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Stochastic Optimization/Evidence
Method evidence record

Stochastic Optimization

Stochastic optimization is a family of iterative methods that minimize an objective function by computing gradients on randomly sampled subsets of data — mini-batches — rather than on the entire dataset at once. Pioneered by Robbins and Monro in 1951 as stochastic approximation, the approach became the standard engine for training large-scale machine-learning models through variants such as SGD with momentum, AdaGrad, RMSProp, and Adam.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Stochastic Optimization (SGD and Variants)
Taxonomic method record · process-pipeline / optimization
  • Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407. · DOI 10.1214/aoms/1177729586
  • Kingma, D.P. & Ba, J. (2015). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR 2015). · URL
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Curated claims

Claims persisted in the evidence ledger, each with its own assessment.

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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Same method familyBayesian Optimizationmachine-suggested · Relational suggestion, not evidence.Same method familyEvolutionary Strategymachine-suggested · Relational suggestion, not evidence.Same method familyRobust Optimizationmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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