Ported for Lean Pool: imports relocated and code adapted to Lean/Mathlib v4.34.0.
Determinant bounds for Hessian estimates #
Determinant bound for positive semidefinite matrices bounded above by a scalar multiple of the identity.
In quantified mathematical form, if A is a positive semidefinite real
n × n matrix, if 0 ≤ lambda, and if A ≤ lambda I in the Loewner order,
then det A ≤ lambda^n.
Determinant bound for Hermitian matrices whose eigenvalues lie between
-lambda and 0.
In quantified mathematical form, if A is Hermitian, 0 ≤ lambda,
-lambda I ≤ A, and A ≤ 0, then |det A| ≤ lambda^n.
The determinant bound in the ℝ≥0∞ form used by the Jacobian estimate.
In quantified mathematical form, if A is Hermitian, 0 ≤ lambda,
-lambda I ≤ A, and A ≤ 0, then
ofReal |det A| ≤ ofReal (max (lambda^n) 1).
The maximum with 1 gives a positive finite constant for the later division
step, while still dominating the determinant bound supplied by the preceding
theorem.
Pointwise determinant bound from Loewner-order Hessian bounds.
In quantified mathematical form, let S ⊆ R^n and let H x be a real
symmetric matrix for every x ∈ S. If 0 ≤ lambda and, for every x ∈ S,
-lambda I ≤ H x ≤ 0, then for every x ∈ S,
ofReal |det (H x)| ≤ ofReal (max (lambda^n) 1).