update on jupyter-book

doc/BookChapters/chapteroptimization.do.txt

@@ -300,25 +300,22 @@ ensures convexity of a function $f$. We write $D_f$ to denote the
 domain of $f$, i.e the subset of $R^n$ where $f$ is defined. For more
 details and proofs we refer to: "S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press":"http://stanford.edu/boyd/cvxbook/, 2004".
 
-!bblock First order condition
-Suppose $f$ is differentiable (i.e $\nabla f(x)$ is well defined for
+_First order condition_: Suppose $f$ is differentiable (i.e $\nabla f(x)$ is well defined for
 all $x$ in the domain of $f$). Then $f$ is convex if and only if $D_f$
-is a convex set and $$f(y) \geq f(x) + \nabla f(x)^T (y-x) $$ holds
+is a convex set and $f(y) \geq f(x) + \nabla f(x)^T (y-x)$ holds
 for all $x,y \in D_f$. This condition means that for a convex function
 the first order Taylor expansion (right hand side above) at any point
 is a global under estimator of the function. To convince yourself you can
 make a drawing of $f(x) = x^2+1$ and draw the tangent line to $f(x)$ and
 note that it is always below the graph.  
-!eblock
 
-!bblock Second order condition 
-Assume that $f$ is twice
+
+_Second order condition_: Assume that $f$ is twice
 differentiable, i.e the Hessian matrix exists at each point in
 $D_f$. Then $f$ is convex if and only if $D_f$ is a convex set and its
 Hessian is positive semi-definite for all $x\in D_f$. For a
 single-variable function this reduces to $f''(x) \geq 0$. Geometrically this means that $f$ has nonnegative curvature
 everywhere.
-!eblock
 
 This condition is particularly useful since it gives us an procedure for determining if the function under consideration is convex, apart from using the definition.
 
@@ -1705,6 +1702,7 @@ Autograd supports many features. However, there are some functions that is not s
 
 Assigning a value to the variable being differentiated with respect to
 !bc pycod
+"""
 import autograd.numpy as np
 from autograd import grad
 def f8(x): # Assume x is an array
@@ -1716,6 +1714,7 @@ f8_grad = grad(f8)
 x = 8.4
 
 print("The derivative of f8 is:",f8_grad(x))
+"""
 !ec
 Here, Autograd tells us that an 'ArrayBox' does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible.