mscroggs.co.uk
mscroggs.co.uk

subscribe

Blog

Inverting a matrix

 2020-02-06 
This is the third post in a series of posts about matrix methods.
Yet again, we want to solve \(\mathbf{A}\mathbf{x}=\mathbf{b}\), where \(\mathbf{A}\) is a (known) matrix, \(\mathbf{b}\) is a (known) vector, and \(\mathbf{x}\) is an unknown vector.
In the previous post in this series, we used Gaussian elimination to invert a matrix. You may, however, have been taught an alternative method for calculating the inverse of a matrix. This method has four steps:
  1. Find the determinants of smaller blocks of the matrix to find the "matrix of minors".
  2. Multiply some of the entries by -1 to get the "matrix of cofactors".
  3. Transpose the matrix.
  4. Divide by the determinant of the matrix you started with.

An example

As an example, we will find the inverse of the following matrix.
$$\begin{pmatrix} 1&-2&4\\ -2&3&-2\\ -2&2&2 \end{pmatrix}.$$
The result of the four steps above is the calculation
$$\frac1{\det\begin{pmatrix} 1&-2&4\\ -2&3&-2\\ -2&2&2 \end{pmatrix} }\begin{pmatrix} \det\begin{pmatrix}3&-2\\2&2\end{pmatrix}& -\det\begin{pmatrix}-2&4\\2&2\end{pmatrix}& \det\begin{pmatrix}-2&4\\3&-2\end{pmatrix}\\ -\det\begin{pmatrix}-2&-2\\-2&2\end{pmatrix}& \det\begin{pmatrix}1&4\\-2&2\end{pmatrix}& -\det\begin{pmatrix}1&4\\-2&-2\end{pmatrix}\\ \det\begin{pmatrix}-2&3\\-2&2\end{pmatrix}& -\det\begin{pmatrix}1&-2\\-2&2\end{pmatrix}& \det\begin{pmatrix}1&-2\\-2&3\end{pmatrix} \end{pmatrix}.$$
Calculating the determinants gives $$\frac12 \begin{pmatrix} 10&12&-8\\ 8&10&-6\\ 2&2&-1 \end{pmatrix},$$ which simplifies to
$$ \begin{pmatrix} 5&6&-4\\ 4&5&-3\\ 1&1&-\tfrac12 \end{pmatrix}.$$

How many operations

This method can be used to find the inverse of a matrix of any size. Using this method on an \(n\times n\) matrix will require:
  1. Finding the determinant of \(n^2\) different \((n-1)\times(n-1)\) matrices.
  2. Multiplying \(\left\lfloor\tfrac{n}2\right\rfloor\) of these matrices by -1.
  3. Calculating the determinant of a \(n\times n\) matrix.
  4. Dividing \(n^2\) numbers by this determinant.
If \(d_n\) is the number of operations needed to find the determinant of an \(n\times n\) matrix, the total number of operations for this method is
$$n^2d_{n-1} + \left\lfloor\tfrac{n}2\right\rfloor + d_n + n^2.$$

How many operations to find a determinant

If you work through the usual method of calculating the determinant by calculating determinants of smaller blocks the combining them, you can work out that the number of operations needed to calculate a determinant in this way is \(\mathcal{O}(n!)\). For large values of \(n\), this is significantly larger than any power of \(n\).
There are other methods of calculating determinants: the fastest of these is \(\mathcal{O}(n^{2.373})\). For large \(n\), this is significantly smaller than \(\mathcal{O}(n!)\).

How many operations

Even if the quick \(\mathcal{O}(n^{2.373})\) method for calculating determinants is used, the number of operations required to invert a matrix will be of the order of
$$n^2(n-1)^{2.373} + \left\lfloor\tfrac{n}2\right\rfloor + n^{2.373} + n^2.$$
This is \(\mathcal{O}(n^{4.373})\), and so for large matrices this will be slower than Gaussian elimination, which was \(\mathcal{O}(n^3)\).
In fact, this method could only be faster than Gaussian elimination if you discovered a method of finding a determinant faster than \(\mathcal{O}(n)\). This seems highly unlikely to be possible, as an \(n\times n\) matrix has \(n^2\) entries and we should expect to operate on each of these at least once.
So, for large matrices, Gaussian elimination looks like it will always be faster, so you can safely forget this four-step method.
Previous post in series
This is the third post in a series of posts about matrix methods.
×3      ×3      ×3      ×2      ×2
(Click on one of these icons to react to this blog post)

You might also enjoy...

Comments

Comments in green were written by me. Comments in blue were not written by me.
 Add a Comment 


I will only use your email address to reply to your comment (if a reply is needed).

Allowed HTML tags: <br> <a> <small> <b> <i> <s> <sup> <sub> <u> <spoiler> <ul> <ol> <li> <logo>
To prove you are not a spam bot, please type "linear" in the box below (case sensitive):

Archive

Show me a random blog post
 2024 

Feb 2024

Zines, pt. 2

Jan 2024

Christmas (2023) is over
 2023 
▼ show ▼
 2022 
▼ show ▼
 2021 
▼ show ▼
 2020 
▼ show ▼
 2019 
▼ show ▼
 2018 
▼ show ▼
 2017 
▼ show ▼
 2016 
▼ show ▼
 2015 
▼ show ▼
 2014 
▼ show ▼
 2013 
▼ show ▼
 2012 
▼ show ▼

Tags

frobel braiding graphs the aperiodical wool crossnumber royal baby pi european cup matrix of minors sobolev spaces logo map projections runge's phenomenon logic ternary a gamut of games live stream news manchester coins bubble bobble pascal's triangle guest posts dragon curves numerical analysis talking maths in public weather station gerry anderson speed london matrices matt parker matrix multiplication realhats christmas data reddit interpolation pizza cutting approximation anscombe's quartet determinants stickers numbers big internet math-off probability radio 4 nine men's morris estimation squares errors triangles chebyshev football newcastle polynomials zines chess christmas card books convergence graph theory hats propositional calculus cross stitch data visualisation royal institution mathslogicbot electromagnetic field tennis dataset reuleaux polygons asteroids stirling numbers fence posts martin gardner geometry weak imposition logs raspberry pi sorting rugby geogebra ucl wave scattering oeis trigonometry harriss spiral folding paper dinosaurs simultaneous equations recursion golden spiral arithmetic gather town tmip game show probability php preconditioning pythagoras manchester science festival binary mathsteroids hannah fry final fantasy light rhombicuboctahedron hyperbolic surfaces countdown plastic ratio puzzles bempp exponential growth accuracy computational complexity people maths fonts hexapawn crochet platonic solids 24 hour maths programming standard deviation flexagons edinburgh databet pi approximation day quadrilaterals machine learning matrix of cofactors noughts and crosses curvature finite element method pac-man error bars go sound palindromes finite group datasaurus dozen turtles correlation signorini conditions golden ratio bodmas games fractals folding tube maps sport london underground mean menace inline code national lottery game of life latex craft phd cambridge statistics inverse matrices python video games boundary element methods draughts javascript world cup advent calendar gaussian elimination youtube mathsjam dates chalkdust magazine misleading statistics captain scarlet

Archive

Show me a random blog post
▼ show ▼
© Matthew Scroggs 2012–2024