home / skills / plurigrid / asi / lasalle-invariance
This skill applies LaSalle invariance to analyze asymptotic stability and long-term behavior of dynamical systems.
npx playbooks add skill plurigrid/asi --skill lasalle-invarianceReview the files below or copy the command above to add this skill to your agents.
---
name: lasalle-invariance
description: Invariance principle for asymptotic stability
version: 1.0.0
---
# LaSalle Invariance
**Trit**: 1 (PLUS)
**Domain**: Dynamical Systems Theory
**Principle**: Invariance principle for asymptotic stability
## Overview
LaSalle Invariance is a fundamental concept in dynamical systems theory, providing tools for understanding the qualitative behavior of differential equations and flows on manifolds.
## Mathematical Definition
```
LASALLE_INVARIANCE: Phase space × Time → Phase space
```
## Key Properties
1. **Local behavior**: Analysis near equilibria and invariant sets
2. **Global structure**: Long-term dynamics and limit sets
3. **Bifurcations**: Parameter-dependent qualitative changes
4. **Stability**: Robustness under perturbation
## Integration with GF(3)
This skill participates in triadic composition:
- **Trit 1** (PLUS): Sources/generators
- **Conservation**: Σ trits ≡ 0 (mod 3) across skill triplets
## AlgebraicDynamics.jl Connection
```julia
using AlgebraicDynamics
# LaSalle Invariance as compositional dynamical system
# Implements oapply for resource-sharing machines
```
## Related Skills
- equilibrium (trit 0)
- stability (trit +1)
- bifurcation (trit +1)
- attractor (trit +1)
- lyapunov-function (trit -1)
---
**Skill Name**: lasalle-invariance
**Type**: Dynamical Systems / LaSalle Invariance
**Trit**: 1 (PLUS)
**GF(3)**: Conserved in triplet composition
## Non-Backtracking Geodesic Qualification
**Condition**: μ(n) ≠ 0 (Möbius squarefree)
This skill is qualified for non-backtracking geodesic traversal:
1. **Prime Path**: No state revisited in skill invocation chain
2. **Möbius Filter**: Composite paths (backtracking) cancel via μ-inversion
3. **GF(3) Conservation**: Trit sum ≡ 0 (mod 3) across skill triplets
4. **Spectral Gap**: Ramanujan bound λ₂ ≤ 2√(k-1) for k-regular expansion
```
Geodesic Invariant:
∀ path P: backtrack(P) = ∅ ⟹ μ(|P|) ≠ 0
Möbius Inversion:
f(n) = Σ_{d|n} g(d) ⟹ g(n) = Σ_{d|n} μ(n/d) f(d)
```
## SDF Interleaving
This skill connects to **Software Design for Flexibility** (Hanson & Sussman, 2021):
### Primary Chapter: 8. Degeneracy
**Concepts**: redundancy, fallback, multiple strategies, robustness
### GF(3) Balanced Triad
```
lasalle-invariance (−) + SDF.Ch8 (−) + [balancer] (−) = 0
```
**Skill Trit**: -1 (MINUS - verification)
### Secondary Chapters
- Ch3: Variations on an Arithmetic Theme
### Connection Pattern
Degeneracy provides fallbacks. This skill offers redundant strategies.
This skill implements the LaSalle invariance principle as a conceptual and practical tool for asymptotic stability analysis of dynamical systems. It frames long-term behavior in terms of invariant sets and limit sets to determine convergence without requiring strict Lyapunov decrease. The presentation emphasizes compositional use in analysis chains and integration with modular dynamical frameworks.
The skill inspects a system's phase space and a candidate Lyapunov-like function to identify forward-invariant sets where the function's derivative vanishes. It locates the largest invariant subset inside that set and uses it to conclude asymptotic stability or characterize limit behavior. The routines support local equilibrium checks, global limit-set reasoning, and compositional chaining with other analysis modules.
Does LaSalle require a strict Lyapunov function?
No. LaSalle works with Lyapunov-like functions whose derivative is negative semidefinite; you then inspect the largest invariant set where the derivative is zero.
Can this be used for global conclusions?
Yes, if the candidate function is proper (radially unbounded) and the invariance conditions hold globally, you can conclude global asymptotic stability.
How does this interact with compositional analyses?
The skill supports chaining: identify invariant sets per component, verify compatibility at interfaces, and then derive system-level limit behavior.