Mathematics for Machine Learning with Python- CST284 KTU Minor - Dr. Binu V P -9847390760

  In the unconstrained optimization we considered the problem of solving for the minimum of a function $min \quad f(x) $ where $f : \mathbb{R}^D \to  \mathbb{R}$ In the constrained optimization we have additional constraints. That is, for real-valued functions $g_i : \mathbb{R}^D \to \mathbb{R}$ for $i = 1,\ldots,m$, we consider the constrained optimization problem  $min\quad f(x)$  subject to $g_i(x) \le 0$ for all  $i = 1,\ldots,m$. In general the functions $f$ and $g_i$ could be non-convex in general. One obvious, but not very practical, way of converting the constrained problem into an unconstrained one is to use an indicator function $J(x)=f(x)+ \sum_{i=1}^{m} 1(g_i(x))$ where $1(z)$ is an infinite step function $1(z)=\left\{\begin{matrix} 0 & if z \le 0\\ \infty & otherwise \end{matrix}\right\}$ This gives infinite penalty if the constraint is not satisfied and hence would provide the same solution.However, this infinite step funct...