Finding the minimal product - c++

How do you find the minimal product from an array? This is the problem I have and the attempted solution isn't working. What have I done wrong?
https://www.codechef.com/problems/CHRL4
After visiting a childhood friend, Chef wants to get back to his home. Friend lives at the first street, and Chef himself lives at the N-th (and the last) street. Their city is a bit special: you can move from the X-th street to the Y-th street if and only if 1 <= Y - X <= K, where K is the integer value that is given to you. Chef wants to get to home in such a way that the product of all the visited streets' special numbers is minimal (including the first and the N-th street). Please, help him to find such a product.
Input
The first line of input consists of two integer numbers - N and K - the number of streets and the value of K respectively. The second line consist of N numbers - A1, A2, ..., AN respectively, where Ai equals to the special number of the i-th street.
Output
Please output the value of the minimal possible product, modulo 1000000007.
Constraints
1 ≤ N ≤ 10^5
1 ≤ Ai ≤ 10^5
1 ≤ K ≤ N
Example
Input:
4 2
1 2 3 4.
Output:
8
#include <iostream>
using namespace std;
int P(int A[], int N, int K) {
if (N == 1) return A[0];
int m = A[0], prod = m;
for (int i = 1; i < N; ++i) {
if (1 <= A[i]-m && A[i]-m <= K) {
prod *= A[i];
}
}
return prod;
}
int main() {
int A[] = {1, 2, 3, 4};
cout << P(A, 4, 2);
}
I get 6 instead of 8.

Such problems can typically be solved by Dynamic Programming:
Construct an appropriate state variable: Let the state be S = current street. Let the factor at street S be calledC_S
For each state S, collect the possible actions: a(S) = {go to any street T for which : 1 <= C_T - C_S <= K, T <=N }, a(N) = {}.
Introduce a value function V(S) = minimal product to get from S to N. Set V(N) = C_N.
Having all this together, one can now solve the Bellman equation backwards from N, where particularly the value V(0) is sought:
V(S) = min_{allowed T} { V(T)*C_S }
Example implementation:
int main()
{
int N = 4;
int K = 2;
std::vector<int> C{1,2,3,4};
std::vector<int> V(N);
V.back() = C.back();
for(int i = N - 2; i>= 0; --i)
{
int min = std::numeric_limits<int>::max(); //possible overflow here,
//better change that
for(int j=i+1; j< N; ++j)
{
double DeltaC = C[j] - C[i];
if(DeltaC <= K && DeltaC >= 1)
{
double vt = V[j] * C[i];
if(vt < min)
{
min = vt;
}
}
}
V[i] = min;
}
std::cout<<V[0]<<std::endl;
}
DEMO
The output is 8.
Please understand the code, test it and then use it with a good conscience (whatever that means).

Related

Speed problem for summation (sum of divisors)

I should implement this summation in C ++. I have tried with this code, but with very high numbers up to 10 ^ 12 it takes too long.
The summation is:
For any positive integer k, let d(k) denote the number of positive divisors of k (including 1 and k itself).
For example, for the number 4: 1 has 1 divisor, 2 has two divisors, 3 has two divisors, and 4 has three divisors. So the result would be 8.
This is my code:
#include <iostream>
#include <algorithm>
using namespace std;
int findDivisors(long long n)
{
int c=0;
for(int j=1;j*j<=n;j++)
{
if(n%j==0)
{
c++;
if(j!=(n/j))
{
c++;
}
}
}
return c;
}
long long compute(long long n)
{
long long sum=0;
for(int i=1; i<=n; i++)
{
sum += (findDivisors(i));
}
return sum;
}
int main()
{
int n, divisors;
freopen("input.txt", "r", stdin);
freopen("output.txt", "w", stdout);
cin >> n;
cout << compute(n);
}
I think it's not just a simple optimization problem, but maybe I should change the algorithm entirely.
Would anyone have any ideas to speed it up? Thank you.
largest_prime_is_463035818's answer shows an O(N) solution, but the OP is trying to solve this problem
with very high numbers up to 1012.
The following is an O(N1/2) algorithm, based on some observations about the sum
n/1 + n/2 + n/3 + ... + n/n
In particular, we can count the number of terms with a specific value.
Consider all the terms n/k where k > n/2. There are n/2 of those and all are equal to 1 (integer division), so that their sum is n/2.
Similar considerations hold for the other dividends, so that we can write the following function
long long count_divisors(long long n)
{
auto sum{ n };
for (auto i{ 1ll }, k_old{ n }, k{ n }; i < k ; ++i, k_old = k)
{ // ^^^^^ it goes up to sqrt(n)
k = n / (i + 1);
sum += (k_old - k) * i;
if (i == k)
break;
sum += k;
}
return sum;
}
Here it is tested against the O(N) algorithm, the only difference in the results beeing the corner cases n = 0 and n = 1.
Edit
Thanks again to largest_prime_is_463035818, who linked the Wikipedia page about the divisor summatory function, where both an O(N) and an O(sqrt(N)) algorithm are mentioned.
An implementation of the latter may look like this
auto divisor_summatory(long long n)
{
auto sum{ 0ll };
auto k{ 1ll };
for ( ; k <= n / k; ++k )
{
sum += n / k;
}
--k;
return 2 * sum - k * k;
}
They also add this statement:
Finding a closed form for this summed expression seems to be beyond the techniques available, but it is possible to give approximations. The leading behavior of the series is given by
D(x) = xlogx + x(2γ - 1) + Δ(x)
where γ is the Euler–Mascheroni constant, and the error term is Δ(x) = O(sqrt(x)).
I used your brute force approach as reference to have test cases. The ones I used are
compute(12) == 35
cpmpute(100) == 482
Don't get confused by computing factorizations. There are some tricks one can play when factorizing numbers, but you actually don't need any of that. The solution is a plain simple O(N) loop:
#include <iostream>
#include <limits>
long long compute(long long n){
long long sum = n+1;
for (long long i=2; i < n ; ++i){
sum += n/i;
}
return sum;
}
int main()
{
std::cout << compute(12) << "\n";
std::cout << compute(100) << "\n";
}
Output:
35
482
Why does this work?
The key is in Marc Glisse's comment:
As often with this kind of problem, this sum actually counts pairs x,
y where x divides y, and the sum is arranged to count first all x
corresponding to a fixed y, but nothing says you have to keep it that
way.
I could stop here, because the comment already explains it all. Though, if it didn't click yet...
The trick is to realize that it is much simpler to count divisors of all numbers up to n rather than n-times counting divisors of individual numbers and take the sum.
You don't need to care about factorizations of eg 123123123 or 52323423 to count all divisors up to 10000000000. All you need is a change of perspective. Instead of trying to factorize numbers, consider the divisors. How often does the divisor 1 appear up to n? Simple: n-times. How often does the divisor 2 appear? Still simple: n/2 times, because every second number is divisible by 2. Divisor 3? Every 3rd number is divisible by 3. I hope you can see the pattern already.
You could even reduce the loop to only loop till n/2, because bigger numbers obviously appear only once as divisor. Though I didn't bother to go further, because the biggest change is from your O(N * sqrt(N)) to O(N).
Let's start off with some math and reduce the O(n * sq(n)) factorization to O(n * log(log(n))) and for counting the sum of divisors the overall complexity is O(n * log(log(n)) + n * n^(1/3)).
For instance:
In Codeforces himanshujaju explains how we can optimize the solution of finding divisors of a number.
I am simplifying it a little bit.
Let, n as the product of three numbers p, q, and r.
so assume p * q * r = n, where p <= q <= r.
The maximum value of p = n^(1/3).
Now we can loop over all prime numbers in a range [2, n^(1/3)]
and try to reduce the time complexity of prime factorization.
We will split our number n into two numbers x and y => x * y = n.
And x contains prime factors up to n^(1/3) and y deals with higher prime factors greater than n^(1/3).
Thus gcd(x, y) = 1.
Now define F(n) as the number of prime factors of n.
From multiplicative rules, we can say that
F(x * y) = F(x) * F(y), if gcd(x, y) = 1.
For finding F(n) => F(x * y) = F(x) * F(y)
So first find F(x) then F(y) will F(n/x)
And there will 3 cases to cover for y:
1. y is a prime number: F(y) = 2.
2. y is the square of a prime number: F(y) = 3.
3. y is a product of two distinct prime numbers: F(y) = 4.
So once we are done with finding F(x) and F(y), we are also done with finding F(x * y) or F(n).
In Cp-Algorithm there is also a nice explanation of how to count the number of divisors on a number. And also in GeeksForGeeks a nice coding example of how to count the number of divisors of a number in an efficient way. One can check the articles and can generate a nice solution to this problem.
C++ implementation
#include <bits/stdc++.h>
using namespace std;
const int maxn = 1e6 + 11;
bool prime[maxn];
bool primesquare[maxn];
int table[maxn]; // for storing primes
void SieveOfEratosthenes()
{
for(int i = 2; i < maxn; i++){
prime[i] = true;
}
for(int i = 0; i < maxn; i++){
primesquare[i] = false;
}
// 1 is not a prime number
prime[1] = false;
for(int p = 2; p * p < maxn; p++){
// If prime[p] is not changed, then
// it is a prime
if(prime[p] == true){
// Update all multiples of p
for(int i = p * 2; i < maxn; i += p){
prime[i] = false;
}
}
}
int j = 0;
for(int p = 2; p < maxn; p++) {
if (prime[p]) {
// Storing primes in an array
table[j] = p;
// Update value in primesquare[p * p],
// if p is prime.
if(p < maxn / p) primesquare[p * p] = true;
j++;
}
}
}
// Function to count divisors
int countDivisors(int n)
{
// If number is 1, then it will have only 1
// as a factor. So, total factors will be 1.
if (n == 1)
return 1;
// ans will contain total number of distinct
// divisors
int ans = 1;
// Loop for counting factors of n
for(int i = 0;; i++){
// table[i] is not less than cube root n
if(table[i] * table[i] * table[i] > n)
break;
// Calculating power of table[i] in n.
int cnt = 1; // cnt is power of prime table[i] in n.
while (n % table[i] == 0){ // if table[i] is a factor of n
n = n / table[i];
cnt = cnt + 1; // incrementing power
}
// Calculating the number of divisors
// If n = a^p * b^q then total divisors of n
// are (p+1)*(q+1)
ans = ans * cnt;
}
// if table[i] is greater than cube root of n
// First case
if (prime[n])
ans = ans * 2;
// Second case
else if (primesquare[n])
ans = ans * 3;
// Third case
else if (n != 1)
ans = ans * 4;
return ans; // Total divisors
}
int main()
{
SieveOfEratosthenes();
int sum = 0;
int n = 5;
for(int i = 1; i <= n; i++){
sum += countDivisors(i);
}
cout << sum << endl;
return 0;
}
Output
n = 4 => 8
n = 5 => 10
Complexity
Time complexity: O(n * log(log(n)) + n * n^(1/3))
Space complexity: O(n)
Thanks, #largest_prime_is_463035818 for pointing out my mistake.

Max Sum Subarray with Partition constraint using Dynamic Programming

Problem statement: Given a set of n coins of some denominations (maybe repeating, in random order), and a number k. A game is being played by a single player in the following manner: Player can choose to pick 0 to k coins contiguously but will have to leave one next coin from picking. In this manner give the highest sum of coins he/she can collect.
Input:
First line contains 2 space-separated integers n and x respectively, which denote
n - Size of the array
x - Window size
Output:
A single integer denoting the max sum the player can obtain.
Working Soln Link: Ideone
long long solve(int n, int x) {
if (n == 0) return 0;
long long total = accumulate(arr + 1, arr + n + 1, 0ll);
if (x >= n) return total;
multiset<long long> dp_x;
for (int i = 1; i <= x + 1; i++) {
dp[i] = arr[i];
dp_x.insert(dp[i]);
}
for (int i = x + 2; i <= n; i++) {
dp[i] = arr[i] + *dp_x.begin();
dp_x.erase(dp_x.find(dp[i - x - 1]));
dp_x.insert(dp[i]);
}
long long ans = total;
for (int i = n - x; i <= n; i++) {
ans = min(ans, dp[i]);
}
return total - ans;
}
Can someone kindly explain how this code is working i.e., how line no. 12-26 in the Ideone solution is producing the correct answer?
I have dry run the code using pen and paper and found that it's giving the correct answer but couldn't figure out the algorithm used(if any). Can someone kindly explain to me how Line No. 12-26 is producing the correct answer? Is there any technique or algorithm at use here?
I am new to DP, so if someone can point out a tutorial(YouTube video, etc) related to this kind of problem, that would be great too. Thank you.
It looks like the idea is converting the problem - You must choose at least one coin in no more than x+1 coins in a row, and make it minimal. Then the original problem's answer would just be [sum of all values] - [answer of the new problem].
Then we're ready to talk about dynamic programming. Let's define a recurrence relation for f(i) which means "the partial answer of the new problem considering 1st to i-th coins, and i-th coin is chosen". (Sorry about the bad description, edits welcome)
f(i) = a(i) : if (i<=x+1)
f(i) = a(i) + min(f(i-1),f(i-2),...,f(i-x-1)) : otherwise
where a(i) is the i-th coin value
I added some comments line by line.
// NOTE f() is dp[] and a() is arr[]
long long solve(int n, int x) {
if (n == 0) return 0;
long long total = accumulate(arr + 1, arr + n + 1, 0ll); // get the sum
if (x >= n) return total;
multiset<long long> dp_x; // A min-heap (with fast random access)
for (int i = 1; i <= x + 1; i++) { // For 1 to (x+1)th,
dp[i] = arr[i]; // f(i) = a(i)
dp_x.insert(dp[i]); // Push the value to the heap
}
for (int i = x + 2; i <= n; i++) { // For the rest,
dp[i] = arr[i] + *dp_x.begin(); // f(i) = a(i) + min(...)
dp_x.erase(dp_x.find(dp[i - x - 1])); // Erase the oldest one from the heap
dp_x.insert(dp[i]); // Push the value to the heap, so it keeps the latest x+1 elements
}
long long ans = total;
for (int i = n - x; i <= n; i++) { // Find minimum of dp[] (among candidate answers)
ans = min(ans, dp[i]);
}
return total - ans;
}
Please also note that multiset is used as a min-heap. However we also need quick random-access(to erase the old ones) and multiset can do it in logarithmic time. So, the overall time complexity is O(n log x).

How to solve this problem through dynamic programming approach?

I was solving a very basic problem on arrays. The problem statement goes like this:
A basketball court has been opened in SIS, so Demid has decided to hold a basketball exercise session. 2⋅n students have come to Demid's exercise session, and he lined up them into two rows of the same size (there are exactly n people in each row). Students are numbered from 1 to n in each row in order from left to right.
Now Demid wants to choose a team to play basketball. He will choose players from left to right, and the index of each chosen player (excluding the first one taken) will be strictly greater than the index of the previously chosen player. To avoid giving preference to one of the rows, Demid chooses students in such a way that no consecutive chosen students belong to the same row. The first student can be chosen among all 2n students (there are no additional constraints), and a team can consist of any number of students.
Demid thinks, that in order to compose a perfect team, he should choose students in such a way, that the total height of all chosen students is maximum possible. Help Demid to find the maximum possible total height of players in a team he can choose.
For example if the input is:(First line contains the value of n, next 2 rows follow which contains the height of the students)
5
9 3 5 7 3
5 8 1 4 5
My approach was:
#These are global variables and functions.
int arr1[n],arr2[n],sum=0,max=0;
void func1(i)
{
if(i==n)
{
if(sum>max)
max=sum;
return;
}
sum+=arr1[i];
for(k=i+1;k<n;k++)
func2(k);
}
void func2(i)
{
if(i==n)
{
if(sum>max)
max=sum;
return;
}
sum+=arr2[i];
for(k=i+1;k<n;k++)
func1(k);
}
#Caller module. In main
for(i=0;i<n;i++)
{
sum=0;
func1(i);
}
This is my algorithm based on my logical reasoning. I have not coded it yet, will code it later. So feel free to point out any logical errors in the code.
I know this can be solved easily using dynamic programming approach and this algorithm is not quite it. How will the functions look like in that case?
As far as I can point out, the problem in this algorithm is I need to declare arr1 and arr2 globally whereas I get to know the value of n in the main function.
Dynamic programming would be quite straightforward here. We have two choices: Choose from A or skip, Choose from B or skip. Our bottom-up recurrence could look like:
// Choose from A or skip
m[i][0] = max(A[i] + m[i - 1][1], m[i - 1][0])
// Choose from B or skip
m[i][1] = max(B[i] + m[i - 1][0], m[i - 1][1])
JavaScript code:
function f(A, B){
let m = new Array(A.length + 1)
for (let i=0; i<=A.length; i++)
// [a or skip, b or skip]
m[i] = [0, 0]
for (let i=1; i<=A.length; i++){
// Choose from A or skip
m[i][0] = Math.max(
A[i-1] + m[i - 1][1], m[i - 1][0])
// Choose from B or skip
m[i][1] = Math.max(
B[i-1] + m[i - 1][0], m[i - 1][1])
}
return Math.max(...m[A.length])
}
var a = [9, 3, 5, 7, 3]
var b = [5, 8, 1, 4, 5]
console.log(f(a, b))
We can define 2 functions A and B. A(i) is the maximum height we can get by next choosing player with index i or greater from the first row. B(i) is the same for the second row. Now we can write A in terms of B and B in terms of A. For example A(i) is the max over all indices k that are i or greater by choosing the k'th element from the first set plus the max we can get by choosing from k+1 or higher from the second. B(i) is symmetric:
A(i) = max_{k=i..n} a[k] + B(k + 1); A(n) = a[n]
B(i) = max_{k=i..n} b[k] + A(k + 1); B(n) = b[n]
The answer will be max(A(1), B(1)).
A simple way to go is just code this as it's written with 2 memoized functions. I'll use C rather than C++ with tweaking to use 0-based indices.
#include <stdio.h>
#define N 5
int a[] = {9, 3, 5, 7, 3};
int b[] = {5, 8, 1, 4, 5};
int Avals[N], Bvals[N];
int B(int i);
int A(int i) {
if (i >= N) return 0;
if (Avals[i]) return Avals[i];
int max = 0;
for (int k = i; k < N; ++k) {
int val = a[k] + B(k + 1);
if (val > max) max = val;
}
return Avals[i] = max;
}
int B(int i) {
if (i >= N) return 0;
if (Bvals[i]) return Bvals[i];
int max = 0;
for (int k = i; k < N; ++k) {
int val = b[k] + A(k + 1);
if (val > max) max = val;
}
return Bvals[i] = max;
}
int main(void) {
int aMax = A(0);
int bMax = B(0);
printf("%d\n", aMax > bMax ? aMax : bMax);
return 0;
}
I claim there's a way to replace the memoized recursion with simple loops that access the elements of Avals and Bvals in strictly decreasing index order, but I'll let you figure out the details. This result will be smaller, faster code.

Remove repeated elements and give a Range Sum

I have a question regarding already asked this question:
SPOJ DQUERY : TLE Even With BIT?
What if I would like to not consider the repeated element to count when I make a range query?
following is an example:
Input
Line 1: n (1 ≤ n ≤ 10^6).
Line 2: n numbers a1, a2, ..., an (-10^9 ≤ ai ≤ ).
Line 3: q (1 ≤ q ≤ 10^6), the number of d-queries.
In the next q lines, each line contains 2 numbers i, j
representing a d-query (1 ≤ i ≤ j ≤ n).
Output
For each d-query (i, j), print the number of distinct elements in the
subsequence ai, ai+1, ..., aj in a single line.
Example
Input
9
1 2 3 2 4 1 2 3 4
3
1 9
2 4
5 9
2 7
Output
0 //Explanation: all elements have been repeated.
1 //Explanation: only 3 has not repeated.
3 //Explanation: only 4 is repeated, so count only 1, 2 and 3.
3 //Explanation: only 2 is repeated, so count only 3, 4 and 1.
what could it be the necessary changes which should have done in #kraskevich 's answer(which is an efficient solution for that particular case)? I tried to add 0 in BIT, instead of -1 in above-mentioned solution, which does not help for all types of queries. Can anybody get me an idea?
I finally made it using MOs Algorithm. Following will work as expected.
/** For the given set of queries find the Unique element
* count of an array using MO's Algorithum. */
#include <iostream>
#include <vector>
#include <algorithm>
#include <iterator>
#include <unordered_map>
using namespace std;
struct Query // struct for storing the queries
{
int Left;
int Right;
int Index;
};
inline void Add(const int i, int &ans, vector<int> &Arr, vector<int> &countArray)
{
++countArray[Arr[i]];
if(countArray[Arr[i]] == 1)
ans += countArray[Arr[i]];
if(countArray[Arr[i]] == 2)
ans -= countArray[Arr[i]];
}
inline void Remove(const int i, int &ans, vector<int> &Arr, vector<int> &countArray)
{
--countArray[Arr[i]];
if(countArray[Arr[i]] == 1)
ans += countArray[Arr[i]];
if(countArray[Arr[i]] == 0)
ans -= countArray[Arr[i]];
}
int main()
{
int _size; cin >> _size;
vector<int> Arr; Arr.reserve(_size);
copy_n(istream_iterator<int>(cin), _size, back_inserter(Arr));
//copy(Arr.cbegin(), Arr.cend(), ostream_iterator<int>(cout, "\t"));
int id = -1;
int sqrt_n = sqrt(_size);
int Q; cin >> Q;
vector<Query> qArr(Q);
unordered_map<int, int> Map;
for (int i = 0; i < _size; ++i)
{
if (Map.count(Arr[i]) == 0)
Map[Arr[i]] = ++id;
Arr[i] = Map[Arr[i]];
}
// read queries
for (int i = 0; i < Q; ++i)
{
int L,R;
cin >> L >> R;
qArr[i].Left = L-1;
qArr[i].Right = R-1;
qArr[i].Index = i;
}
// sort the queries according to(MO's Algorithum)
sort(qArr.begin(),qArr.end(),
[&](const Query &lhs, const Query &rhs)->bool
{
return ( (lhs.Left/sqrt_n == rhs.Left/sqrt_n) ?
lhs.Right < rhs.Right: // Qs with same Left case
(lhs.Left / sqrt_n) < (rhs.Left / sqrt_n) ); // Qs with diff values case
});
int currStart = 0;
int currEnd = 0;
int tempAnswer= 0;
vector<int> Answer(Q);
vector<int> countArray(_size);
for (int i = 0; i < Q; ++i)
{
int L = qArr[i].Left;
int R = qArr[i].Right;
/** Remove extra elements of previous range. For
* example if previous range is [0, 3] and current
* range is [2, 5], then a[0] and a[1] are subtracted */
while (currStart < L)
{
Remove(currStart, tempAnswer, Arr, countArray);
++currStart;
}
/** Add Elements of current Range */
while (currStart > L)
{
Add(currStart - 1, tempAnswer, Arr, countArray);
--currStart;
}
while (currEnd <= R)
{
Add(currEnd, tempAnswer, Arr, countArray);
++currEnd;
}
/** Remove elements of previous range. For example
* when previous range is [0, 10] and current range
* is [3, 8], then a[9] and a[10] are subtracted */
while (currEnd > R + 1)
{
Remove(currEnd - 1, tempAnswer, Arr, countArray);
--currEnd;
}
Answer[qArr[i].Index] = tempAnswer;
}
for(const auto &it: Answer) cout<<it<<endl;
return 0;
}

Dynamic Programing : Rod cutting implementation error

Given a rod of length n inches and a table of prices pi for
i = 1, 2,... n, determine the maximum revenue rn obtainable by cutting up
the rod and selling the pieces.
Bottom_Up_Cut_Rod(p, n)
1 let r[0...n] be a new array
2 r[0] = 0
3 for j = 1 to n
4 q = -infinity
5 for i = 1 to j
6 q = max(q; p[i] + r[j - i])
7 r[j] = q
8 return r[n]
Implementation
#include <iostream>
#include <algorithm>
using namespace std;
int RodCut(long long P[],long long n)
{
long long r[n];
r[0]=0;
for(long long j=0;j<n;j++)
{
long long q = -100000;
for(long long i=0;i<j;i++)
{
q = max(q , P[i] + r[j-i]);
}
r[j] = q;
}
return r[n];
}
int main()
{
long long num;
long long N;
long long K;
cin>>N;
long long a[N];
for (long long i = 0; i < N; i++)
{
cin>>num;
a[i] = num;
}
int res = 0;
res = RodCut(a,N);
cout<<"Answer : "<<res;
return 0;
}
My input is 1 5 8 9 10 17 17 20 24 30, but output is 2686348.
What is wrong with my code?
There are several issues. You want the main loop to go from j = 1 to n, because it represents the best you can do using j elements.
You should stick to using either ints or long longs.
int r[n+1];
r[0]=0;
// Calculate best we can do with j elements
for(int j=1;j<=n;j++) {
int q = -100000;
for(int i=0;i<j;i++) {
q = max(q , P[i] + r[j-i-1]);
}
r[j] = q;
}
return r[n];
This seems to give me the right solution for a variety of inputs.
There are two things. One is returning r[n], which should be r[n-1]. Second, start j from 1 to n, since r[0] is getting replaced with -100000 in the first round.
Also, r[0] should be P[0]; i.e. you'll atleast make P[0] money given a rod with length 1.
Also, note that q should be P[j], thats the minimum you'll make.
So assuming the array is P[0..n] // not including n
and r[0..n] is your memo for applying DP
foreach index from (0..n] // not including n
r[index] = P[index]
foreach splitIndex from (0..index] // not including index
r[index] = max(r[index], P[splitIndex] + r[index-splitIndex-1]
return r[n-1]