proximal gradient descent
Proximal gradient descent is an optimization algorithm used to minimize a function's value by iteratively moving towards the function's minimum. It combines gradient descent, which uses the derivative of the function to find the direction of steepest descent, with a proximity operator that incorporates additional constraints or penalties on the solution. This method enables effective optimization when dealing with functions that have specific structures or constraints, allowing for better convergence and more accurate solutions.
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Similar Concepts
- accelerated gradient descent methods
- batch gradient descent
- conjugate gradient descent
- constraints in gradient descent optimization
- 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
- second-order methods in gradient descent
- stochastic gradient descent
- variants of gradient descent algorithms