PROBLEM LINK:
Author: Sergey Kulik
Tester: Yanpei Liu
Editorialist: Pawel Kacprzak
DIFFICULTY:
Medium
PREREQUISITES:
dp
PROBLEM:
Your working day is divided into N periods. In each one, you can either write some code or drink a cup of coffee. If you decide to write code in the i^{th} period, you will write a_i lines in that period. On the other hand, if you decide to get a cup of coffee in the i^{th} period, then you do not write any lines of code in that period, but for each period j > i, such that i + D \geq j, the number of lines that you write in that period gets multiplied by M. The only restriction is that you have to drink exactly K cups of coffee during the whole day. Given N, K, D, M and the array a of size N, your task is to compute the maximum number of lines of code which you can write during all N periods.
QUICK EXPLANATION:
Please, skip this section if you want to write about how to solve subtasks or how to derive the desired solution.
A quick explanations is that, you can use dynamic programming to count the maximum number of lines of code which you can write during the first i periods, drinking exactly j cups of coffee, and drinking the last one at time t.
EXPLANATION:
If the problem looks complicated at first sight, it is always good to simplify it a bit.
Simplified problem
Letâ€™s consider a version of the problem, when K equals 1, so you are have to drink exactly one cup of coffee during the whole day. Solving this simplifier problem is enough to complete the first subtask here. Letâ€™s assume that we know what period is the best for drinking coffee. Let denote this period by P. How to compute the number of lines of which we write, when we drink coffee during period P? Well, this is pretty simple:

In period P, we do not write any code, because we drink coffee then.

In each period j > P, such that P + D \geq j, we write M \cdot a_j lines

In any other period i, we write a_i lines.
So for a fixed period P, we can compute the number of lines we write in O(N). If we try any period as P, we can solve this simplified problem in O(N^2) time, which is perfectly fine for the first subtask, because N \leq 18 there. Since we have T test cases to handle, the total complexity of that solution is O(T \cdot N^2).
General problem for small N
Ok, so the case when K = 1 for not big N is quite simple, letâ€™s try now to solve the problem for any K < N, but without worrying about the number total number of periods. Do we know how to solve the problem, if we fix all K periods during which we drink coffee? Of course we know, and this is very similar to a method presented in in the above paragraph:
Let S be a set of K periods during which we drink coffee:

In any period from S, we do not write any code, because we drink coffee during these periods.

For any other period j, let P be a period with the greatest index less than j, such that we drank coffee in that period. If P exists and P + d \geq j, we write M \cdot a_j lines of code in period j, otherwise, we write just a_j lines in that period.
If we scan periods from left to right, updating the last period when we drank coffee, we can compute the number of lines of code we write for that fixed set S in O(N) time. In order to compute the best set S, we can iterate over all \binom{N}{ K} such sets, and compute the best one. In the second subtask, N is also not greater than 18, so that method will work there, but since there are at most 200 test cases to handle, you can precompute a list of all Kelement subsets of the set of N periods for any N, K, where 1 \leq K \leq N \leq 18. Doing that, you can achieve the total time complexity of O(2^N + T \cdot \binom{N}{K} \cdot N).
Speeding it up
However, in order to get a full credit for that problem, you have a much faster solution Original constraints are 1 \leq K < N \leq 200. Letâ€™s try to derive it.
In any period i, if you have drunk less than K cups of coffee in previous periods, you can either drink a cup and do not write any lines of code, or do not drink a cup and write a_i lines, or write M \cdot a_i lines, if you have drunk at least one cup in any of last D periods. This reasoning leads to a nice and clear dynamic programming approach.
Let g[i][j][t] be the maximum lines of code that you can write during the first i periods, drinking exactly j cups of coffee, and drinking the last one in period t. Then you can use the following rules to compute the whole g table. Notice that the result here is \max \{g[N][K][t] : K \leq t \leq N\}:
//initially, set all entries in g table to 1, because some entries are invalid, like when the number of drunk cups is greater than the number of periods //computing g[i + 1][j][t] if i + 1 > t && t + D >= i + 1: lines = m * a[i + 1] else: lines = a[i + 1] g[i + 1][j][t] = max(g[i + 1][j][t], g[i][j][t] + lines) g[i + 1][j + 1][t] = max(g[i + 1][j + 1][t], g[i][j][t])
The correctness of this method is pretty straightforward to prove, because we consider here all possibilities here. The idea behind this kind of dynamic programming approaches is that you do not have to know in what periods you decided to drink coffee, you just need to know how many cups you have drunk so far and when you drunk the last one. Using this idea, we can speed up exponential solutions for many problems.
The time complexity of this approach for a single test case is O(N^3) and you may wonder if it is enough to pass the third subtask, since there are up to 200 test cases to handle. Well, just notice that the sum of N in all of them is at most 1000, so we are all good with the third subtask. However it is still too slow to get a full credit.
Final solution
How to get even faster solution? If you look closer at the above dynamic programming structure, you will see that for many entries, the result is the best entry computed before plus the sum of lines written in periods between these two entries with or without a multiplier M applied to them. Based on this idea, we can derive even faster dynamic programming solution:
Let a[N+1] = a[N+2] = ... = a[N+N] = 0].
In addition, let p[i] := \sum\limits_{j=1}^i a[i], in other words, p[i] is the sum of the number of lines of code written in periods from 1 to i without any multiplier.
With these definitions, we can start working on a new dynamic programming approach.
Let f[i][j] be the maximum number of lines of code produced during the first i + D periods, such that in exactly j of these periods we decided to drink a cup of coffee and we have drunk the last one in the i^{th} period.
If we are able to compute the f table, we can easily compute the result as the maximum value of f[i][K] for 1 \leq i \leq N.
So how can we compute the f table? First, notice that the value of f[i][1], for any 1 \leq i \leq N, can be easily computed using the p table in constant time.
Now, we want to know how to compute the value of f[i][j] knowing the values of f[i'][j  1] for i' < i.
There are two cases to consider:
1. i' + D < i
Then :
Notice that for a fixed i, the above value varies only on f[i'][j  1] + p[i' + D], so in order to compute f[i][j] in this case, we only need to know what is the value of i' which maximizes this term, and it is easy to compute the best values of it in previous steps.
2. i' + D \geq i
Then
The same similar observation as in the previous case applies here. For a fixed i, the only varying term is f[i'][j  1]  p[i' + D] \cdot M, and in order to pick the best i', you can use values precomputed in previous steps. The tester used queue to deal with it, check his solution for more details.
Time complexity
The total complexity of this method is $O(N \cdot D)$, because this is the size of $f$ table, and we fill each of its entries in a contant time.AUTHORâ€™S AND TESTERâ€™S SOLUTIONS:
Authorâ€™s solution can be found here.
Testerâ€™s solution can be found here.