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-rw-r--r--doc/source/user/quickstart.rst55
1 files changed, 2 insertions, 53 deletions
diff --git a/doc/source/user/quickstart.rst b/doc/source/user/quickstart.rst
index ab5bb5318..b50d8a5ba 100644
--- a/doc/source/user/quickstart.rst
+++ b/doc/source/user/quickstart.rst
@@ -20,8 +20,7 @@ in addition to NumPy.
**Learner profile**
-This is a quick overview of
-algebra and arrays in NumPy. It demonstrates how n-dimensional
+This is a quick overview of arrays in NumPy. It demonstrates how n-dimensional
(:math:`n>=2`) arrays are represented and can be manipulated. In particular, if
you don't know how to apply common functions to n-dimensional arrays (without
using for-loops), or if you want to understand axis and shape properties for
@@ -1391,57 +1390,6 @@ Indexing with strings
See :ref:`structured_arrays`.
-Linear Algebra
-==============
-
-Work in progress. Basic linear algebra to be included here.
-
-Simple Array Operations
------------------------
-
-See linalg.py in numpy folder for more.
-
-::
-
- >>> import numpy as np
- >>> a = np.array([[1.0, 2.0], [3.0, 4.0]])
- >>> print(a)
- [[1. 2.]
- [3. 4.]]
- >>> a.transpose()
- array([[1., 3.],
- [2., 4.]])
- >>> np.linalg.inv(a)
- array([[-2. , 1. ],
- [ 1.5, -0.5]])
- >>> u = np.eye(2) # unit 2x2 matrix; "eye" represents "I"
- >>> u
- array([[1., 0.],
- [0., 1.]])
- >>> j = np.array([[0.0, -1.0], [1.0, 0.0]])
- >>> j @ j # matrix product
- array([[-1., 0.],
- [ 0., -1.]])
- >>> np.trace(u) # trace
- 2.0
- >>> y = np.array([[5.], [7.]])
- >>> np.linalg.solve(a, y)
- array([[-3.],
- [ 4.]])
- >>> np.linalg.eig(j)
- (array([0.+1.j, 0.-1.j]), array([[0.70710678+0.j , 0.70710678-0.j ],
- [0. -0.70710678j, 0. +0.70710678j]]))
-
-::
-
- Parameters:
- square matrix
- Returns
- The eigenvalues, each repeated according to its multiplicity.
- The normalized (unit "length") eigenvectors, such that the
- column ``v[:, i]`` is the eigenvector corresponding to the
- eigenvalue ``w[i]`` .
-
Tricks and Tips
===============
@@ -1530,3 +1478,4 @@ Further reading
- `SciPy Tutorial <https://docs.scipy.org/doc/scipy/reference/tutorial/index.html>`__
- `SciPy Lecture Notes <https://scipy-lectures.org>`__
- A `matlab, R, IDL, NumPy/SciPy dictionary <http://mathesaurus.sf.net/>`__
+- :doc:`tutorial-svd`