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\begin{document}

\title{Introduction to Machine Learning\\
Homework 5:  Gradient Calculations and Nonlinear Optimization}
\author{Prof. Sundeep Rangan}
\date{}

\maketitle

Submit answers only to problems 1, 3 and 4(b) and (c).  You do not need to answer 2 or 4(a).
But, make sure you know how to do all the problems.

\begin{enumerate}

\item Suppose we want to fit a model,
\[
    \hat{y} = \frac{1}{w_0 + \sum_{j=1}^d w_jx_j},
\]
for parameters $\wbf$. Given training data $(\xbf_i,y_i)$, $i=1,\ldots,n$,
a nonlinear least squares fit could use the loss function,
\[
    J(\wbf) = \sum_{i=1}^n \left[y_i -
        \frac{1}{w_0 + \sum_{j=1}^d w_jx_{ij}}\right]^2
\]
\begin{enumerate}[(a)]
\item Find a function $g(\zbf)$ and matrix $\Abf$ such that
the loss function is given by,
\[
    J(\wbf) = g(\zbf), \quad \zbf = \Abf\wbf,
\]
and $g(\zbf)$ is factorizable, meaning $g(\zbf) = \sum_i g_i(z_i)$ for
some functions $g_i(z_i)$.

\item What is the gradient $\nabla J(\wbf)$?

\item What is the gradient descent update for $\wbf$?

\item Write a few lines of python code to compute the loss function $J(\wbf)$ and
$\nabla J(\wbf)$.
\end{enumerate}

\item In this problem, we will see why gradient descent can often exhibit
very slow convergence, even on apparently simple functions.
Consider the objective function,
\[
    J(\wbf) = \frac{1}{2}b_1w_1^2 + \frac{1}{2}b_2w_2^2,
\]
defined on a vector $\wbf=(w_1,w_2)$ with constants $b_2 > b_1 > 0$.
\begin{enumerate}[(a)]
  \item What is the gradient $\nabla J(\wbf)$?
  \item What is the minimum $\wbf^* = \argmin_{\wbf} J(\wbf)$?
  \item Part (b) shows that we can minimize $J(\wbf)$ easily by hand.
  But, suppose we tried to minimize it via gradient descent.
  Show that the gradient descent update of $\wbf$ with a step-size $\alpha$
has the form,
\[
    w_1^{k+1} = \rho_1 w_1^k, \quad w_2^{k+1} = \rho_2 w_2^k,
\]
for some constants $\rho_i$, $i=1,2$.  Write $\rho_i$ in terms of
$b_i$ and the step-size $\alpha$.
  \item For what values $\alpha$ will gradient descent converge to the minimum? That is, what step sizes guarantee that $\wbf^k \arr \wbf^*$.

\item Take $\alpha = 2/(b_1+b_2)$.  It can be shown that this choice of $\alpha$ results in the fastest convergence.  You do not need to show this.
But, show that with this selection of $\alpha$,
\[
    \|\wbf^k\| = C^k \|\wbf^0\|, \quad C = \frac{\kappa-1}{\kappa+1 }, \quad
    \kappa = \frac{b_2}{b_1}.
\]
The term $\kappa$ is called the \emph{condition number}.  The above calculation
shows that when $\kappa$ is very large, $C \approx 1$ and the convergence
of gradient descent is slow.  In general, gradient descent performs poorly
when the problems are ill-conditioned like this.
\end{enumerate}

\item \emph{Matrix minimization}.  Consider the problem of finding a matrix
$\Pbf \in \R^{m \x m}$ to minimize the loss function,
\[
    J(\Pbf) = \sum_{i=1}^n \left[ \frac{z_i}{y_i} - \ln(z_i) \right],
    \quad
    z_i = \xbf_i\tran \Pbf \xbf_i.
\]
The problem arises in wireless communications where an $m$-antenna receiver wishes
to estimate a spatial covariance matrix $\Pbf$ from $n$ power measurements.
In this setting, $y_i > 0$ is the $i$-th receive power measurement and $\xbf_i$
is the beamforming direction for that measurement.  In reality, the quantities
would be complex, but for simplicity we will just look at the real-valued case.
See the following article for more details:
\begin{quote}
  Eliasi, Parisa A., Sundeep Rangan, and Theodore S. Rappaport. ``Low-rank spatial channel estimation for millimeter wave cellular systems," \emph{IEEE Transactions on Wireless Communications} 16.5 (2017): 2748-2759.
\end{quote}
\begin{enumerate}[(a)]
\item What is the gradient $\nabla_{\Pbf} z_i$?

\item What is the gradient $\nabla_{\Pbf} J(\Pbf)$?

\item Write a few lines of python code to evaluate $J(\Pbf)$ and
$\nabla_{\Pbf} J(\Pbf)$ given data $\xbf_i$ and $y_i$.  You can use a for loop.

\item See if you can rewrite (c) without a for loop.  You will need Python broadcasting.
\end{enumerate}

\item \emph{Nested optimization}.  Suppose we are given a loss function
$J(\wbf_1,\wbf_2)$ with two parameter vectors $\wbf_1$ and $\wbf_2$.
In some cases, it is easy to minimize over one of the sets of parameters, say
$\wbf_2$, while holding the other parameter vector (say, $\wbf_1$) constant.
In this case, one could perform the following \emph{nested} minimization:
Define
\[
    J_1(\wbf_1) := \min_{\wbf_2} J(\wbf_1,\wbf_2), \quad
    \wbfhat_2(\wbf_1) := \argmin_{\wbf_2} J(\wbf_1,\wbf_2),
\]
which represent the minimum and argument of the loss function over $\wbf_2$
holding $\wbf_1$ constant.   Then,
\[
    \wbfhat_1 = \argmin_{\wbf_1} J_1(\wbf_1) = \argmin_{\wbf_1} \min_{\wbf_2}
    J(\wbf_1,\wbf_2).
\]
Hence, we can find the optimal $\wbf_1$ by minimizing $J_1(\wbf_1)$
instead of minimizing $J(\wbf_1,\wbf_2)$ over $\wbf_1$ and $\wbf_2$.
\begin{enumerate}[(a)]
\item Show that the gradient of $J_1(\wbf_1)$ is given by
\[
    \nabla_{\wbf_1} J_1(\wbf_1) = \left. \nabla_{\wbf_1} J(\wbf_1,\wbf_2)\right|_{\wbf_2=\wbfhat_2}.
\]
Thus, given $\wbf_1$, we can evaluate the gradient from (i) solve the minimization
$\wbfhat_2:= \argmin_{\wbf_2} J(\wbf_1,\wbf_2)$; and (ii) take the gradient
$\nabla_{\wbf_1} J(\wbf_1,\wbf_2)$ and evaluate at $\wbf_2 = \wbfhat_2$.

\item Suppose we want to minimize a nonlinear least squares,
\[
    J(\abf,\bbf) := \sum_{i=1}^n \left( y_i -
        \sum_{j=1}^d b_j e^{-a_jx_i} \right)^2,
\]
over two parameters $\abf$ and $\bbf$.  Given parameters $\abf$,
describe how we can minimize over $\bbf$.  That is, how can we compute,
\[
    \hat{\bbf} := \argmin_{\bbf} J(\abf,\bbf).
\]

\item In the above example, how would we compute the gradients,
\[
    \nabla_\abf J(\abf,\bbf).
\]

\end{enumerate}

\end{enumerate}
\end{document}