Interactive derivative geometry
Three arrays,
three jobs.
The Jacobian moves tiny vectors, the Wronskian measures the area spanned by solution states, and the Hessian records how slope itself changes.
The unifying ideaEach object packages derivatives into linear algebra so geometry can do part of the reasoning.
Jacobian
The best local linear map
For F: ℝⁿ → ℝᵐ, the entry in row i and column j is ∂Fᵢ/∂xⱼ. Thus DF(p)h predicts the output change produced by a small input displacement h.
F(p + h) = F(p) + DF(p)h + o(‖h‖)When the matrix is square, det DF gives signed local volume scale. A zero determinant means at least one infinitesimal direction has collapsed.
Wronskian
Area in state space
For two scalar functions, W = y₁y₂′ − y₂y₁′ is the signed area spanned by their state vectors (y, y′). Nonzero W at one point certifies independence.
Important caveat
Zero is not always a converse
For arbitrary differentiable functions, W ≡ 0 need not imply dependence. For solutions of the same regular linear ODE, Abel's identity restores the all-or-nothing conclusion.
Hessian
Directional curvature
For a scalar field f, Hᶠ contains every second partial derivative. In a unit direction u, the second directional derivative is uᵀHᶠu.
Classification
Read the eigenvalue signs
At a critical point: two positive eigenvalues mean a local minimum; two negative a maximum; mixed signs a saddle. A zero eigenvalue makes the quadratic test inconclusive.
Reliable calculation
Keep the indexing visible
- Jacobian: outputs label rows, inputs label columns.
- Wronskian: choose a consistent order for functions and derivative rows.
- Hessian: mixed partials should agree when f is C²; asymmetry is usually an algebra error.
- Always interpret a determinant with its domain, codomain, and units.