ltv $=[354,181,283,107]$

conv$\_$rate $=[0\mathrm{.}3,0\mathrm{.}32,0\mathrm{.}3,0\mathrm{.}3]$

def estimate$\_$optimal$\_$bids$($alpha$\_$beta, ltv$,$ conv$\_$rate$)$:

alpha, beta $=$ alpha$\_$beta

x$0=$ np$.$zeros$(4)$

params$0=$ np$.$concatenate$([$x$0,[$alpha$,$ beta$],$ ltv$,$ conv$\_$rate$])$

bounds $=[(0,$ None$)]*4+[($None$,$ None$),($None$,$ None$)]+[(0,$ None$)]*4$

result $=$ minimize$($objective$,$ params$0,$ bounds$=$bounds$)$

optimal$\_$bids $=$ result.x$[$:$4]$

return optimal$\_$bids

alpha$\_$beta$\_$kw$8322228=[0\mathrm{.}1,0\mathrm{.}2]$

optimal$\_$bids$\_$kw$8322228=$ estimate$\_$optimal$\_$bids$($alpha$\_$beta$\_$kw$8322228,$ ltv$,$ conv$\_$rate$)$

print$(\text{'}$kw$8322228-$ optimal bids:$\text{'},$ optimal$\_$bids$\_$kw$8322228)$

$---------------------------------------------------------------------------$

ValueError Traceback $($most recent call last$)$

$/$var$/$folders$/$pv$/$wv$1191$lx$7$kv$6$ns$91$rgv$99$l$\_$h$0000$gn$/$T$/$ipykernel$\_51013/1741954947.$py in

$12$

$13$ alpha$\_$beta$\_$kw$8322228=[0\mathrm{.}1,0\mathrm{.}2]$

$--->14$ optimal$\_$bids$\_$kw$8322228=$ estimate$\_$optimal$\_$bids$($alpha$\_$beta$\_$kw$8322228,$ ltv$,$ conv$\_$rate$)$

$15$ print$(\text{'}$kw$8322228-$ optimal bids:$\text{'},$ optimal$\_$bids$\_$kw$8322228)$

$/$var$/$folders$/$pv$/$wv$1191$lx$7$kv$6$ns$91$rgv$99$l$\_$h$0000$gn$/$T$/$ipykernel$\_51013/1741954947.$py in estimate$\_$optimal$\_$bids$($alpha$\_$beta, ltv$,$ conv$\_$rate$)$

$7$ params$0=$ np$.$concatenate$([$x$0,[$alpha$,$ beta$],$ ltv$,$ conv$\_$rate$])$

$8$ bounds $=[(0,$ None$)]*4+[($None$,$ None$),($None$,$ None$)]+[(0,$ None$)]*4$

$---->9$ result $=$ minimize$($objective$,$ params$0,$ bounds$=$bounds$)$

$10$ optimal$\_$bids $=$ result.x$[$:$4]$

$11$ return optimal$\_$bids

~$/$opt$/$anaconda$3/$lib$/$python$3\mathrm{.}9/$site$-$packages$/$scipy$/$optimize$/\_$minimize.py in minimize$($fun$,$ x$0,$ args, method, jac, hess, hessp, bounds, constraints, tol, callback, options$)$

$682$ if callable$($jac$)$:

$683$ jac $=\_$remove$\_$from$\_$func$($jac$,$ i$\_$fixed, x$\_$fixed, remove$=1)$

$-->684$

$685$ # make a copy of the constraints so the user's version doesn't

$686$ # get changed. $($Shallow copy is ok$)$

~$/$opt$/$anaconda$3/$lib$/$python$3\mathrm{.}9/$site$-$packages$/$scipy$/$optimize$/\_$minimize.py in standardize$\_$bounds$($bounds$,$ x$0,$ meth$)$

$945$ res $=\_$minimize$\_$scalar$\_$bounded$($fun$,$ bounds, args, $**$options$)$

$946$ elif meth $==$ 'golden':

$-->947$ res $=\_$recover$\_$from$\_$bracket$\_$error$(\_$minimize$\_$scalar$\_$golden,

$948$ fun, bracket, args, $**$options$)$

$949$ else:

~$/$opt$/$anaconda$3/$lib$/$python$3\mathrm{.}9/$site$-$packages$/$scipy$/$optimize$/\_$constraints.py in new$\_$bounds$\_$to$\_$old$($lb$,$ ub$,$ n$)$

$395$

$396$ excess$\_$lb $=$ np$.$maximum$($self$.$bounds$[0]-$ ev$,0)$

$-->397$ excess$\_$ub $=$ np$.$maximum$($ev $-$ self.bounds$[1],0)$

$398$

$399$ return excess$\_$lb $+$ excess$\_$ub

~$/$opt$/$anaconda$3/$lib$/$python$3\mathrm{.}9/$site$-$packages$/$numpy$/$core$/$overrides$.$py in broadcast$\_$to$(*$args$,**$kwargs$)$

~$/$opt$/$anaconda$3/$lib$/$python$3\mathrm{.}9/$site$-$packages$/$numpy$/$lib$/$stride$\_$tricks.py in broadcast$\_$to$($array$,$ shape, subok$)$

$411[1,2,3]])$

$412"""$

$-->413$ return $\_$broadcast$\_$to$($array$,$ shape, subok$=$subok, readonly$=$True$)$

$414$

$415$

~$/$opt$/$anaconda$3/$lib$/$python$3\mathrm{.}9/$site$-$packages$/$numpy$/$lib$/$stride$\_$tricks.py in $\_$broadcast$\_$to$($array$,$ shape, subok, readonly$)$

$347$ 'negative'$)$

$348$ extras $=[]$

$-->349$ it $=$ np$.$nditer$($

$350($array$,),$ flags$=[\text{'}$multi$\_$index', 'refs$\_$ok$\text{'},$ 'zerosize$\_$ok$\text{'}]+$ extras,

$351$ op$\_$flags$=[\text{'}$readonly$\text{'}],$ itershape$=$shape, order$=\text{'}$C$\text{'})$

ValueError: operands could not be broadcast together with remapped shapes $[$original$->$remapped$]$: $(10,)$ and requested shape $(14,)$