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Unformatted text preview: Operators and Matrices You’ve been using operators for years even if you’ve never heard the term. Differentiation falls into this category; so does rotation; so does wheel-alignment. In the subject of quantum mechanics, familiar ideas such as energy and momentum will be represented by operators. You probably think that pressure is simply a scalar, but no. It’s an operator. 7.1 The Idea of an Operator You can understand the subject of matrices as a set of rules that govern certain square or rectangular arrays of numbers — how to add them, how to multiply them. Approached this way the subject is remarkably opaque. Who made up these rules and why? What’s the point? If you look at it as simply a way to write simultaneous linear equations in a compact way, it’s perhaps convenient but certainly not the big deal that people make of it. It is a big deal. There’s a better way to understand the subject, one that relates the matrices to more fundamental ideas and that even provides some geometric insight into the subject. The technique of similarity transformations may even make a little sense. This approach is precisely parallel to one of the basic ideas in the use of vectors. You can draw pictures of vectors and manipulate the pictures of vectors and that’s the right way to look at certain problems. You quickly find however that this can be cumbersome. A general method that you use to make computations tractable is to write vectors in terms of their components, then the methods for manipulating the components follow a few straight-forward rules, adding the components, multiplying them by scalars, even doing dot and cross products. Just as you have components of vectors, which are a set of numbers that depend on your choice of basis, matrices are a set of numbers that are components of — not vectors, but functions (also called operators or transformations or tensors). I’ll start with a couple of examples before going into the precise definitions. The first example of the type of function that I’ll be interested in will be a function defined on the two-dimensional vector space, arrows drawn in the plane with their starting points at the origin. The function that I’ll use will rotate each vector by an angle α counterclockwise. This is a function, where the input is a vector and the output is a vector. f ( ~v ) α ~v f ( ~v 1 + ~v 2 ) α ~v 1 + ~v 2 What happens if you change the argument of this function, multiplying it by a scalar? You know f ( ~v ) , what is f ( c~v ) ? Just from the picture, this is c times the vector that you got by rotating ~v . What happens when you add two vectors and then rotate the result? The whole parallelogram defining the addition will rotate through the same angle α , so whether you apply the function before or after adding the vectors you get the same result....
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This note was uploaded on 01/30/2012 for the course PHYS 315 taught by Professor Nearing during the Fall '08 term at University of Miami.
- Fall '08