Problem:
I assume the problem is
$$
\begin{align}
S
&= \max_{c \in \{1, \ldots, 4L\}}
\sum_{i=1}^{4L} N_i \min \left(|i-c|, 4L-|i-c|\right) \\
&= \max_{c \in \{1, \ldots, 4L\}} T(c)
\end{align}
\quad (*)
$$
Complexity Simple Search:
A simple search of $(*)$ over all $c$-values would require about $(4L)^2$ term evaluations, needing 1 $+$, 1 $\cdot$, 2 $-$, 1 $\min(.,.)$ and 1 $|.|$ calculation each.
Lemma 1:
The change $\Delta T$ for increasing $c$ is:
$$
\Delta T(c) := T(c+1) - T(c)
$$
It can be calculated recursively:
$$
\begin{align}
\Delta T(1) &:= N_1 - \sum_{i=2}^{2L+1} N_i + \sum_{i=2L+2}^{4L} N_i \\
\Delta T(c) &:= \Delta T(c-1) + 2(N_{c} - N_{2L + c})
\quad c \in \{ 2, \ldots, 2L \}
\end{align}
\quad (**)
$$
Lemma 2: The change has a symmetry:
$$
\Delta T(c) = -\Delta T(c - 2L),
\quad c \in \{ 2L+1, \ldots, 4L \}
$$
Thus we can calculate the $2L$ values of $\Delta T$ in the second half of the $c$-interval faster, because we can make use of the values from the first half.
Quick Summation:
Calculate the relative sum vector $Q$:
$$
\begin{align}
Q(1) &:= 0 \\
Q(c) &:= Q(c-1) + \Delta T(c-1) \\
\end{align}
\quad c \in \{ 2, \cdots, 4L \}
$$
Lemma 3: The $c^*$ which maximizes $Q(c)$ will maximize $T(c)$ as well.
Solution:
Determine $c^*$ according to lemma 3 and the quick summation procedure. Then calculate
$$
S = T(c^*)
$$
using the original defintion $(*)$ or even better using the optimized term evaluation:
$$
\begin{align}
T_{L->R}(c)
&:= \sum_{i = 1}^{c-1} N_i \, (c-i) +
\sum_{i = c+1}^{2L+c-1} N_i \, (i-c) +
\sum_{i = 2L+c}^{4L} N_i \, (4L - (i-c)) \\
& \quad c \in \{ 1, \ldots, 2L \}
\\
T_{R->L}(c)
&:= \sum_{i = 1}^{c - 2L} N_i \, (4L - (c-i)) +
\sum_{i = c - 2L + 1}^{c-1} N_i \, (c-i) +
\sum_{i = c + 1}^{4L} N_i \, (i-c) \\
& \quad c \in \{ 2L+1, \ldots, 4L \}
\end{align}
\quad (\#)
$$
The optimized term $(\#)$ avoids minimum and absolute value operations. Its greater benefit is that it allows to determine the differences for lemma 1.
Complexity of the Solution:
The complexity relative to the simple search has been reduced to less than $16 L$ $+$, $12 L$ $-$ and $6L$ $\cdot$ operations. Or roughly from quadratic to linear. In both cases not considering the similar $4L$ comparisons for the extremum search.
Bonus Lemma 1: The same procedure works for searching the minimum sum efficiently, just replace $\max$ with $\min$.
Bonus Lemma 2: The $N_i$ can be arbitrary real numbers.
Derivation of the Optimized Terms:
We can avoid the minimum calculations in $(*)$ by using $T_{L\to R}$ for the $c$-values from $1$ up to $c_s = 2L$ and then using $T_{R\to L}$. We further split those sums after $i_{s, L\to R} = 2L+c-1$ and $i_{s, R\to L} = c - 2L$:
$$
\begin{align}
T_{L->R}(c)
&= \sum_{i = 1}^{2L+c-1} N_i \, |i-c| +
\sum_{i = 2L+c}^{4L} N_i \, (4L - (i-c)) \\
T_{R->L}(c)
&= \sum_{i = 1}^{c - 2L} N_i \, (4L - (c-i)) +
\sum_{i = c - 2L + 1}^{4L} N_i \, |i-c|
\end{align}
$$
And we can split up two sums further to avoid the decisions during absolute value calculations, getting $(i-c)$ and $(c-i)$ factors. This yields $(\#)$.
Proof Lemma 1:
The change $\Delta T$ for increasing $c$ is:
$$
\Delta T(c) := T(c+1) - T(c) =
\sum_{i=1}^c N_i - \sum_{i=c+1}^{2L+c} N_i + \sum_{i=2L+c+1}^{4L} N_i,
\quad c \in \{ 1, \ldots, 2L \}
\quad (\#\#)
$$
This has been derived from $(\#)$ by analyzing how that expression changes from $c$ to $c+1$. It is a discrete analogon to differentiation. The expression is simpler than $(*)$ but still contains the information to determine the optimum.
Applying this idea again: If one knows $\Delta T(c)$, one can calculate $\Delta T(c+1)$ using:
$$
\Delta^2 T(c) := \Delta T(c+1) - \Delta T(c) =
2(N_{c+1} - N_{2L+c+1})
$$
This has been derived from $(\#\#)$. The result is the recursive scheme $(**)$.
Proof Lemma 2: Start with the second branch from $(\#)$.
Determine the difference like in the proof of lemma 1.
Compare the result with $(\#\#)$.
Proof Lemma 3: The relative sum $Q$ differs only by a constant from $T$
$$
Q(c) = T(c) - T(1).
$$
Appendix:
- Here is a program
written in Ruby,
for those who want to experiment a bit more with this problem.
Sample output
here or
here or
here.
It generates random $N$-vectors and also checks if the optimized equations
$(\#)$ give the same results like the original $(*)$.
- Observation: The changes of the sums
$$
\frac{\Delta}{\Delta c} \sum\limits_{i = a(c)}^{b(c)}t(i, c)
$$
seem to behave similar to the derivatives of the parameter integrals
$$
\frac{\partial}{\partial c} \int\limits_{a(c)}^{b(c)} t(x, c) \, dx =
t(b(c),c) \, b'(c) - t(a(c), c) \, a'(c) +
\int\limits_{a(c)}^{b(c)} \!\! \frac{\partial}{\partial c} t(x, c) \, dx
$$