## Performing a familiar task, just using the built-in packages of Maxima.

According to an earlier posting, I had suggested a recipe for ‘Perpendicularizing’ a matrix, that was to represent a Quadric Equation, according to methods which I learned in “Linear 1″. That approach used the application ‘wxMaxima’, which is actually a fancy front-end for the application ‘Maxima’. But the main drawback with the direct approach I had suggested, was that it depended on the package ‘lapack’, which I had written, takes a long time to compile.

Since writing that posting, I discovered that some users cannot even get ‘lapack’ to compile, making that a broken, unusable package for them. Yet, the desire could still exist, to carry out the same project. Therefore, I have now expounded on this, by using the package ‘eigen’, which is built in to Maxima, and which should work for more users, assuming there is no bug in the way Maxima was built.

The following work-sheet explains what initially goes wrong when using the package ‘eigen’, and, how to remedy the initial problem…

(Updated 6/17/2020, 14h35… )

## The Difference Between a Quartic, and a Quadric

I’ve talked to people who did not distinguish, between a Quartic, and a Quadric.

The following is a Quartic:

y = ax4 + bx3 + cx2 + dx + e

It follows in the sequence from a linear equation, through a quadratic, through a cubic, to arrive at the quartic. What follows it is called a “Quintic”.

a1 x2 + a2 y2 + a3 z2 +

a4 (xy) + a5 (yz) + a6 (az) +

a7 x + a8 y + a9 z – C = 0

The main reason quadrics are important, is the fact that they represent 3D shapes such as Hyperboloids, Ellipsoids, and Mathematically significant, but mundanely insignificant shapes, that radiate away from 1 axis out of 3, but that are symmetrical along the other 2 axes.

If the first-order terms of a quadric are zero, then the mixed terms merely represent rotations of these shapes, while, if the mixed terms are also zero, then these shapes are aligned with the 3 axes. Thus, if (C) was simply equal to (5), and if the signs of the 3 single, squared terms, by themselves, are:

+x2 +y2 +z2 = C : Ellipsoid .

+x2 -y2 -z2 = C : Hyperboloid .

+x2 +y2 – z2 = C : ‘That strange shape’ .

The way in which quadrics can be manipulated with Linear Algebra is of some curiosity, in that we can have a regular column vector (X), which represents a coordinate system, and we can state the transpose of the same vector, (XT), which forms the corresponding row-vector, for the same coordinate system. And in that case, the quadric can also be stated by the matrix product:

XT M X = C

(Updated 1/13/2019, 21h35 : )

## Self-Educating about Perpendicular Matrices with Complex Elements

One of the key reasons for which my class was taught Linear Algebra, including how to compute Eigenvalues and Eigenvectors of Matrices, was so that we could Diagonalize Symmetrical Matrices, in Real Numbers. What this did was to compute the ‘Perpendicular Matrix’ of a given matrix, in which each column was one of its Eigenvectors, and which was an example of an Orthogonal Matrix.  (It might be the case that what was once referred to as a Perpendicular Matrix, may now be referred to as the Orthogonal Basis of the given matrix,?)

(Edit 07/04/2018 :

In fact, what we were taught, is now referred to as The Eigendecomposition of a matrix. )

Having computed the perpendicular matrix P of M, it was known that the matrix product

PT M P = D,

which gives a Diagonal Matrix ‘D’. But, a key problem my Elementary Linear class was not taught to solve, was what to do if ‘M’ had complex Eigenvalues. In order to be taught that, we would need to have been taught in general, how to combine Linear Algebra with Complex Numbers. After that, the Eigenvectors could have been computed as easily as before, using Gauss-Jordan Elimination.

I have brushed up on this in my old Linear Algebra textbook, where the last chapter writes about Complex Numbers. Key facts which need to be understood about Complex Vector Spaces, is

• The Inner Product needs to be computed differently from before, in a way that borrows from the fact that complex numbers naturally have conjugates. It is now the sum, of each element of one vector, multiplied by the conjugate, of the corresponding element of the other vector.
• Orthogonal and Symmetrical Matrices are relatively unimportant with Complex Elements.
• A special operation is defined for matrices, called the Conjugate Transpose, A* .
• A Unitary Matrix now replaces the Orthogonal Matrix, such that A-1 = A* .
• A Hermitian Matrix now replaces the Symmetrical Matrix, such that A = A* , and the elements along the main diagonal are Real. Hermitian Matrices are also easy to recognize by inspection.
• Not only Hermitian Matrices can be diagonalized. They have a superset, known as Normal Matrices, such that A A* = A* A . Normal Matrices can be diagonalized.

This could all become important in Quantum Mechanics, considering the general issue known to exist, by which the bases that define how particles can interact, somehow need to be multiplied by complex numbers, to describe accurately, how particles do interact.