double ticks required for nbsphinx links with code block?

docs/notebooks/3new_groups.ipynb

@@ -12,7 +12,7 @@
    "metadata": {},
    "source": [
     "Implementing a new group equivariance in our library is fairly straightforward. \n",
-    "You need to specify the discrete and continuous generators of the group in a given representation: $\\rho(h_i)$ and $d\\rho(A_k)$, and then call the init method. These two fields, `self.discrete_generators` and `self.lie_algebra` should be a sequence of square matrices. These can either be specified as dense arrays (such as through `np.ndarray`s of size `(M,n,n)` and `(D,n,n)`) or as [`LinearOperator`](https://github.com/mfinzi/equivariant-MLP/blob/master/emlp/solver/linear_operators.py) objects that implement matmul lazily. In general it's possible to implement any matrix group, and we'll go through a few illustrative examples. After checking out these examples, you can browse through the implementations for many other groups [here](https://github.com/mfinzi/equivariant-MLP/blob/master/emlp/solver/groups.py)."
+    "You need to specify the discrete and continuous generators of the group in a given representation: $\\rho(h_i)$ and $d\\rho(A_k)$, and then call the init method. These two fields, `self.discrete_generators` and `self.lie_algebra` should be a sequence of square matrices. These can either be specified as dense arrays (such as through `np.ndarray`s of size `(M,n,n)` and `(D,n,n)`) or as [``LinearOperator``](https://github.com/mfinzi/equivariant-MLP/blob/master/emlp/solver/linear_operators.py) objects that implement matmul lazily. In general it's possible to implement any matrix group, and we'll go through a few illustrative examples. After checking out these examples, you can browse through the implementations for many other groups [here](https://github.com/mfinzi/equivariant-MLP/blob/master/emlp/solver/groups.py)."
    ]
   },
   {
@@ -227,9 +227,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For larger representations our solver uses an iterative method that benefits from faster multiplies with the generators. Instead of specifying the generators using dense matrices, you can specify them as [`LinearOperator`](https://github.com/mfinzi/equivariant-MLP/blob/master/emlp/solver/linear_operators.py) objects in a way that makes use of known structure (like sparsity, permutation, etc). These LinearOperator objects are modeled after [scipy Linear Operators](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.LinearOperator.html) but adapted to be compatible with jax and with some additional features.\n",
+    "For larger representations our solver uses an iterative method that benefits from faster multiplies with the generators. Instead of specifying the generators using dense matrices, you can specify them as [``LinearOperator``](https://github.com/mfinzi/equivariant-MLP/blob/master/emlp/solver/linear_operators.py) objects in a way that makes use of known structure (like sparsity, permutation, etc). These LinearOperator objects are modeled after [scipy Linear Operators](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.LinearOperator.html) but adapted to be compatible with jax and with some additional features.\n",
     "\n",
-    "Returning to the alternating group example, we can specify the generators as permutation operators directly. There are many useful LinearOperators implemented in [`LinearOperator`](https://github.com/mfinzi/equivariant-MLP/blob/master/emlp/solver/linear_operators.py) which we recommend using if available, but we will go through the minimum steps for implementing a new operator like Permutation as an example.\n",
+    "Returning to the alternating group example, we can specify the generators as permutation operators directly. There are many useful LinearOperators implemented in [``LinearOperator``](https://github.com/mfinzi/equivariant-MLP/blob/master/emlp/solver/linear_operators.py) which we recommend using if available, but we will go through the minimum steps for implementing a new operator like Permutation as an example.\n",
     "\n",
     "Note that you need to be using quite large representations before any speedups will be felt due to the increased compile times with Jax (we are hoping to speed this up)."
    ]

docs/notebooks/4new_representations.ipynb

@@ -27,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As a first example, we show one can implement the real irreducible representations of the group SO(2). All of irreducible representations $\\psi_n$ of SO(2) are $2$-dimensional (except for $\\psi_0$ which is the same as [`Scalar`](https://emlp.readthedocs.io/en/latest/package/emlp.solver.representation.html#emlp.models.solver.representation.Scalar) $= \\mathbb{R} = \\psi_0$). These representations can be written $\\psi_n(R_\\theta) = \\begin{bmatrix}\\cos(n\\theta) &\\sin(n\\theta)\\\\-\\sin(n\\theta) &  \\cos(n\\theta) \\end{bmatrix}$ or simply: $\\psi_n(R) = R^n$."
+    "As a first example, we show one can implement the real irreducible representations of the group SO(2). All of irreducible representations $\\psi_n$ of SO(2) are $2$-dimensional (except for $\\psi_0$ which is the same as [Scalar](https://emlp.readthedocs.io/en/latest/package/emlp.solver.representation.html#emlp.models.solver.representation.Scalar) $= \\mathbb{R} = \\psi_0$). These representations can be written $\\psi_n(R_\\theta) = \\begin{bmatrix}\\cos(n\\theta) &\\sin(n\\theta)\\\\-\\sin(n\\theta) &  \\cos(n\\theta) \\end{bmatrix}$ or simply: $\\psi_n(R) = R^n$."
    ]
   },
   {