Nesterov Accelerated-Gradient Step
Calculate one scalar Nesterov momentum update from a gradient evaluated at the lookahead point.
Description
Calculate one scalar Nesterov momentum update from a gradient evaluated at the lookahead point.
Nesterov Accelerated-Gradient Step: Calculate one scalar Nesterov momentum update from a gradient evaluated at the lookahead point.
When to use Nesterov Accelerated-Gradient Step
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.
- Velocity
- Required number input.
- Lookahead gradient
- Required number input.
- Learning rate
- Required number input.
- Momentum
- Required number input.
How Nesterov Accelerated-Gradient Step works
Calculate one scalar Nesterov momentum update from a gradient evaluated at the lookahead point. 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
- Nesterov step
- The resulting nesterov step returned as an object.
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
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