorithms is that their search direction is determined by the gradient vector of the objective function. One peculiar behavior of gradient based searches is that any two consecutive directions would be perpendicular to each other. Because of this, the path of the search will zigzag towards the optimal solution. The zigzag movement of the search is associated with the unfavorable e?ect of slowing down the algorithms especially when the surface of the function is in the form of ravi...
International audienceWe describe a convergence acceleration scheme for multistep optimization algor...
237 pagesIt seems that in the current age, computers, computation, and data have an increasingly imp...
this paper we extend the algorithm to a scaled gradient projection. The diagonal scaling matrix appr...
Batch gradient descent, \Deltaw(t) = \GammajdE=dw(t), converges to a minimum of quadratic form with ...
A momentum term is usually included in the simulations of connectionist learning algorithms. Althoug...
A momentum term is usually included in the simulations of connectionist learning algorithms. Althoug...
Momentum based learning algorithms are one of the most successful learning algorithms in both convex...
It is pointed out that the so called momentum method, much used in the neural network literature as ...
The back propagation algorithm has been successfully applied to wide range of practical problems. Si...
This research proposes and investigates some improvements in gradient descent iterations that can be...
This paper uses the dynamics of weight space probabilities [3, 4] to address stochastic gradient alg...
Batch gradient descent, ~w(t) = -7JdE/dw(t) , conver~es to a minimum of quadratic form with a time ...
The article examines in some detail the convergence rate and mean-square-error performance of moment...
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Comput...
Momentum methods have been shown to accelerate the convergence of the standard gradient descent algo...
International audienceWe describe a convergence acceleration scheme for multistep optimization algor...
237 pagesIt seems that in the current age, computers, computation, and data have an increasingly imp...
this paper we extend the algorithm to a scaled gradient projection. The diagonal scaling matrix appr...
Batch gradient descent, \Deltaw(t) = \GammajdE=dw(t), converges to a minimum of quadratic form with ...
A momentum term is usually included in the simulations of connectionist learning algorithms. Althoug...
A momentum term is usually included in the simulations of connectionist learning algorithms. Althoug...
Momentum based learning algorithms are one of the most successful learning algorithms in both convex...
It is pointed out that the so called momentum method, much used in the neural network literature as ...
The back propagation algorithm has been successfully applied to wide range of practical problems. Si...
This research proposes and investigates some improvements in gradient descent iterations that can be...
This paper uses the dynamics of weight space probabilities [3, 4] to address stochastic gradient alg...
Batch gradient descent, ~w(t) = -7JdE/dw(t) , conver~es to a minimum of quadratic form with a time ...
The article examines in some detail the convergence rate and mean-square-error performance of moment...
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Comput...
Momentum methods have been shown to accelerate the convergence of the standard gradient descent algo...
International audienceWe describe a convergence acceleration scheme for multistep optimization algor...
237 pagesIt seems that in the current age, computers, computation, and data have an increasingly imp...
this paper we extend the algorithm to a scaled gradient projection. The diagonal scaling matrix appr...