Use this optimization step or heuristic to study an explicitly defined objective and constraints with reproducible parameters, initialization, and stopping rules.

Parameter

Required number input.

Gradient

Required number input.

Accumulated squared gradient

Required number input.

Learning rate

Required number input.

Epsilon

Required number input.

Calculate one scalar Adagrad parameter update and its accumulated gradient state. 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]

Adagrad step

The resulting adagrad step returned as an object.

• 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.

Scale variables, monitor objective and constraint residuals, compare starts and algorithms, and verify small instances or local optimality with an independent solver.

• AdaDelta Optimizer Step Calculator

  Calculate one scalar AdaDelta parameter update and its accumulated state.

• RMSProp Optimizer Step Calculator

  Calculate one scalar RMSProp parameter update and its moving squared-gradient state.

• Adam Optimizer Step Calculator

  Calculate one scalar Adam parameter update with bias-corrected moments.