Exercises: conjugate gradient and matrices

Last updated on 2026-02-21 | Edit this page

Overview

Questions

  • How can I really push what I’ve learnt so far?

Objectives

  • Write a conjugate gradient solver.
  • Read a Matrix Market Exchange file and plot a downloaded matrix.

Exercises

To finish things off, here are two fairly substantial exercises you can tackle which should put everything you’ve learnt to the test.

Challenge

Conjugate gradient solver

Contrasting with the very specific tri-diagonal solver we’ve been working on, the conjugate gradient method provides a slightly more general method for the solution of linear systems

\(Ax = b\)

where the matrix \(A\) is symmetric positive definite. The algorithm is explained in detail on Wikipedia, and if you are very interested you might like to read An Introduction to the Conjugate Gradient Method Without the Agonizing Pain.

However, it is not necessary to understand the details, as here we are just interested in implementing the algorithm.

There are two main steps involved. The first is to perform a matrix-vector multiplication. This can be done using the Fortran intrinsic function matmul(). (Or you can have a go at implementing your own version - it’s not too difficult.)

The second step is to compute a scalar residual from a vector residual. If we have an array (vector) r(1:n) this can be done with:

FORTRAN 

  residual = sum(r(:)*r(:))

All the operations in the algorithm can be composed of these, as well as vector additions and multiplications by a scalar.

A template program cg_test.f90 is provided with a small matrix to use as a test. You need to implement a module cgradient which supplies a function cg_test() taking the arguments set out in the template.

Remember that you can always check your answer by multiplying out \(Ax\) to recover the original right-hand side \(b\).

You can check a suggested solution with cg_test.f90 and cgradient_solution.f90.

Challenge

Storage of large sparse matrices

The Matrix Market provides a number of archived matrices of different types. It also defines a simple ASCII format for the storage of sparse matrices.

The Matrix Market Exchange format .mtx files are structured as follows.

OUTPUT 

%% Exactly one header line starting %%
% Zero or more comment lines starting %
nrows ncols nnonzero
i1 j1 a(i1,j1)
i2 j2 a(i2,j2)
...

where nrows and ncols are the number of rows and columns in the matrix, respectively. nnonzero is the number of non-zero entries in the matrix. For each non-zero entry there then follows a single line which contains the row index, the column index, and the value of the matrix element itself.

Download an example, e.g.,

BASH 

$ wget https://math.nist.gov/pub/MatrixMarket2/Harwell-Boeing/laplace/gr_30_30.mtx.gz
$ gunzip gr_30_30.mtx

If you look at the first few line of this example, you should see

OUTPUT 

%%MatrixMarket matrix coordinate real symmetric
900 900 4322
1 1  8.0000000000000e+00
2 1 -1.0000000000000e+00
31 1 -1.0000000000000e+00
32 1 -1.0000000000000e+00
2 2  8.0000000000000e+00

  • Define a type that can hold the sparse representation and provide a procedure which initialises such a type from a file.

  • Provide a procedure which brings into existence a dense matrix (just a two-dimensional array) initialised with the correct non-zero elements.

  • Use the .pbm file generator to produce an image of the non-zero elements to provide a check the file has been read correctly.

  • Try some other matrices from the Matrix Market.

A suggested solution in provided in mmtest.f90 and mmarket.f90.

Key Points
  • Fortran performance and array handling make it ideal for the solution of intense problems.