Link to derivative computation sections

Gram-matrix-parameterization.md

@@ -115,8 +115,8 @@ We minimize the loss function using a cheap imitation of Ueda and Yamashita's re
 The minimization routine is implemented in [`engine.rs`](../src/branch/main/app-proto/src/engine.rs). (In the old Julia prototype of the engine, it's in [`Engine.jl`](../src/branch/main/engine-proto/gram-test/Engine.jl).) It works like this.
 
 1. Do Newton steps, as described below, until the loss gets tolerably close to zero. Fail out if we reach the maximum allowed number of descent steps.
-   1. Find $-\operatorname{grad}(f)$, as described in "The first derivative of the loss function."
-   2. Find the Hessian $H(f) := d\operatorname{grad}(f)$, as described in "The second derivative of the loss function."
+   1. Find $-\operatorname{grad}(f)$, as described in ["The first derivative of the loss function."](Gram-matrix-parameterization#the-first-derivative-of-the-loss-function)
+   2. Find the Hessian $H(f) := d\operatorname{grad}(f)$, as described in ["The second derivative of the loss function."](Gram-matrix-parameterization#the-first-derivative-of-the-loss-function)
       * Recall that we express $H(f)$ as a matrix in the standard basis for $\operatorname{End}(\mathbb{R}^n)$.
    3. If the Hessian isn't positive-definite, make it positive definite by adding $-c \lambda_\text{min}$, where $\lambda_\text{min}$ is its lowest eigenvalue and $c > 1$ is a parameter of the minimization routine. In other words, find the regularized Hessian
       $$H_\text{reg}(f) := H(f) + \begin{cases}0 & \lambda_\text{min} > 0 \\ -c \lambda_\text{min} & \lambda_\text{min} \le 0 \end{cases}.$$