Add learning outcomes to Sparse Linear Algebra (#163)

* add learning outcomes to 01-sparse-matrices.md * add learning outcomes to 02-coo-matrix.md * add learning outcomes to 03-finite-difference.md * add learning outcomes to 04-scipy-sparse.md * add learning outcomes to 06-jacobi-relaxation-methods.md * add learning outcomes to 07-conjugate-gradient-method.md * format sentences in learning outcomes * Apply suggestions from code review Co-authored-by: Martin Robinson <[email protected]> --------- Co-authored-by: Martin Robinson <[email protected]>
UNIVERSE-HPC · Nov 22, 2024 · 020260c · 020260c
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diff --git a/scientific_computing/sparse_linear_algebra/01-sparse-matrices.md b/scientific_computing/sparse_linear_algebra/01-sparse-matrices.md
@@ -2,6 +2,9 @@
 name: Sparse Matrices
 dependsOn: []
 tags: []
+learningOutcomes:
+  - Sparse matrices and the benefits of using them in scientific computing.
+  - Some typical applications of sparse matrices.
 attribution:
   - citation: This material has been adapted from material by Martin Robinson from the "Scientific Computing" module of the SABS R³ Center for Doctoral Training.
     url: https://www.sabsr3.ox.ac.uk

diff --git a/scientific_computing/sparse_linear_algebra/02-coo-matrix.md b/scientific_computing/sparse_linear_algebra/02-coo-matrix.md
@@ -2,6 +2,9 @@
 name: COOrdinate format
 dependsOn: ["scientific_computing.sparse_linear_algebra.01-sparse-matrices"]
 tags: []
+learningOutcomes:
+  - Understand the COOrdinate (COO) format and the advantages of using it. 
+  - Be able to create a C00 matrix.
 attribution:
   - citation: This material has been adapted from material by Martin Robinson from the "Scientific Computing" module of the SABS R³ Center for Doctoral Training.
     url: https://www.sabsr3.ox.ac.uk

diff --git a/scientific_computing/sparse_linear_algebra/03-finite-difference.md b/scientific_computing/sparse_linear_algebra/03-finite-difference.md
@@ -2,6 +2,8 @@
 name: Finite Difference Matrix
 dependsOn: ["scientific_computing.sparse_linear_algebra.02-coo-matrix"]
 tags: []
+learningOutcomes:
+  -  Understand how to construct a sparse matrix that is derived from the finite difference discretisation of the Poisson equation.
 attribution:
   - citation: This material has been adapted from material by Martin Robinson from the "Scientific Computing" module of the SABS R³ Center for Doctoral Training.
     url: https://www.sabsr3.ox.ac.uk
@@ -16,7 +18,7 @@ attribution:
 Many matrices in scientific computing contain mostly zeros, particularly those arising
 from the discretisation of partial differential equations (PDEs). Here we will construct
 a sparse matrix using `scipy.sparse` that is derived from the finite difference
-discretistaion of the Poisson equation. In 1D, Poisson equation is
+discretisation of the Poisson equation. In 1D, Poisson equation is
 
 $$
 u_{xx} = f(x)\text{ for }0 \le x \le 1

diff --git a/scientific_computing/sparse_linear_algebra/04-scipy-sparse.md b/scientific_computing/sparse_linear_algebra/04-scipy-sparse.md
@@ -2,6 +2,9 @@
 name: Scipy.sparse and problems
 dependsOn: ["scientific_computing.sparse_linear_algebra.03-finite-difference"]
 tags: []
+learningOutcomes:
+  - Know what sparse matrix types and direct solvers are available in `scipy.sparse`.
+  - Be able to construct a sparse finite difference matrix and solve this using `scipy.sparse`.
 attribution:
   - citation: This material has been adapted from material by Martin Robinson from the "Scientific Computing" module of the SABS R³ Center for Doctoral Training.
     url: https://www.sabsr3.ox.ac.uk

diff --git a/scientific_computing/sparse_linear_algebra/06-jacobi-relaxation-methods.md b/scientific_computing/sparse_linear_algebra/06-jacobi-relaxation-methods.md
@@ -2,6 +2,9 @@
 name: Jacobi and Relaxation Methods
 dependsOn: ["scientific_computing.sparse_linear_algebra.04-scipy-sparse"]
 tags: []
+learningOutcomes:
+  - Be able to use iterative methods to produce a series of approximate solutions.
+  - Understand the basics of relaxation methods e.g. the Jacobi method.
 attribution:
   - citation: This material has been adapted from material by Martin Robinson from the "Scientific Computing" module of the SABS R³ Center for Doctoral Training.
     url: https://www.sabsr3.ox.ac.uk

diff --git a/scientific_computing/sparse_linear_algebra/07-conjugate-gradient-method.md b/scientific_computing/sparse_linear_algebra/07-conjugate-gradient-method.md
@@ -2,6 +2,10 @@
 name: Krylov subspace methods and CG
 dependsOn: ["scientific_computing.sparse_linear_algebra.06-jacobi-relaxation-methods"]
 tags: []
+learningOutcomes:
+  - Understand what are Krylov subspace methods, in particular the Conjugate Gradient Method
+  - Be able to use the CG, BICGSTAB, GMRES iterative solvers in `scipy`
+  - Awareness of the performance characteristics of these iterative solvers, compared with direct solvers such as LU or Cholesky
 attribution:
   - citation: This material has been adapted from material by Martin Robinson from the "Scientific Computing" module of the SABS R³ Center for Doctoral Training.
     url: https://www.sabsr3.ox.ac.uk