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Compatible discrete Maxwell constraints and finite algorithms — Examples
1 · Prerequisites
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Cm Math Dynamics
- Compatible discrete Maxwell constraints and finite algorithms
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- 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
- Cyclic Groups and Direct Products
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Limits of Real Functions
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Metric Spaces
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Simple Field Extensions and the Construction of the Complex Numbers
- Splitting Fields
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Galois Correspondence
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The ZFC Axioms and the Basic Set Constructions
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
3 · Logical flowchart
4 · Definitions, postulates, theorems and proofs
None yet.
5 · Examples, experiments and counterexamples
Constraint preservation does not imply bounded leapfrog evolution
Statement refuted
The statement refuted is: for every finite cochain datum of the type defined in the item named “Finite cochain Maxwell phase space,” every positive rational step size in the item named “Finite compatible leapfrog evolution,” and every initial state satisfying and , repeated application of its one-step map has a bounded orbit. Here “bounded orbit” means that the Euclidean norm of the coordinate vector has finite supremum over all integers .
Statement references
Facts & Assumptions
Given: A finite real cochain complex, its positive-definite mass maps, and the one-step update in Finite compatible leapfrog evolution.
Finite cochain Maxwell phase space: the constraints are and , with .
Finite compatible leapfrog evolution: one step is followed by .
Exact discrete constraints and semidiscrete and staggered energy: the real one-step map preserves both constraints for every , and on the algorithm's rational input domain it is the exact rational update; when , its is the Euclidean operator norm of .
Define , , , and , with the displayed standard bases. Take the one-step size , initial data and , and matrices
Counterexample
The matrices in [A1] are the signed boundary-incidence maps of one oriented tetrahedron, with edges ordered , faces ordered , and cell orientation . Matrix multiplication gives and . Also and , so the initial state satisfies both constraints.
Put , so . Direct multiplication gives . For every , this gives . For real coordinate vectors , direct expansion gives , hence . Applying this with and gives . For , applying the same identity with and gives for unit ; if equality is attained at , while if both sides are zero. Thus and for every . Since , is unit and ; consequently is unit and . Thus the operator norm of is exactly , attained at . Here , so the defined in [F3] is . For a state , , the update in [F2] with gives and , hence the invariant two-dimensional subspace has matrix . The initial state has modal coordinates .
The matrix in step 2.1 has trace , determinant , and eigenvalues and , found by solving . Since , and . Direct substitution gives eigenvectors and ; their determinant is , so write the initial modal vector as . The coefficient is nonzero, since otherwise would be a multiple of , whose second coordinate is nonzero. The first modal coordinate after steps is therefore . If were bounded, then would be bounded because , so their difference would be bounded, contradicting and . Since , the state orbit is unbounded.
By [F3], each update preserves both constraints, and step 1.1 sets them to zero initially. Explicitly, and , so induction gives zero constraints at every iterate. Thus exact constraint preservation holds at every step although this rational finite-complex algorithm has an unbounded orbit for . This refutes the stated bounded-orbit claim.