We conclude our chapter on the Adjoint of a linear operator.
Our main theorems
THEOREM:
Let be an inner product space and be linear operators on . Then:
The proof of this theorem is based on the definition of the adjoint: .
COROLLARY
The above properties hold for matrices too (through the operator)
APPLICATION: Least Squares Approximation
The definition of the problem is classic: Given points , we are trying to minimize , with being the line of best-fit slope and is its offset. If
and
then we are basically trying to minimize . We will study a general method of solving this minimization problem when is an matrix. This allows for fitting of general polynomials of degree as well. We assume that .
If we let and , then it is easy to see that from the properties of the adjoint studied thus far. We also know that by the dimension theorem, because the nullities of the two matrices are the same (this is straightforward to see). Thus, if has rank , then is invertible.
Using these observations, our norm minimization problem can be solved through the minimality of orthogonal projections: Since , the range of is a subspace of . The projection of onto is a vector and it is such that for all . To find , let us note that lies in , so for all . In other words, so solves the equation . If additionally the rank of is , we get that:
APPLICATION: Minimal solutions to Systems of Linear Equations
How do we find minimal norm solutions to systems of linear equations? We prove the following theorem:
Theorem
Let and suppose that the system is consistent. Then:
- There exists exactly one minimal solution to and .
- The vector is the only solution to that lines in . So, if satisfies , then .
In other words, finding minimal norm solutions to systems like can be done by finding any solution to the system and letting .
Proof
The theorem follows relatively easily from the minimality of the projection onto a subspace (like before). We do, however, need that , a fact which holds for all linear transformations. This fact can be easily established, so we leave it as an exercise.