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 natural logarithm has derivative 1/x and equals the integral from 1 to x of 1/t
Statement
For , is differentiable and
Facts & Assumptions
Given: A positive real .
If a differentiable injective function has nonzero derivative, its inverse is differentiable at an image point and its derivative is the reciprocal of the original derivative (Derivative of an inverse: if is continuous and injective on a nondegenerate interval and differentiable at with , then the inverse is differentiable at with ; and if then is not differentiable at ).
for every real (The exponential function is smooth and ).
The reciprocal of a nonvanishing continuous real function is continuous, and the first FTC differentiates when is continuous (Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function, The first fundamental theorem: if is integrable on and continuous at , then ; in particular a continuous has as a primitive, The integral with oriented limits: and ).
A differentiable real function with zero derivative on an interval is constant (A function continuous on an interval whose derivative vanishes at every interior point of is constant on ; consequently two such functions with the same derivative differ by a constant).
Proof
At , ; hence the inverse rule gives .
The function is continuous on , so satisfies and .
The difference has derivative zero on , and is zero at , so it is identically zero.
Depends on
- The natural logarithm as the inverse of the exponential function
- Derivative of an inverse: if $f$ is continuous and injective on a nondegenerate interval $I$ and differentiable at $c \in I$ with $f'(c) \ne 0$, then the inverse $g$ is differentiable at $f(c)$ with $g'(f(c)) = 1/f'(c)$; and if $f'(c) = 0$ then $g$ is not differentiable at $f(c)$
- The exponential function is smooth and $(\exp)'=\exp$
- The first fundamental theorem: if $f$ is integrable on $[a,b]$ and continuous at $c$, then $F'(c) = f(c)$; in particular a continuous $f$ has $F$ as a primitive
- The integral with oriented limits: $\int_a^a f := 0$ and $\int_b^a f := -\int_a^b f$
- Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function
- A function continuous on an interval $I$ whose derivative vanishes at every interior point of $I$ is constant on $I$; consequently two such functions with the same derivative differ by a constant
Used by
- Finite-bit Box–Muller with certified elementary-function evaluators Algorithm
- One-sample natural error in the Gaussian model Corollary
- A power weight fails at both Aₚ endpoints Counterexample
- An almost-Lipschitz vector field has a unique solution through zero but is not locally Lipschitz there Counterexample
- An optimized mean-field law can lose posterior correlation Counterexample
- Hilbert transform is not strong type (1,1) Counterexample
- Identifiability and KL support do not replace testability Counterexample
- Iid strong law fails at infinite absolute mean Counterexample
- Marginal sub-Gaussianity does not justify an anytime confidence sequence Counterexample
- Omitting the start-time discount changes the policy gradient Counterexample
- Point evaluation is unbounded below the Sobolev continuity threshold Counterexample
- The claimed univariate clean-quantile count exponent is too strong Counterexample
- The complementarity product needs extra regularity Counterexample
- The critical Riesz potential can diverge and be essentially unbounded Counterexample
- The fundamental Hessian is not absolutely locally integrable Counterexample
- Conjugate-form prior for an exponential family Definition
- Differentiable stationary stochastic policies and score vectors Definition
- Fixed-particle finite-temperature density-matrix free energy Definition
- Heat balls and their time slices Definition
- Score and Fisher information Definition
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- A Dirac mass has precisely sufficiently negative Sobolev order Example
- A finite Bernoulli large-deviation tail Example
- A one-observation Bernoulli-logit Fisher-scoring step Example
- A one-parameter density prior meets the logarithmic contraction conditions Example
- A regularized logistic finite sum with an exact minimizer Example
- A sieve can miss the entropy bound while the posterior contracts exactly Example
- A small sensitivity solve error can reverse a two-design ranking Example
- A state baseline can eliminate this estimator's variance Example
- A strictly plurisubharmonic exhaustion of the convex unit ball Example
- An exponential best-error rate transferred to Chebyshev interpolation Example
- An interior Cauchy principal value reduces to a regular integral Example
- Cauchy law and its characteristic function Example
- Complete separation prevents a finite logistic MLE Example
- Endpoint sensor times are D-optimal for an intercept-slope model Example
- Exact CPO cost feasibility can disagree with a sampled line-search cost check Example
- Flux normalization on every centered sphere Example
- Gauss–Jacobi regularization for x to the minus one half times log x Example
…and 163 more results.
Dependency tree · two levels
53 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
- J. Lebl, Basic Analysis, Logarithm and Exponential (standard reference, not scraped)
- MIT OpenCourseWare 18.100B Real Analysis, Spring 2025 full lecture notes (standard reference, not scraped)