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Large-deviation variational mechanics and ensemble inequivalence — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Binary Operations, Monoids, Groups and Subgroups
- Compactness
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Countability and Uncountability
- Finite Counting, Factorials and Binomial Coefficients
- Finite Probability Spaces and Random Variables
- Foundations of the Real Numbers for Analysis
- Independence Borel Cantelli and Zero One Laws
- Large-deviation variational mechanics and ensemble inequivalence
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Method of Types and Finite Large Deviations
- Metric Spaces
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Order, Zorn's Lemma, and the Axiom of Choice
- Power Series and Real-Analytic Functions
- Probability Spaces Random Variables and Expectation
- Properties of the Integral and the Working FTC
- Quantum Communication and Fingerprinting
- Quantum Information Measures
- Relations, Functions, and Quotients
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Subspaces, Products, and Quotients
- Suprema and Infima
- The Derivative and the Mean Value Theorems
- The Exponential Function
- The Logarithm and General Powers
- The Riemann Integral: Definition and Integrability
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
3 · Logical flowchart
4 · Definitions, postulates, theorems and proofs
A strictly convex entropy interval with no canonical energies
Example
Take the binary alphabet with uniform reference and continuous energy-density map on . With and , the constrained entropy from the finite-alphabet mean-field model is for . For , Each such energy lies in an interval of strict convexity and therefore has no affine upper supporting line of any slope. Consequently none of these microcanonical energies is realized by a canonical mean-field ensemble at any .
Example references
Finite-alphabet mean-field probability ensembles
Finite-alphabet mean-field ensembles and upper supporting lines
The natural logarithm as the inverse of the exponential function
Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm
The natural logarithm has derivative 1/x and equals the integral from 1 to x of 1/t
Facts & Assumptions
Given: The binary alphabet, uniform reference, energy map, entropy convention, and the definitions and suppliers listed below.
Finite-alphabet mean-field probability ensembles: , , and the canonical variational objective is .
Finite-alphabet mean-field ensembles and upper supporting lines: an energy is canonical at slope only if on the whole energy range.
The natural logarithm as the inverse of the exponential function and Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm: is the inverse of on , is strictly increasing, and satisfies .
The chain rule, in one line from Carathéodory: if is differentiable at and is differentiable at , then is differentiable at with : derivatives of real function compositions obey the chain rule under the stated domain hypotheses.
The mean value theorem, as the case of Cauchy's: for continuous on with and differentiable on there is with : for a continuous function on a closed interval differentiable in its interior, the endpoint secant slope equals an interior derivative.
Verification
If , then forces and , so the constraint fiber is the singleton . By [F1], , and therefore . If , then forces and , again a singleton fiber. The relative-entropy formula in [F1] gives ; hence .
For , [F4]–[F5] give and therefore . Differentiating once more yields , where .
On , decreases and increases, so decreases. At , and . Since , [F3] gives ; and [F4] gives because on with strict inequality on . Thus . Also by integer multiplication, so and throughout . In particular on that interval. By applying [F6] first to and then to , positive second derivative makes strictly increasing and every interior chord lie strictly above the graph; hence is strictly convex on .
Fix and choose so that , possible because . Strict convexity from step 3.1 gives . If an upper supporting line with slope existed at , its inequalities at and would instead give , a contradiction. By [F2], no such energy is canonical for any real .
5 · Examples, experiments and counterexamples
None yet.