Select iteration coefficients according to the proved convergence condition when implementing stochastic approximation¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article supports a concrete implementation strategy: when deploying the Robbins-Monro procedure, choose the iteration-coefficient sequence so that it satisfies the necessary and sufficient convergence condition proved in the bulletin. This turns an abstract theorem into an implementable rule for designers of sequential estimation or adaptive measurement procedures, ensuring the estimate sequence converges with probability one and in the quadratic mean.

Design Implications¶

Context¶

Requirements¶

Verification that the chosen coefficient sequence meets the proved condition

Constraints¶

Applies to the Robbins-Monro procedure as analyzed in the article; no instructional implementation is reported

Target Learners¶

learners in adaptive testing or sequential instruction contexts

Target Learning Goals¶

reliable sequential estimation of learner parameters

Affordances¶

Robbins Monro Stochastic Approximation

Examples¶

Key Sources¶

Wolff, Hans. (1970). On Stochastic Approximation. https://eric.ed.gov/?id=ED051258

Page metadata

Field	Value
`type`	`strategy`
`id`	`coefficient-selection-strategy`
`title`	`Select iteration coefficients according to the proved convergence condition when implementing stochastic approximation`
`description`	`The article supports a concrete implementation strategy: when deploying the Robbins-Monro procedure, choose the iteration-coefficient sequence so that it satisfies the necessary and sufficient convergence condition pr...`
`status`	`review`
`generated`	by `process:wiki-ingest` on `2026-09-25`
`trust`	unverified — no one has confirmed this page's content
`sources`	1 — `wolff-1970`

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