How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
The Axiom of Choice
Definition
The Axiom of Choice (AC) is the following statement.
Every family of nonempty sets has a choice function (Choice function).
Written out: for every set all of whose members are nonempty, there exists a function with domain satisfying for all .
An equivalent formulation is that a product of nonempty sets is nonempty: if for every , then . Here is the set of functions with domain such that for every ; when a family of nonempty sets is indexed by itself, such an is precisely a choice function for it.
Definition references
Remarks
- This is an axiom, not a theorem. The results on this page prove equivalences and consequences from AC; they do not assume an independence theorem in order to define or use it. The proved choice ledger: hypotheses, equivalences, and upper bounds records only the implications and proof costs established by local results.
- Being an axiom, AC carries no well-definedness obligation, which is why this
item has no
justified_by. - The case of a family listed by a natural number, which is the finite case once finiteness is defined, is a theorem of ZF and needs no axiom (Every natural-number-indexed list of nonempty sets has a choice function on its family of values). AC is exactly the extension of that theorem to arbitrary index sets, and the gap between the two is not a matter of degree: Russell's shoes and socks exhibits the difference concretely.
- "ZFC" abbreviates ZF together with AC. A result that invokes AC should say so where it is stated, so that a reader can tell which theorems are choice-free; that bookkeeping is the purpose of The proved choice ledger: hypotheses, equivalences, and upper bounds. The proved choice cost of the ultrafilter lemma records the narrower, locally proved upper bound for the ultrafilter lemma. Its exact reverse implications wait for the later Boolean-algebra and symmetric-model development.
Depends on
Used by
- ABC Metropolis–Hastings on simulator-augmented states Algorithm
- Autonomous Euler–Maruyama scheme and interpolation Algorithm
- BAOAB underdamped Langevin splitting Algorithm
- Bias-corrected and accelerated bootstrap interval Algorithm
- Bias-corrected bootstrap interval Algorithm
- Border-corrected estimator for Ripley K Algorithm
- Bulk effective sample size Algorithm
- Certified near-quadrature and far-multipole matrix assembly Algorithm
- Colored Jones evaluation from ribbon data Algorithm
- Compute the twisted Burau determinant for a colored braid closure Algorithm
- Conditional flow matching for inverse posteriors Algorithm
- Dyadic Brownian bridge increment tree Algorithm
- Episodic REINFORCE with a fixed state baseline Algorithm
- Episodic REINFORCE with a frozen episode policy Algorithm
- Epsilon-prediction diffusion objective estimator Algorithm
- Euler–Maruyama on a predictable grid with bridge-refinement status Algorithm
- Euler–Maruyama recursion Algorithm
- Exact EM updates for a finite Gaussian mixture Algorithm
- Expectation-maximization Algorithm
- Finite-candidate Gaussian-process acquisition search with explicit statuses Algorithm
- Finite-horizon doubly robust off-policy value evaluation Algorithm
- Finite-horizon square-root Kalman filter by rectangular factors and QR Algorithm
- Finite-simulation plug-in synthetic-likelihood evaluation Algorithm
- Finite-sum SAGA Algorithm
- Finite-sum SVRG Algorithm
- Full-resampling fixed-level SIR splitting with invariant mutations Algorithm
- Inverse probability weighting for a causal mean Algorithm
- Metropolis-adjusted Langevin algorithm (MALA) Algorithm
- Metropolis–Hastings kernel Algorithm
- One-step episodic actor–critic with a bootstrapped critic Algorithm
- Ordinary, per-decision, and weighted trajectory importance sampling Algorithm
- Particle marginal Metropolis–Hastings Algorithm
- Perturbed-observation ensemble Kalman filter Algorithm
- Predictor-corrector sampling for score SDEs Algorithm
- Prior-preserving Crank–Nicolson sampler Algorithm
- Quantile Monte Carlo standard error Algorithm
- Rank-normalized folded split R-hat Algorithm
- Rao–Blackwellized particle filter Algorithm
- Recursive Bayesian filtering under dominated observations Algorithm
- Recursive group-sequential boundary calibration Algorithm
…and 7094 more results.
Dependency tree · two levels
5 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- I. Khatchatourian, The Axiom of Choice (University of Toronto MAT327 notes) (standard reference, not scraped)
- Axiom of choice (Wikipedia) (standard reference, not scraped)
- Choice function (Wikipedia) (standard reference, not scraped)