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The Moore-Penrose inverse
Recall that the Moore-Penrose inverse of a complex matrix (or more generally of a linear operator ) is the complex matrix (resp. the linear operator) satisfying where denotes the Hermitian adjoint of a complex matrix (resp. a linear operator) .
Handbook of Linear Algebra
Uniqueness of Moore-Penrose Inverse (Fact 1 in [Hog13, Sec. I.5.7])
If and both satisfy the Moore-Penrose identities for , then .
Required property: Enconding the Hermitian transpose
Strategy: Add for every assumed identitiy also the corresponding adjoint identity and simplify all operator expressions using the following identities before translating them into polynomials.
Existence of Moore-Penrose Inverse (Fact 1 in [Hog13, Sec. I.5.7])
Every complex matrix has a Moore-Penrose Inverse.
Required property: Proving an existential statements
Strategy: Construct explicit expression for the existentially quantified variable.
To do this, the package provides dedicated methods, such as the command find_equivalent_expression.
Moore-Penrose inverse of real matrix is real (Fact 2 in [Hog13, Sec. I.5.7])
If is a real matrix, then is real as well.
Required property: Enconding real matrices
Strategy: Decompose Hermitian adjoint into complex conjugation and transposition, i.e., , and express being real as .
In terms of polynomials, this means introducing new variables and and adding the polynomial .
Additionally, for every assumption , the corresponding adjoint identity , the transposed identity , and the conjugated identitiy have to be translated into polynomials as well. These additional identities first have to be simplified using the following rules that relate the different function symbols to each other with such that and .
Full Rank Decomposition (Fact 3 in [Hog13, Sec. I.5.7])
If is a full rank decomposition, i.e., has full column rank and has full row rank, then .
Required property: Enconding full rank of matrices
Strategy: Use the fact that full row (resp. column) rank correspond to the existence of a right (resp. left) inverse.
Thus, having full row rank can be encoded via the identity , where is a new symbol that does not satisfy any additional hypotheses and is the identity matrix. Analogously, full column rank of corresponds to the identity .
To encode the identity matrices, a new indeterminate has to be introduced for every identity matrix and the identities satisfied by have to be added explicitly to the assumptions. In particular, these are the idempotency of , the fact that is self-adjoint, and the identities and for all basic operators for which these expressions are well-defined.
Remark: More generally, using one-sided inverses also injectivity and surjectivity of operators can be encoded.
Remark: Note that is invertible because and are.
Moore-Penrose Inverse via Singular Value Decomposition (Fact 4,17 in [Hog13, Sec. I.5.7])
If with unitary, then
Moore-Penrose inverse is Involution (Fact 10 in [Hog13, Sec. I.5.7])
Moore-Penrose Inverse of Adjoint (Fact 10 in [Hog13, Sec. I.5.7])
Moore-Penrose inverse of non-singular square matrix (Fact 11 in [Hog13, Sec. I.5.7])
If is a non-singular square matrix, then .
Orthonormal columns or rows (Fact 12 in [Hog13, Sec. I.5.7])
If has orthonormal columns or orthonormal rows, then
Self-Inverse (Fact 13 in [Hog13, Sec. I.5.7])
If and , then .
Characterization of Inverse is Adjoint (Fact 14 in [Hog13, Sec. I.5.7])
if and only if is idempotent.
Normal matrices commute with Moore-Penrose inverse (Fact 15 in [Hog13, Sec. I.5.7])
If is normal, i.e., , then .
Sufficient condition for orthonormal columns (Fact 16 in [Hog13, Sec. I.5.7])
If has full column rank and satisfies , then has orthonormal columns.
Moore-Penrose Inverse in terms of Gram Matrix (Fact 18 in [Hog13, Sec. I.5.7])
It holds that .
Orthogonal projections (Fact 19a in [Hog13, Sec. I.5.7])
, , , are orthogonal projections.
Projections onto ranges (Fact 20 in [Hog13, Sec. I.5.7])
; .
Properties of Ranges and Kernels (Fact 21a-d in [Hog13, Sec. I.5.7])
(a) .
(b) .
(c) .
(d) .
Reverse Order Law for Full Rank Decomposition (Fact 23 in [Hog13, Sec. I.5.7])
If is a full rank decomposition, i.e., has full column rank and has full row rank, then .
Moore-Penrose Inverse of Gram Matrix (Fact 24 in [Hog13, Sec. I.5.7])
It holds that .
Reverse Order Law for Moore-Penrose Inverse (Fact 25 in [Hog13, Sec. I.5.7])
Each one of the following conditions is necessary and sufficient for :
and .
and are both Hermitian matrices.
and .
.
and .
We show .
To prove the implication , we first show the following lemma:
Lemma: If has a Moore-Penrose inverse, then .
Since every matrix has a Moore-Penrose inverse, we can apply the lemma above to any expression. In particular, instead of proving the identities appearing in condition 3, we can also show that , from which we can conclude , or equivalently .
Reverse Order Law ([DD10, Thm. 2.2 – 2.4])
The following statement combines the main results of Theorem 2.2 – 2.4 in [DD10].
Let be Hilbert spaces, and let and be bounded linear operators such that , , have closed ranges. Then the following statements are equivalent:
and
and
and
and
and
and
and
In the following, we show
.
Remark: To prove the implication , we note that, for all bounded linear operators , we have
,
which follows from .
Thus, instead of proving the identities appearing in condition 3, we can also show that , or from which we can conclude , or equivalently .
Triple Reverse Order Law
Hartwig's original statement ([CIHHP21, Thm. 2.1])
Let be complex matrices such that the product is defined and let , . The following conditions are equivalent:
;
, , and both and are Hermitian;
, , and both and are EP;
, and ;
, and ;
In the following, we show and .
Generalisation 1 ([CIHHP21, Thm. 2.3])
Let be a ring with involution . Let be such that are MP-invertible. Let and , for . Then the following conditions are equivalent:
is Moore-Penrose invertible and ;
, and ;
is right -cancellable, , and ;
, , and ;
In the following, we show .
Generalisation 2 ([CIHHP21, Thm. 2.4])
Let be a ring with involution . Let be such that are MP-invertible. Let and , for . Then the following conditions are equivalent:
is Moore-Penrose invertible and ;
, and ;
is left -cancellable, , and ;
, , and ;
In the following, we show .
References
[Hog13] Hogben L., Handbook of Linear Algebra. CRC press, 2 edn. (2013)
[DD10] Djordjević, D.S., Dinčić, N.Č.: Reverse order law for the Moore-Penrose inverse. J. Math. Anal. Appl. 361(1), 252–261 (2010)
[CIHHP 21] Cvetković-Ilić, D. S., Hofstadler, C., Hossein Poor, J., Milošević, J., Raab, C. G., Regensburger, G.: Algebraic proof methods for identities of matrices and operators: improvements of Hartwig’s triple reverse order law. Appl. Math. Comput. 409, 126357 (2021)