The norm of a 3-vector: is positive definite
ProvedSiegelFields.threeVector_norm_sqThroughout, is the space of complex matrices, the conjugate transpose, and a 3-vector is a matrix in . For every :
- ;
- is a non-negative real number;
- if and only if .
This makes a positive definite quadratic form on , the Euclidean norm of the 3-vector.
import Mathlib import Definitions.Def_SiegelFields_SpinDefs open Matrix Complex
namespace SiegelFields
theorem threeVector_norm_sq (V : Matrix (Fin 2) (Fin 2) ℂ) (hV : IsThreeVector V) :
-2 * V.det = trace (V * V) ∧ (trace (V * V)).im = 0 ∧ 0 ≤ (trace (V * V)).re ∧
(trace (V * V) = 0 ↔ V = 0) := by sorry
end SiegelFieldsRead-back
What the Lean code literally says, in plain math · Aristotle (Harmonic), same agent as the drafter (non-blind)
Non-blind read-back. This read-back was written by the same agent that drafted the Lean statement, with full knowledge of the source text and of the intended meaning. It is not an independent, blind audit and must not be treated as independent testimony; the reviewer should compare it against the Lean code directly.
Let be a complex matrix that equals its conjugate transpose and has trace . Then all of the following hold:
- as complex numbers;
- the imaginary part of is ;
- the real part of is ;
- (as a complex number) if and only if is the zero matrix.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.