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/-
Copyright (c) 2020 Scott Morrison. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Scott Morrison, Eric Wieser
-/
import data.matrix.basis
import ring_theory.tensor_product
/-!
We show `matrix n n A ββ[R] (A β[R] matrix n n R)`.
-/
universes u v w
open_locale tensor_product
open_locale big_operators
open tensor_product
open algebra.tensor_product
open matrix
variables {R : Type u} [comm_semiring R]
variables {A : Type v} [semiring A] [algebra R A]
variables {n : Type w}
variables (R A n)
namespace matrix_equiv_tensor
/--
(Implementation detail).
The function underlying `(A β[R] matrix n n R) ββ[R] matrix n n A`,
as an `R`-bilinear map.
-/
def to_fun_bilinear : A ββ[R] matrix n n R ββ[R] matrix n n A :=
(algebra.lsmul R (matrix n n A)).to_linear_map.complβ (algebra.linear_map R A).map_matrix
@[simp] lemma to_fun_bilinear_apply (a : A) (m : matrix n n R) :
to_fun_bilinear R A n a m = a β’ m.map (algebra_map R A) := rfl
/--
(Implementation detail).
The function underlying `(A β[R] matrix n n R) ββ[R] matrix n n A`,
as an `R`-linear map.
-/
def to_fun_linear : A β[R] matrix n n R ββ[R] matrix n n A :=
tensor_product.lift (to_fun_bilinear R A n)
variables [decidable_eq n] [fintype n]
/--
The function `(A β[R] matrix n n R) ββ[R] matrix n n A`, as an algebra homomorphism.
-/
def to_fun_alg_hom : (A β[R] matrix n n R) ββ[R] matrix n n A :=
alg_hom_of_linear_map_tensor_product
(to_fun_linear R A n)
begin
intros,
simp_rw [to_fun_linear, lift.tmul, to_fun_bilinear_apply, mul_eq_mul, matrix.map_mul],
ext,
dsimp,
simp_rw [matrix.mul_apply, pi.smul_apply, matrix.map_apply, smul_eq_mul, finset.mul_sum,
_root_.mul_assoc, algebra.left_comm],
end
begin
intros,
simp_rw [to_fun_linear, lift.tmul, to_fun_bilinear_apply,
matrix.map_one (algebra_map R A) (map_zero _) (map_one _), algebra_map_smul,
algebra.algebra_map_eq_smul_one],
end
@[simp] lemma to_fun_alg_hom_apply (a : A) (m : matrix n n R) :
to_fun_alg_hom R A n (a ββ m) = a β’ m.map (algebra_map R A) :=
by simp [to_fun_alg_hom, alg_hom_of_linear_map_tensor_product, to_fun_linear]
/--
(Implementation detail.)
The bare function `matrix n n A β A β[R] matrix n n R`.
(We don't need to show that it's an algebra map, thankfully --- just that it's an inverse.)
-/
def inv_fun (M : matrix n n A) : A β[R] matrix n n R :=
β (p : n Γ n), M p.1 p.2 ββ (std_basis_matrix p.1 p.2 1)
@[simp] lemma inv_fun_zero : inv_fun R A n 0 = 0 :=
by simp [inv_fun]
@[simp] lemma inv_fun_add (M N : matrix n n A) :
inv_fun R A n (M + N) = inv_fun R A n M + inv_fun R A n N :=
by simp [inv_fun, add_tmul, finset.sum_add_distrib]
@[simp] lemma inv_fun_smul (a : A) (M : matrix n n A) :
inv_fun R A n (a β’ M) = (a ββ 1) * inv_fun R A n M :=
by simp [inv_fun,finset.mul_sum]
@[simp] lemma inv_fun_algebra_map (M : matrix n n R) :
inv_fun R A n (M.map (algebra_map R A)) = 1 ββ M :=
begin
dsimp [inv_fun],
simp only [algebra.algebra_map_eq_smul_one, smul_tmul, βtmul_sum, mul_boole],
congr,
conv_rhs {rw matrix_eq_sum_std_basis M},
convert finset.sum_product, simp,
end
lemma right_inv (M : matrix n n A) : (to_fun_alg_hom R A n) (inv_fun R A n M) = M :=
begin
simp only [inv_fun, alg_hom.map_sum, std_basis_matrix, apply_ite β(algebra_map R A), smul_eq_mul,
mul_boole, to_fun_alg_hom_apply, ring_hom.map_zero, ring_hom.map_one, matrix.map_apply,
pi.smul_def],
convert finset.sum_product, apply matrix_eq_sum_std_basis,
end
lemma left_inv (M : A β[R] matrix n n R) : inv_fun R A n (to_fun_alg_hom R A n M) = M :=
begin
induction M using tensor_product.induction_on with a m x y hx hy,
{ simp, },
{ simp, },
{ simp [alg_hom.map_sum, hx, hy], },
end
/--
(Implementation detail)
The equivalence, ignoring the algebra structure, `(A β[R] matrix n n R) β matrix n n A`.
-/
def equiv : (A β[R] matrix n n R) β matrix n n A :=
{ to_fun := to_fun_alg_hom R A n,
inv_fun := inv_fun R A n,
left_inv := left_inv R A n,
right_inv := right_inv R A n, }
end matrix_equiv_tensor
variables [fintype n] [decidable_eq n]
/--
The `R`-algebra isomorphism `matrix n n A ββ[R] (A β[R] matrix n n R)`.
-/
def matrix_equiv_tensor : matrix n n A ββ[R] (A β[R] matrix n n R) :=
alg_equiv.symm { ..(matrix_equiv_tensor.to_fun_alg_hom R A n), ..(matrix_equiv_tensor.equiv R A n) }
open matrix_equiv_tensor
@[simp] lemma matrix_equiv_tensor_apply (M : matrix n n A) :
matrix_equiv_tensor R A n M =
β (p : n Γ n), M p.1 p.2 ββ (std_basis_matrix p.1 p.2 1) :=
rfl
@[simp] lemma matrix_equiv_tensor_apply_std_basis (i j : n) (x : A):
matrix_equiv_tensor R A n (std_basis_matrix i j x) =
x ββ (std_basis_matrix i j 1) :=
begin
have t : β (p : n Γ n), (i = p.1 β§ j = p.2) β (p = (i, j)) := by tidy,
simp [ite_tmul, t, std_basis_matrix],
end
@[simp] lemma matrix_equiv_tensor_apply_symm (a : A) (M : matrix n n R) :
(matrix_equiv_tensor R A n).symm (a ββ M) =
M.map (Ξ» x, a * algebra_map R A x) :=
begin
simp [matrix_equiv_tensor, to_fun_alg_hom, alg_hom_of_linear_map_tensor_product, to_fun_linear],
refl,
end
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