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Linear Algebra

MATH Such implementations are stringently tested before being used with the NAG Library, to ensure that they correctly meet the specifications of the BLAS, and that they return the desired accuracy. Because of the overlap of functionality with , only a subset of functions defined by the Technical Forum are implemented in this chapter.

The functions in this chapter make available only some of the Basic Linear Algebra Subprograms which carry out the low level operations required by linear algebra applications.

Vector intro for linear algebra - Vectors and spaces - Linear Algebra - Khan Academy

As with vectors, you can use the dot function to perform multiplication with Numpy:. Matrix multiplication was a hard concept for me to grasp on too, but what really helped is doing it on paper by hand.

Introduction

There are tons of examples online. And now something simple, to rest your brain for a minute. But just for a minute. The idea is really simple — you only need to exchange rows and columns of the matrix. Transpose operator is in most cases denoted with capital letter T , and notation can be put either before the matrix or as an exponent.

As you can see the diagonal elements stayed the same, and those off-diagonal switched their position.

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Just as with transpose, Identity matrices are also really simple to grasp on. It is a matrix where:.

Now back to harder stuff kind of. The determinant is a scalar value that can be computed from the elements of a square matrix and encodes certain properties of the linear transformation described by the matrix. The determinant of a matrix A is denoted det A , det A , or A. Geometrically, it can be viewed as the volume scaling factor of the linear transformation described by the matrix. To develop a more intuitive sense of what the determinant is, and what it is used for, please refer to the video playlist linked down in the article conclusion section. I mean you can if you want to, but why?

Discovery module overview

The goal here is to develop the intuition , computers were made to do the calculations. You are doing great. The last section follows, and then you are done! A square matrix is called invertible or nonsingular if multiplication of the original matrix by its inverse results in the identity matrix.


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From that statement, you can conclude that not all matrices have inverses. For a matrix to be invertible, it has to satisfy the following conditions:. Logically, for square matrix to be singular, its determinant must be equal to 0. As you can see the matrix inverse is denoted by this -1 term in the superscript.

Further Linear Algebra - T.S. Blyth, E F. Robertson - Google книги

You can see here why the determinant cannot be 0 — division by 0 is undefined. This term is then multiplied with the slightly rearranged version of the original matrix. The diagonal items are switched, and off-diagonal elements are multiplied by negative one