Results 281 to 290 of about 2,575,606 (330)
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2006
The endeavour to solve systems of linear algebraic systems is already two thousand years old. In the paper we consider the conjugate gradient method that is (theoretically) finite but, in practice, it can be treated as an iterative method. We survey a known modification of the method, the preconditioned conjugate gradient method, that may converge ...
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The endeavour to solve systems of linear algebraic systems is already two thousand years old. In the paper we consider the conjugate gradient method that is (theoretically) finite but, in practice, it can be treated as an iterative method. We survey a known modification of the method, the preconditioned conjugate gradient method, that may converge ...
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2011
A policy gradient method is a reinforcement learning approach that directly optimizes a parametrized control policy by gradient descent. It belongs to the class of policy search techniques that maximize the expected return of a policy in a fixed policy class while traditional value function approximation approaches derive policies from a value function.
Peters, J., Bagnell, J.
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A policy gradient method is a reinforcement learning approach that directly optimizes a parametrized control policy by gradient descent. It belongs to the class of policy search techniques that maximize the expected return of a policy in a fixed policy class while traditional value function approximation approaches derive policies from a value function.
Peters, J., Bagnell, J.
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2014
One of the newest approaches in general nonsmooth optimization is to use gradient sampling algorithms developed by Burke, Lewis, and Overton. The gradient sampling method is a method for minimizing an objective function that is locally Lipschitz continuous and smooth on an open dense subset of \(\mathbb {R}^n\).
Adil Bagirov +2 more
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One of the newest approaches in general nonsmooth optimization is to use gradient sampling algorithms developed by Burke, Lewis, and Overton. The gradient sampling method is a method for minimizing an objective function that is locally Lipschitz continuous and smooth on an open dense subset of \(\mathbb {R}^n\).
Adil Bagirov +2 more
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2017
The gradient method belongs to the direct optimization methods , characterized by the fact that the extreme is found without any prior indication of the necessary existence conditions. Several results which refer to the first order approximation of the gradient method are presented in this chapter. The complexity of the treatment is gradually increased,
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The gradient method belongs to the direct optimization methods , characterized by the fact that the extreme is found without any prior indication of the necessary existence conditions. Several results which refer to the first order approximation of the gradient method are presented in this chapter. The complexity of the treatment is gradually increased,
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2014
In this chapter, we introduce two discrete gradient methods that can be considered as semi-derivative free methods in a sense that they do not use subgradient information and they do not approximate the subgradient but at the end of the solution process (i.e., near the optimal point). The introduced methods are the original discrete gradient method for
Adil Bagirov +2 more
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In this chapter, we introduce two discrete gradient methods that can be considered as semi-derivative free methods in a sense that they do not use subgradient information and they do not approximate the subgradient but at the end of the solution process (i.e., near the optimal point). The introduced methods are the original discrete gradient method for
Adil Bagirov +2 more
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1970
For the sake of simplicity and clarity we shall first consider the problem where there are no constraints of the form (2) or (3). We shall also assume that the final time T is given.
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For the sake of simplicity and clarity we shall first consider the problem where there are no constraints of the form (2) or (3). We shall also assume that the final time T is given.
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1994
In the following, A ∈ ℝ I x I and b ∈ ℝ I are real. We consider a system $$ Ax\, = \,b $$ (9.1.1) and assume that $$ A\,is\,positive\,definite. $$ (9.1.2) System (1) is associated with the function $$ F\left( x \right): = \,\frac{1}{2}\left\langle {Ax,\,x} \right\rangle \, - \,\left\langle {b,\,x} \right\rangle . $$ (9.1.3)
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In the following, A ∈ ℝ I x I and b ∈ ℝ I are real. We consider a system $$ Ax\, = \,b $$ (9.1.1) and assume that $$ A\,is\,positive\,definite. $$ (9.1.2) System (1) is associated with the function $$ F\left( x \right): = \,\frac{1}{2}\left\langle {Ax,\,x} \right\rangle \, - \,\left\langle {b,\,x} \right\rangle . $$ (9.1.3)
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