From e529e0e2775cf50207c7d01d5907214d03cdff5c Mon Sep 17 00:00:00 2001 From: Michael Orlitzky Date: Thu, 29 Aug 2019 22:48:51 -0400 Subject: [PATCH] eja: get a rudimentary spectral decomposition for operators working. --- mjo/eja/TODO | 9 --------- mjo/eja/eja_operator.py | 40 ++++++++++++++++++++++++++++++++++++++++ mjo/eja/eja_utils.py | 33 +++++++-------------------------- 3 files changed, 47 insertions(+), 35 deletions(-) diff --git a/mjo/eja/TODO b/mjo/eja/TODO index b2495b5..985e50a 100644 --- a/mjo/eja/TODO +++ b/mjo/eja/TODO @@ -8,12 +8,3 @@ 5. Factor out the unit-norm basis (and operator symmetry) tests once all of the algebras pass. - -6. Implement spectral projector decomposition for EJA operators - using jordan_form() or eigenmatrix_right(). I suppose we can - ignore the problem of base rings for now and just let it crash - if we're not using AA as our base field. - -7. Do we really need to orthonormalize the basis in a subalgebra? - So long as we can decompose the operator (which is invariant - under changes of basis), who cares? diff --git a/mjo/eja/eja_operator.py b/mjo/eja/eja_operator.py index 0e898b5..c32ff1e 100644 --- a/mjo/eja/eja_operator.py +++ b/mjo/eja/eja_operator.py @@ -426,3 +426,43 @@ class FiniteDimensionalEuclideanJordanAlgebraOperator(Map): """ # The matrix method returns a polynomial in 'x' but want one in 't'. return self.matrix().minimal_polynomial().change_variable_name('t') + + + def spectral_decomposition(self): + """ + Return the spectral decomposition of this operator as a list of + (eigenvalue, orthogonal projector) pairs. + + SETUP:: + + sage: from mjo.eja.eja_algebra import RealSymmetricEJA + + EXAMPLES:: + + sage: J = RealSymmetricEJA(4,AA) + sage: x = sum(J.gens()) + sage: A = x.subalgebra_generated_by(orthonormalize_basis=True) + sage: L0x = A(x).operator() + sage: Ps = [ P*l for (l,P) in L0x.spectral_decomposition() ] + sage: Ps[0] + Ps[1] == L0x + True + + """ + if not self.matrix().is_symmetric(): + raise ValueError('algebra basis is not orthonormal') + + D,P = self.matrix().jordan_form(subdivide=False,transformation=True) + eigenvalues = D.diagonal() + us = P.columns() + projectors = [] + for i in range(len(us)): + # they won't be normalized, but they have to be + # for the spectral theorem to work. + us[i] = us[i]/us[i].norm() + mat = us[i].column()*us[i].row() + Pi = FiniteDimensionalEuclideanJordanAlgebraOperator( + self.domain(), + self.codomain(), + mat) + projectors.append(Pi) + return zip(eigenvalues, projectors) diff --git a/mjo/eja/eja_utils.py b/mjo/eja/eja_utils.py index d486b4c..cf75e32 100644 --- a/mjo/eja/eja_utils.py +++ b/mjo/eja/eja_utils.py @@ -26,11 +26,11 @@ def gram_schmidt(v): sage: u = gram_schmidt(v) sage: all( u_i.inner_product(u_i).sqrt() == 1 for u_i in u ) True - sage: u[0].inner_product(u[1]) == 0 + sage: bool(u[0].inner_product(u[1]) == 0) True - sage: u[0].inner_product(u[2]) == 0 + sage: bool(u[0].inner_product(u[2]) == 0) True - sage: u[1].inner_product(u[2]) == 0 + sage: bool(u[1].inner_product(u[2]) == 0) True TESTS: @@ -67,29 +67,10 @@ def gram_schmidt(v): # And now drop all zero vectors again if they were "orthogonalized out." v = [ v_i for v_i in v if not v_i.is_zero() ] - # Now pretend to normalize, building a new ring R that contains - # all of the necessary square roots. - norms_squared = [0]*len(v) - - for i in xrange(len(v)): - norms_squared[i] = v[i].inner_product(v[i]) - ns = [norms_squared[i].numerator(), norms_squared[i].denominator()] - - # Do the numerator and denominator separately so that we - # adjoin e.g. sqrt(2) and sqrt(3) instead of sqrt(2/3). - for j in xrange(len(ns)): - PR = PolynomialRing(R, 'z') - z = PR.gen() - p = z**2 - ns[j] - if p.is_irreducible(): - R = NumberField(p, - 'sqrt' + str(ns[j]), - embedding=RLF(ns[j]).sqrt()) - - # When we're done, we have to change every element's ring to the - # extension that we wound up with, and then normalize it (which - # should work, since "R" contains its norm now). + # Just normalize. If the algebra is missing the roots, we can't add + # them here because then our subalgebra would have a bigger field + # than the superalgebra. for i in xrange(len(v)): - v[i] = v[i].change_ring(R) / R(norms_squared[i]).sqrt() + v[i] = v[i] / v[i].norm() return v -- 2.43.2