| name | majorization-supermodularity-information |
| description | Majorization lattice supermodularity and subadditivity framework — two structural majorization relations (precursors) underlying supermodularity and subadditivity of all sum-concave functions including Tsallis, Rényi, and Shannon entropies on the majorization lattice. Applies to quantum information theory, entropy inequalities, lattice theory. Activation: majorization, supermodularity, subadditivity, majorization lattice, Tsallis entropy, Rényi entropy, sum-concave, information theory lattice, 信息论格, 优超格 |
| arxiv_id | 2605.30331 |
| arxiv_date | 2026-05-28 |
Majorization Lattice Supermodularity & Subadditivity Framework
Source
- arXiv: 2605.30331 — "Majorization precursors to supermodularity and subadditivity on the majorization lattice"
- Authors: Alexander Stévins, Michael G. Jabbour, Serge Deside, Nicolas J. Cerf
- Category: Information Theory (cs.IT)
- Date: 2026-05-28
Core Concept
This paper establishes two structural majorization relations called "precursors" that underlie the properties of supermodularity and subadditivity on the lattice induced by majorization. These precursors immediately imply that all sums of concave functions ("sum-concave functions") are supermodular and subadditive on the majorization lattice.
Key Results
1. Majorization Precursors
Two structural relations on the majorization lattice that serve as foundations for:
- Supermodularity: f(x ∨ y) + f(x ∧ y) ≥ f(x) + f(y)
- Subadditivity: f(x ∨ y) ≤ f(x) + f(y) - f(x ∧ y)
2. Entropy Families Covered
- Tsallis entropies (for all α): proven supermodular and strictly subadditive
- Rényi entropies (for all α): proven supermodular and strictly subadditive
- Shannon entropy: recovered as special case, proven strictly supermodular and strictly subadditive
3. Strengthened Inequalities
- (i) All entropic functionals are strictly subadditive on the majorization lattice
- (ii) Tsallis entropies (and Shannon entropy) are strictly supermodular on the majorization lattice
Mathematical Framework
Majorization Lattice
- The majorization preorder induces a lattice structure on probability distributions
- For distributions x, y: x ∧ y = meet (greatest lower bound), x ∨ y = join (least upper bound)
- Sum-concave functions: functions that are sums of concave functions applied to individual components
Precursor Relations
The two structural majorization relations serve as "precursors" — stronger statements that immediately imply supermodularity and subadditivity for the entire class of sum-concave functions.
Applications
Quantum Information Theory
- Nicolas J. Cerf is a leading quantum information theorist
- Majorization is fundamental in quantum state transformation, entanglement theory
- These results strengthen the mathematical foundation of quantum entropy inequalities
- Applicable to quantum resource theories where majorization determines state convertibility
Classical Information Theory
- Strengthens known entropy inequalities (Shannon, Tsallis, Rényi)
- Provides lattice-theoretic understanding of entropy properties
- Unifies treatment of different entropy families under a single framework
Machine Learning & Optimization
- Sum-concave functions appear in regularization, information-theoretic objectives
- Supermodularity enables efficient optimization (greedy algorithms with guarantees)
- Subadditivity bounds for information-theoretic generalization bounds
Reusable Patterns
Pattern 1: Lattice-Theoretic Entropy Analysis
Problem: Prove entropy inequality on probability distributions
Approach:
1. Identify the relevant lattice structure (majorization lattice)
2. Find structural "precursor" relations on the lattice
3. Show the entropy function is sum-concave
4. Apply precursor → supermodularity/subadditivity follows immediately
Pattern 2: Unification via Sum-Concavity
Problem: Multiple entropy families need separate proofs
Approach:
1. Show all target functions are sum-concave (sum of concave functions)
2. Prove precursor relation holds on the lattice
3. All sum-concave functions inherit the property simultaneously
4. Strengthens to strict inequality where possible
Pattern 3: Strengthening Known Results
Problem: Existing inequality is non-strict
Approach:
1. Start with known non-strict inequality
2. Analyze equality conditions on the lattice
3. Show equality holds only in trivial/degenerate cases
4. Conclude strict inequality for all non-trivial inputs
Connections to Existing Skills
- quantum-entropy-inequalities: This provides the lattice-theoretic foundation for many quantum entropy inequalities
- majorization-quantum-state-transform: Majorization is the core ordering relation in quantum state transformation theory
- information-theoretic-generalization: Subadditivity of entropy connects to information-theoretic generalization bounds
Activation Keywords
majorization, supermodularity, subadditivity, majorization lattice, Tsallis entropy, Rényi entropy, Shannon entropy, sum-concave function, information theory, lattice theory, quantum information theory, entropy inequalities, 优超, 次可加性, 超模性, 格理论
