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Mixture Dynamics And Shells — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Areas of Elementary Plane Figures
- Binary Operations, Monoids, Groups and Subgroups
- Compactness
- Compactness in Metric Spaces
- Complete Metrizability, Čech-Completeness, and Baire Category
- Completeness, Completion, and Uniform Continuity
- Complex Differentiability and the Cauchy–Riemann Equations
- Complex Lp Spaces and Test-Function Conventions
- Connectedness
- 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
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Countability Axioms and Cardinal Functions
- Density Separability and Convolution in Lᵖ
- Determinants of Matrices over a Commutative Ring
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Euclidean Ordinary Differential Equations with Smooth Dependence
- Exterior Powers, Orientation and Hodge Duality
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Fubini and Change of Variables
- Fundamental Trigonometric Identities
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Hamiltonian Mechanics and Completely Integrable Systems
- Hereditary and Productive Behaviour of the Separation Axioms
- Hilbert Space Geometry and Riesz Representation
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Lebesgue Measure on Euclidean Space
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Manifolds with Boundary Collars and Orientations
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Measurable Functions and Simple Approximation
- Measure Preserving Transformations and Poincare Recurrence
- Measure-Preserving Systems and Mixing Criteria
- Measures and Their Basic Properties
- Metric Spaces
- Mixed Partials, Taylor Formulae, and Extrema
- Mixture Dynamics And Shells
- Modes of Convergence Egorov and Lusin
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Outer Measure and the Caratheodory Extension Theorem
- Partitions of Unity and Paracompactness
- Picard-Lindelöf and First-Order Ordinary Differential Equations
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Properties of the Integral and the Working FTC
- Rank Theorems and Embedded Submanifolds
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Separation Axioms: the Hierarchy
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Signed and Complex Measures Hahn and Jordan
- Simple Field Extensions and the Construction of the Complex Numbers
- Sine, Cosine, and the Definition of Pi
- Smooth Manifolds and Smooth Maps
- Smooth Partitions of Unity and Exhaustions
- Smooth Vector Bundles and Sections
- Subspaces, Products, and Quotients
- Suprema and Infima
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- Symplectic Manifolds, Moser Stability, and Darboux–Weinstein Theory
- Tangent Cotangent and the Differential
- Tensor Fields Exterior Algebra and Differential Forms
- The Cantor Set, Baire Category, and Measure Zero in ℝ
- The Complex Exponential and Euler's Formula
- The Derivative and the Mean Value Theorems
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Ergodic Theorems of von Neumann and Birkhoff
- The Exponential Function
- The Exterior Derivative and Cartan Calculus
- The Fundamental Theorems of Calculus
- The Inverse and Implicit Function Theorems
- The Inverse Function Theorem Completed
- The Lebesgue Integral and the Convergence Theorems
- The Logarithm and General Powers
- The Lᵖ Spaces Holder Minkowski and Riesz Fischer
- The Radon Nikodym Theorem and Lebesgue Decomposition
- The Riemann Integral in Rᵐ and Jordan Content
- The Riemann Integral: Definition and Integrability
- The Topology of Euclidean Space
- The Total Derivative in ℝᵐ → ℝⁿ
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Fields Flows and Lie Derivatives
- Vector Spaces, Linear Subspaces, Span and Direct Sums
- Weak Convergence Tightness and Representation
2 · Summary
These leaf examples give a noncompact mixture with an unattained infimum, a finite sequence with controlled nonconcave entropy and ensemble inequivalence, a complete finite-volume Hamiltonian flow that is not ergodic, and a two-level pure state whose time-averaged density has positive entropy. Each example identifies the model and its exact mathematical conclusion; none is reported as an experiment or observation.
3 · Logical flowchart
4 · Definitions, postulates, theorems and proofs
None yet.
5 · Examples, experiments and counterexamples
Noncompact mixtures can have an unattained energy infimum
Statement refuted
For every nonempty Borel generator set and continuous energy bounded below, the unconstrained infimum of over Borel probability measures on is attained whenever it is finite, without a compactness or coercivity hypothesis.
Example
Extend the objective in Compact phase-generator mixture to the noncompact Borel generator set with energy , and impose no barycenter constraint. The infimum of over Borel probability measures is , but no probability measure attains it. This shows that a general unconstrained attainment claim needs compactness or a substitute such as coercivity; the example makes no assertion about the barycenter-constrained theorem Attainment and finite support for compact continuous mixtures.
Facts & Assumptions
Given: The explicit noncompact set, continuous energy, and unconstrained Borel-probability objective.
, , and ranges over all Borel probability measures on with objective .
Compact phase-generator mixture defines the mean energy ; its constrained mixture energy is an infimum of those means at a prescribed barycenter. Here [A1] extends only the mean-energy objective to the stated noncompact set and omits the constraint. Monotonicity and nonnegative homogeneity of the nonnegative integral gives monotonicity, and The integral of a nonnegative simple function together with The nonnegative integral agrees with the simple integral on simple functions gives for . Since and , these rules also give .
Attainment and finite support for compact continuous mixtures: compactness of the generator space is among the hypotheses of its attained-minimum result.
Counterexample
Given: [A1] and the mixture objective [F1].
For each integer , the set is Borel, and . Since , at least one has positive probability: otherwise the disjoint sets all have probability zero, and countable additivity would give . On , , while elsewhere , so Therefore every admissible probability has strictly positive objective.
The point mass at is a Borel probability on and has objective . These values tend to zero, while 1.1 shows all objectives are positive. Hence the infimum is zero and is not attained. The generator set is noncompact, so this conclusion does not contradict the compact result [F2]; it shows why an attainment theorem must retain its compactness/coercivity hypotheses.
A controlled nonconcave entropy model with ensemble inequivalence
Statement refuted
For every sequence of finite systems with admissible exact-group microcanonical energy densities tending to an interior value , there is a fixed positive inverse temperature for which the canonical energy density concentrates at as system size tends to infinity, without further hypotheses such as entropy concavity.
Example
Fix an energy scale . For each and , set , , and Construct the finite state set with energy for each state in group . Its dimensionless exact-group entropy density tends uniformly on the grid to on , which is nonconcave. For fixed inverse temperature , put and let be the canonical log partition sum per site. Then For every compact , the canonical probability that the energy density lies in tends to zero, while an exact microcanonical group with is supported at that interior density and has entropy density tending to . This is an explicit constructed finite sequence demonstrating that ensemble equivalence does not follow from large alone.
Facts & Assumptions
Given: The finite group degeneracies, energies, and canonical weighting defined in the statement.
, , , and .
At , the group weight is ; an exact microcanonical group is uniform on its states.
The real exponential function and the number by a power series and The natural logarithm as the inverse of the exponential function: the exponential is positive and is its inverse on positive reals.
The natural logarithm has derivative 1/x and equals the integral from 1 to x of 1/t: is continuous and increasing on .
Heine-Borel in : with the Euclidean metric a subset of is compact if and only if it is closed and bounded, and the proof by bisection uses no choice principle; the same holds on the real line: closed bounded intervals are compact. A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value: a continuous real-valued function on a nonempty compact metric space attains its minimum and maximum.
Counterexample
Given: [A1]–[A2] and [F1]–[F3].
Since , . For , , so The error tends to zero uniformly in . The grid mesh is ; for a continuous function on , uniform continuity makes its grid maxima converge to its interval maximum.
Let and . The largest summand is and there are summands, so By 1.1, . If with , direct expansion gives strictly below the weighted endpoint values. Thus the maximum is at an endpoint and equals .
If , the probability is zero. Otherwise, for compact strict convexity gives by [F3]. Uniform approximation in 1.1 makes every grid exponent in at most the maximum minus for all sufficiently large . On the other hand, an endpoint group has exponent tending to the maximum. By [F2], , so the polynomial factor is dominated by the exponential gap and the canonical probability of , at most , tends to zero.
Choose integers with . The microcanonical exact-group law has energy density exactly at every state. By 1.1 its entropy density tends to . Thus, for example at the crossing , canonical mass escapes every compact neighborhood of any interior target while the microcanonical group remains there. Finally , so is not concave. The example is a constructed model, not an observation about a material.
A finite-volume Hamiltonian map that is not ergodic
Statement refuted
Every nonzero-time map of a complete Hamiltonian flow preserving finite symplectic volume is ergodic with respect to the normalized volume measure.
Example
On the symplectic torus with , let . The Hamiltonian flow is complete and preserves finite area, but every nonzero-time map is nonergodic: the invariant set has exactly half the total area. This gives a concrete reason volume preservation alone cannot identify time averages with whole-space ensemble averages.
Facts & Assumptions
Given: The symplectic torus, canonical Hamiltonian convention, and area measure.
are angular coordinates modulo , , and .
Symplectic form and symplectic manifold and Hamiltonian vector field and Hamiltonian function: a Hamiltonian vector field satisfies .
Hamiltonian volume, recurrence, and ergodic scope: a complete invariant Hamiltonian flow map preserves symplectic area.
Ergodicity relative to an invariant measure: ergodicity requires every invariant measurable set to have measure zero or full measure.
Counterexample
Given: [A1] and the canonical convention [F1].
Write . Then , while . Equating coefficients gives and . The explicit solution is which is defined for every real and is a diffeomorphism with inverse .
Since , the set is invariant under every . The torus has total area . The allowed values in have total length , while ranges over length , so , strictly between zero and total area. The map preserves area by [F2], and the invariant set violates the criterion [F3]; hence it is not ergodic for any nonzero sampling time.
Pure-state entropy and entropy of a dephased average
Example
Use with , linear in the first argument, and , . Choose real and define the self-adjoint energy operator . Fix . For set , and define , , and for define by entrywise integration. All limits below are as . Let , . Define the finite-dimensional entropy of a density matrix with eigenvalues as , with and fixed . Every instantaneous unitary state is pure and has entropy zero. The Cesàro states converge to , whose entropy is . In fact , whereas for every . Entropy of the averaged state and average instantaneous entropy are distinct quantities.
Facts & Assumptions
Given: The two-level discrete-spectrum model, pure initial density, and entropy convention in the statement.
are orthonormal, , , , , and for .
, , and , The complex exponential is entire and its complex derivative is itself, and The second fundamental theorem: if is differentiable on with and is integrable, then : imaginary exponential phases have modulus one, their derivatives give the explicit antiderivative, and the real and imaginary integrals are evaluated componentwise. The finite-dimensional Cesàro limit is computed here and does not invoke an uninstantiated infinite-dimensional Gibbs-spectrum theorem.
Real and complex inner-product spaces and their induced length gives the orthonormal-coordinate inner product; The complex exponential by its power series defines the complex exponential; , , and gives for real .
The natural logarithm as the inverse of the exponential function and The natural logarithm has derivative 1/x and equals the integral from 1 to x of 1/t: is continuous on positive reals and is finite.
Verification
Given: [A1] and the entropy convention.
The unitary evolution sends to , which remains a unit vector by [F2]. Its rank-one density has eigenvalues , so its entropy is for every .
The two energy eigenspaces are one-dimensional. The matrix entry between them is an exponential with nonzero frequency; by [F1] its average has magnitude at most and tends to zero. The diagonal entries remain , so the direct finite-dimensional average yields . Its eigenvalues are , so Since every , the time average of instantaneous entropy is exactly zero.
In the basis , has diagonal entries and off-diagonal entry whose magnitude is at most by direct integration. Its eigenvalues are , so both tend to . Continuity of at gives . Thus entropy of the averaged state has a nonzero limit even though the averaged instantaneous entropy remains zero.