diff --git a/docs/source/tutorials/linalg/linear_solvers_quickstart.ipynb b/docs/source/tutorials/linalg/linear_solvers_quickstart.ipynb index 533cd4840..5247165c7 100644 --- a/docs/source/tutorials/linalg/linear_solvers_quickstart.ipynb +++ b/docs/source/tutorials/linalg/linear_solvers_quickstart.ipynb @@ -52,6 +52,7 @@ "$$\n", "A \\mathbf{x} = \\mathbf{b}\n", "$$\n", + "\n", "where $A\\in\\mathbb{R}^{n\\times n}$ is a symmetric positive definite matrix, $\\mathbf{b}\\in\\mathbb{R}^n$ is a vector and $\\mathbf{x}\\in\\mathbb{R}^n$ is the unknown solution of the linear system. \n", "\n", "Solving such a linear system is arguably one of the most fundamental computations in statistics, machine learning and scientific computation. Many problems can be reduced to the solution of one or many (large-scale) linear systems. Some examples include least-squares regression, kernel methods, second-order optimization, quadratic programming, Kalman filtering, linear differential equations and all Gaussian (process) inference. Here, we will solve such a system using one of ProbNum' *probabilistic linear solvers*.\n"