Program the steepest descent algorithm with fixed step size to minimize l$.$ Plot the objective function as a function of number of iterations.

$1.$ use python as language

$2.$ Initial solution is set to be the vector of zeros, i$.$e$.$ w$0=0.$

$3.$ The algorithm stops when the maximum iteration $(1000)$ is hit

$4.$ The loss function is given by negative log$-$likelihood

l$($w$)=-(1)/($n$)\backslash$sum$\_($i$=1)^$n log$\backslash$sigma $($y$\_($i$)$w$^($T$)$x$\_($i$)$Fix the step size to be $0\mathrm{.}85,$ where k is the number of iterations k$.$ Using the optimizer w$^(*),$ a data point x$\_($i$)$ can be classified

according to whether $\backslash$sigma $($y$\_($i$)($w$^(*))^($T$)$x$\_($i$))>=0\mathrm{.}5,$ i$.$e$.$

y$\_($i$)=\{(+1,$ if $($w$^(*))^($T$)$x$\_($i$)>=0),(-1,$ otherwise $)$:$\}$