Gradient Descent Optimizer Step
Apply one gradient-descent update x − η∇f; the caller supplies the gradient and learning rate.
Description
Apply one gradient-descent update x − η∇f; the caller supplies the gradient and learning rate.
Gradient Descent Optimizer Step: Apply one gradient-descent update x − η∇f; the caller supplies the gradient and learning rate.
When to use Gradient Descent Optimizer Step
Use this optimization step or heuristic to study an explicitly defined objective and constraints with reproducible parameters, initialization, and stopping rules.
- position
- Required list input.
- gradient
- Required list input.
- learning Rate
- Required number input.
How Gradient Descent Optimizer Step works
Apply one gradient-descent update x − η∇f; the caller supplies the gradient and learning rate. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.1
- Next position
- The resulting next position returned as a list.
Limitations and assumptions
- Convergence and solution quality depend on smoothness, convexity, scaling, gradients, conditioning, hyperparameters, randomness, constraints, and implementation details. Non-convex methods need not find a global optimum.
- Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.
Alternative or Complementary approaches
Scale variables, monitor objective and constraint residuals, compare starts and algorithms, and verify small instances or local optimality with an independent solver.
References
-
Mathematical optimization — Wikipedia contributors
Similar or alternative tools
- Nadam Optimizer Step
Apply a bias-corrected Nesterov Adam update from caller-supplied first and second moments and the one-based iteration.
- Steepest Descent Optimizer Step
Move a specified Euclidean distance along the negative normalized gradient; this differs from learning-rate gradient descent.
- Stochastic Gradient Descent Step Calculator
Apply one stochastic-gradient-descent parameter update from a supplied gradient.