Stochastic gradient descent is an optimization algorithm that iteratively updates parameters by computing the gradient of an objective function on randomly sampled mini-batches of data rather than the full dataset, trading gradient accuracy for computational efficiency. It is parameterized by learning rate, mini-batch size, and number of iterations, and persists through implementation in optimization libraries and computational frameworks. [formal: sgd | substrate: behavior | horizon: hours | explicit: yes | epoch: 0.01]
Accepted ontology entry
stochastic gradient descent
Stochastic gradient descent is an optimization algorithm that iteratively updates parameters by computing the gradient of an objective function on randomly sampled mini-batches of data rather than the full dataset, trading gradient accurac…
Definition
Why it is in scope
A human-authored optimization algorithm that approximates the true gradient of an objective function by computing it on small random subsets (mini-batches) of data, enabling scalable learning in high-dimensional parameter spaces. Built by statistical learning theorists and implemented in optimization libraries.
Names and aliases
- stochastic gradient descenten · CANONICAL
Relations from this entry
- cmsllb31s07h4nobp1h0i7lu3INSTANCE_OF →
Tested direction: specific→general (Law 9). Stochastic gradient descent is a specific kind of optimization algorithm that uses noisy gradient estimates on mini-batches. A competent speaker would call SDE 'an optimization algorithm'.
- cmsd5x8jv03ih3vv3xpnf9qdkDERIVED_FROM →
Tested direction: which-came-first (Law 7). Gradient descent existed first as the deterministic optimization method; stochastic gradient descent was derived from it by adding noisy mini-batch sampling to make it scalable. Y (gradient descent) predates and fed into X (SGD).
- cmslw6qf308b6nobpim6pb0bzDEPENDS_ON →
Stochastic gradient descent is an optimization algorithm that uses gradient estimates to update model parameters. Remove gradient and SGD stops operating — it literally computes and follows gradients. The removal test passes.
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Record identity
- Created
- Aug 10, 2026, 12:27 AM UTC
- Content hash
- 2c8011120e59d45ce662f480a74f61b375ba9c05f9ef3588733365a44f0ded9d