Hello, this is my assignment and I can write my code on the range of # your implementation starts below, # your implementation ends above. I want to generate a sparse matrix that is here: $[[10.0.5.0.25.0.10.0.]$

$[0.10.0.5.0.25.0.10.]$

$[1.1.0.0.0.0.0.0.]$

$[0.0.1.1.0.0.0.0.]$

$[0.0.0.0.1.1.0.0.]$

$[0.0.0.0.0.0.1.1.]]$ However, I cannot generate this matrix. You can see my codes in the below. My result is: $[[11.1.5.0.25.0.10.0.][0.10.1.6.0.25.0.10.][0.0.0.0.1.1.0.0.][0.0.0.0.0.0.1.1.][0.0.0.0.0.0.0.0.][0.0.0.0.0.0.0.0.]].$ Am I use v$-$stack? Could you help me$?$ Really, I am confused and I cannot create this matrix. I think the problem is about the column code but I could not fix the problem. Thank you.

# load libraries

import numpy as np

import scipy.sparse as sp

import cplex as cp def mixed$\_$integer$\_$linear$\_$programming$($direction$,$ A$,$ senses$,$ b$,$ c$,$ l$,$ u$,$ types, names$)$:

# create an empty optimization problem

prob $=$ cp$.$Cplex$()$

# add decision variables to the problem including their coefficients in objective and ranges

prob.variables.add$($obj $=$ c$.$tolist$(),$ lb $=$ l$.$tolist$(),$ ub $=$ u$.$tolist$(),$ types $=$ types.tolist$(),$ names $=$ names.tolist$())$

# define problem type

if direction $==$ "maximize":

prob.objective.set$\_$sense$($prob$.$objective.sense.maximize$)$

else:

prob.objective.set$\_$sense$($prob$.$objective.sense.minimize$)$

# add constraints to the problem including their directions and right$-$hand side values

prob.linear$\_$constraints.add$($senses $=$ senses$.$tolist$(),$ rhs $=$ b$.$tolist$())$

# add coefficients for each constraint

row$\_$indices, col$\_$indices $=$ A$.$nonzero$()$

prob.linear$\_$constraints.set$\_$coefficients$($zip$($row$\_$indices.tolist$(),$ col$\_$indices.tolist$(),$ A$.$data.tolist$()))$

print$($prob$.$write$\_$as$\_$string$())$

# solve the problem

prob.solve$()$

# check the solution status

print$($prob$.$solution.get$\_$status$())$

print$($prob$.$solution.status$[$prob$.$solution.get$\_$status$()])$

# get the solution

x$\_$star $=$ prob.solution.get$\_$values$()$

obj$\_$star $=$ prob.solution.get$\_$objective$\_$value$()$

return$($x$\_$star, obj$\_$star$)$ def coin$\_$distribution$\_$problem$($coins$\_$file, M$)$:

# your implementation starts below

coins $=$ np$.$loadtxt$($coins$\_$file$)$

m $=$ coins.shape$[0]$

c $=$ np$.$repeat$(1,2*$ m $)$

senses $=$ np$.$concatenate$(($np$.$repeat$("$E$",$ int$($m$/2)),($np$.$repeat$("$E$",$ m$))))$

print$($senses$)$

f $=$ np$.$repeat$($coins$[2],$ int$($m$/2))$

$=$ np$.$repeat$(1,$ m$)$

print$($f$)$

print$()$

b $=$ np$.$concatenate$([$f$,])$

print$($b$,"$B$1")$

l $=$ np$.$repeat$(0,2*$ m$)$

print$($l$)$

u $=$ np$.$repeat$(1,2*$ m$)$

print$($u$)$

types $=$ np$.$repeat$("$B$",2*$ m$)$

print$($types$)$

names $=$ np$.$array$(["$x$\_\{\}\_\{\}".$format$($i $+1,$ j $+1)$ for i in range$($m$)$ for j in range$($m$)])$

print$($names$)$

aij$\_1=$ np$.$repeat$($coins$,2)$ # row ve col dizilerini m ile yaz$$yoruz

row$\_1=$ np$.$tile$($np$.$arange$(2),$m$)$

col$\_1=$ np$.$repeat$($np$.$arange$(2*$ m$),1)$

A$\_1=$ sp$.$csr$\_$matrix$(($aij$\_1,($row