accelerated gradient descent methods
Accelerated gradient descent methods are optimization algorithms that enhance the standard gradient descent approach by incorporating extra information from previous iterations to accelerate convergence and reach the optimal solution faster.
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Similar Concepts
- batch gradient descent
- conjugate gradient descent
- convergence of gradient descent
- gradient descent for linear regression
- gradient descent for neural networks
- hybrid optimization algorithms combining gradient descent
- mini-batch gradient descent
- non-convex optimization using gradient descent
- online gradient descent
- parallel and distributed gradient descent
- proximal gradient descent
- regularization techniques in gradient descent
- second-order methods in gradient descent
- stochastic gradient descent
- variants of gradient descent algorithms