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lasalle-invariance skill

/skills/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-invariance

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SKILL.md
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---
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.

Overview

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.

How this skill works

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.

When to use it

  • Determining asymptotic convergence when a strict Lyapunov function is unavailable
  • Analyzing long-term behavior of nonlinear ODEs or flows on manifolds
  • Composing stability arguments across subsystems or modular components
  • Characterizing attractors and limit sets in control or biological models
  • Investigating bifurcation scenarios where invariance identifies new limit sets

Best practices

  • Provide a continuously differentiable candidate function; verify its derivative sign on trajectories
  • Explicitly compute or bound the set where the derivative is zero before seeking invariant subsets
  • Combine with local linearization near equilibria to strengthen conclusions about asymptotic stability
  • When composing analyses, ensure consistent domain assumptions and no hidden state revisits
  • Use degeneracy and redundancy patterns to supply fallback Lyapunov-like constructions

Example use cases

  • Proving asymptotic stability of an equilibrium when the Lyapunov derivative is negative semidefinite
  • Showing convergence of trajectories to an invariant manifold in a coupled oscillator network
  • Analyzing switched or hybrid systems by identifying invariant regions common to all modes
  • Verifying that a control law yields convergence to a desired set despite model uncertainties
  • Supporting bifurcation analysis by tracking how invariant limit sets change with parameters

FAQ

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.