I have come across a problem where we want to tell the maximum size of the longest increasing sub-sequence.
an array A consisting of N integers.
M queries (Li, Ri)
for each query we wants to find the length of the longest increasing subsequence in
array A[Li], A[Li + 1], ..., A[Ri].
I implemented finding the sub-sequence using dp approach
// mind the REPN, LLD, these are macros I use for programming
// LLD = long long int
// REPN(i, a, b) = for (int i = a; i < b; ++i)
LLD a[n], dp[n];
REPN(i, 0, n)
{
scanf("%lld", &a[i]);
dp[i] = 1;
}
REPN(i, 1, n)
{
REPN(j, 0, i)
{
if(a[i] > a[j])
dp[i] = std::max(dp[j] + 1, dp[i]);
}
}
For example:
Array: 1 3 8 9 7 2 4 5 10 6
dplis: 1 2 3 4 3 1 3 4 5 5
max: 5
But if it was for range Li=2 & Ri=9
Then:
Array: 3 8 9 7 2 4 5 10
dplis: 1 2 3 2 1 2 3 4
max: 4
How can i determine the maximum longest increasing sub-sequence in a sub array?
PS: I don't want to recompute the whole dplis array, I want to use the original one because too much computation will kill the purpose of the question.
One of the approaches was to construct a complete 2D DP array that consists of sub-sequence from position i where range of i is from 0 to n, but it fails on many cases due to TLE(Time limit exceeded)
REPN(k,0,n) {
REPN(i,k+1,n) {
REPN(j,k,i) {
if(a[i]>a[j]) dp[k][i]=std::max(dp[k][j]+1, dp[k][i]);
}
}
}
REPN(i,0,q) {
read(l); read(r);
LLD max=-1;
REPN(i,0,r) {
if(max<dp[l-1][i]) max=dp[l-1][i];
}
printf("%lld\n", max);
}
If you have any new logic/implementation, I will gladly study it in-depth. Cheers.
Related
Here is the link to the question. Essentially, it asks to find the kth number having digit sum as 10. I have tried multiple solutions and also looked upon solutions online. Specifically this one (also shared below). The one with constant time talks about outliers in Arithmetic Progression and uses it to find the nth number having sum as 10. Obviously, the code is incorrect as it fails for test cases when k=1000 etc.
#include <bits/stdc++.h>
using namespace std;
int findNth(int n)
{
int nthElement = 19 + (n - 1) * 9;
int outliersCount = (int)log10(nthElement) - 1;
// find the nth perfect number
nthElement += 9 * outliersCount;
return nthElement;
}
int main()
{
cout << findNth(5) << endl;
return 0;
}
Eventually, I ended up writing combination of Arithmetic Progression + brute force as below
#include <bits/stdc++.h>
using namespace std;
#define ll unsigned long long
int main() {
int n;
cin >> n;
int count = 0;
ll i = 19;
for (; ; i += 9) {
int curr = i;
int localSum = 0;
while (curr) {
localSum += curr%10;
curr /= 10;
}
if (localSum == 10) {
count += 1;
}
if (count == n) {
break;
}
}
cout << i << endl;
return 0;
}
I am wondering, if there is no constant time or better algorithm that does not require me to calculate the sum, but my algorithm always hops in a way that I have number whose digit sum is 10?
Here is a Python solution that you can translate into C++.
cached_count_ds_l = {}
def count_digit_sum_length (s, l):
k = (s, l)
if k not in cached_count_ds_l:
if l < 2:
if s == 0:
return 1
elif l == 1 and s < 10:
return 1
else:
return 0
else:
ans = 0
for i in range(min(10, s+1)):
ans += count_digit_sum_length(s-i, l-1)
cached_count_ds_l[k] = ans
return cached_count_ds_l[k]
def nth_of_sum (s, n):
l = 0
while count_digit_sum_length(s, l) < n:
l += 1
digits = []
while 0 < l:
for i in range(10):
if count_digit_sum_length(s-i, l-1) < n:
n -= count_digit_sum_length(s-i, l-1)
else:
digits.append(str(i))
s -= i
l -= 1
break
return int("".join(digits))
print(nth_of_sum(10, 1000))
The idea is to use dynamic programming to find how many numbers there are of a given maximum length with a given digit sum. And then to use that to cross off whole blocks of numbers on the way to finding the right one.
The main logic goes like this:
0 numbers of length 0 sum to 10
- need longer
0 numbers of length 1 sum to 10
- need longer
9 numbers of length 2 sum to 10
- need longer
63 numbers of length 3 sum to 10
- need longer
282 numbers of length 4 sum to 10
- need longer
996 numbers of length 5 sum to 10
- need longer
2997 numbers of length 6 sum to 10
- answer has length 6
Looking for 1000th number of length 6 that sums to 10
- 996 with a leading 0 sum to 10
- Need the 4th past 99999
- 715 with a leading 1 sum to 10
- Have a leading 1
Looking for 4th number of length 5 that sums to 9
- 495 with a leading 0 sum to 9
- Have a leading 10
Looking for 4th number of length 4 that sums to 9
- 220 with a leading 0 sum to 9
- Have a leading 100
Looking for 4th number of length 3 that sums to 9
- 55 with a leading 0 sum to 9
- Have a leading 1000
Looking for 4th number of length 2 that sums to 9
- 1 with a leading 0 sum to 9
- Need the 3rd past 9
- 1 with a leading 1 sum to 9
- Need the 2nd past 19
- 1 with a leading 2 sum to 9
- Need the 1st past 29
- 1 with a leading 3 sum to 9
- Have a leading 10003
Looking for 1st number of length 1 that sums to 6
- 0 with a leading 0 sum to 6
- Need the 1st past 0
- 0 with a leading 1 sum to 6
- Need the 1st past 1
- 0 with a leading 2 sum to 6
- Need the 1st past 2
- 0 with a leading 3 sum to 6
- Need the 1st past 3
- 0 with a leading 4 sum to 6
- Need the 1st past 4
- 0 with a leading 5 sum to 6
- Need the 1st past 5
- 1 with a leading 6 sum to 6
- Have a leading 100036
And it finishes in a fraction of a second.
Incidentally the million'th is 20111220000010, the billionth is 10111000000002000000010000002100, and the trillionth is 10000000100000100000100000000000001000000000000100000000010110001000.
OK I am trying to make Prima algorithm so i need my edges array sorted, I tried to use quicksort here but it didn't work as I planned.
#include <iostream>
using namespace std;
void Sort (int arr[100][4], int m, int l) {
int i,j,x,v;
i=m;
j=l;
x=(i+j)/2;
do
{
while (((arr[i][3]<arr[x][3]))and(i<=l)) i++;
while (((arr[j][3]>arr[x][3]))and(j>=m)) j--;
if (i<=j)
{
v=arr[i][1];
arr[i][1]=arr[j][1];
arr[j][1]=v;
v=arr[i][2];
arr[i][2]=arr[j][2];
arr[j][2]=v;
v=arr[i][3];
arr[i][3]=arr[j][3];
arr[j][3]=v;
i++;
j--;
}
}
while (i<=j);
if (i<l) Sort(arr,i,l);
if (m<j) Sort(arr,m,j);
}
int main () {
int i,x,y,z,n,m;
int a[100][4];
fill(&a[0][0],&a[0][0]+400,0);
cout<<"Enter number of nodes and edges\n";
cin>>n>>m;
cout<<"Enter edges and their weights\n";
for (i=0;i<m;i++) {
cin>>x>>y>>z;
a[i][1]=min(x,y);
a[i][2]=max(x,y);
a[i][3]=z;
}
Sort (a,0,m-1);
for (i=0;i<m;i++) {
cout<<i+1<<") "<<a[i][1]<<' '<<a[i][2]<<' '<<a[i][3]<<endl;
}
return 0;
}
what I put is
5 10
1 2 4
1 3 7
4 1 5
5 1 8
2 3 3
2 4 6
2 5 6
3 4 8
3 5 2
4 5 4
what I get is
1) 3 5 2
2) 2 3 3
3) 1 4 5
4) 1 2 4
5) 4 5 4
6) 2 5 6
7) 2 4 6
8) 1 3 7
9) 1 5 8
10) 3 4 8
I don't understand why 5 is going ahead of 4's. Hope you could help.
You choose the pivot element in the middle of the (sub)array, which is fine, but you leave it in that position when you run the partitioning loop, and rely on it to stay there, which is not ok. With your approach, the pivot is likely to be swapped to a different position during the ordinary course of partitioning, after which the remainder of the partitioning will be based on the key swapped into the pivot's original position, which is likely to be different.
The usual approach is to start by swapping the pivot element to one end of the array or the other, partition the rest of the array, and then afterward swap the pivot into its correct position, as discovered via the partitioning process.
Change the code to use the pivot value instead of the pivot index, and some fixes to make it more like conventional Hoare partition scheme:
i=m-1;
j=l+1;
x=arr[(i+j)/2][3];
while(1)
{
while (arr[++i][3] < x);
while (arr[--j][3] > x);
if(i >= j)
return j;
// ...
Is there efficient way to downscale number of elements in array by decimal factor?
I want to downsize elements from one array by certain factor.
Example:
If I have 10 elements and need to scale down by factor 2.
1 2 3 4 5 6 7 8 9 10
scaled to
1.5 3.5 5.5 7.5 9.5
Grouping 2 by 2 and use arithmetic mean.
My problem is what if I need to downsize array with 10 elements to 6 elements? In theory I should group 1.6 elements and find their arithmetic mean, but how to do that?
Before suggesting a solution, let's define "downsize" in a more formal way. I would suggest this definition:
Downsizing starts with an array a[N] and produces an array b[M] such that the following is true:
M <= N - otherwise it would be upsizing, not downsizing
SUM(b) = (M/N) * SUM(a) - The sum is reduced proportionally to the number of elements
Elements of a participate in computation of b in the order of their occurrence in a
Let's consider your example of downsizing 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 to six elements. The total for your array is 55, so the total for the new array would be (6/10)*55 = 33. We can achieve this total in two steps:
Walk the array a totaling its elements until we've reached the integer part of N/M fraction (it must be an improper fraction by rule 1 above)
Let's say that a[i] was the last element of a that we could take as a whole in the current iteration. Take the fraction of a[i+1] equal to the fractional part of N/M
Continue to the next number starting with the remaining fraction of a[i+1]
Once you are done, your array b would contain M numbers totaling to SUM(a). Walk the array once more, and scale the result by N/M.
Here is how it works with your example:
b[0] = a[0] + (2/3)*a[1] = 2.33333
b[1] = (1/3)*a[1] + a[2] + (1/3)*a[3] = 5
b[2] = (2/3)*a[3] + a[4] = 7.66666
b[3] = a[5] + (2/3)*a[6] = 10.6666
b[4] = (1/3)*a[6] + a[7] + (1/3)*a[8] = 13.3333
b[5] = (2/3)*a[8] + a[9] = 16
--------
Total = 55
Scaling down by 6/10 produces the final result:
1.4 3 4.6 6.4 8 9.6 (Total = 33)
Here is a simple implementation in C++:
double need = ((double)a.size()) / b.size();
double have = 0;
size_t pos = 0;
for (size_t i = 0 ; i != a.size() ; i++) {
if (need >= have+1) {
b[pos] += a[i];
have++;
} else {
double frac = (need-have); // frac is less than 1 because of the "if" condition
b[pos++] += frac * a[i]; // frac of a[i] goes to current element of b
have = 1 - frac;
b[pos] += have * a[i]; // (1-frac) of a[i] goes to the next position of b
}
}
for (size_t i = 0 ; i != b.size() ; i++) {
b[i] /= need;
}
Demo.
You will need to resort to some form of interpolation, as the number of elements to average isn't integer.
You can consider computing the prefix sum of the array, i.e.
0 1 2 3 4 5 6 7 8 9
1 2 3 4 5 6 7 8 9 10
yields by summation
0 1 2 3 4 5 6 7 8 9
1 3 6 10 15 21 28 36 45 55
Then perform linear interpolation to get the intermediate values that you are lacking, like at 0*, 10/6, 20/6, 30/5*, 40/6, 50/6, 60/6*. (Those with an asterisk are readily available).
0 1 10/6 2 3 20/6 4 5 6 40/6 7 8 50/6 9
1 3 15/3 6 10 35/3 15 21 28 100/3 36 45 145/3 55
Now you get fractional sums by subtracting values in pairs. The first average is
(15/3-1)/(10/6) = 12/5
I can't think of anything in the C++ library that will crank out something like this, all fully cooked and ready to go.
So you'll have to, pretty much, roll up your sleeves and go to work. At this point, the question of what's the "efficient" way of doing it boils down to its very basics. Which means:
1) Calculate how big the output array should be. Based on the description of the issue, you should be able to make that calculation even before looking at the values in the input array. You know the input array's size(), you can calculate the size() of the destination array.
2) So, you resize() the destination array up front. Now, you no longer need to worry about the time wasted in growing the size of the dynamic output array, incrementally, as you go through the input array, making your calculations.
3) So what's left is the actual work: iterating over the input array, and calculating the downsized values.
auto b=input_array.begin();
auto e=input_array.end();
auto p=output_array.begin();
Don't see many other options here, besides brute force iteration and calculations. Iterate from b to e, getting your samples, calculating each downsized value, and saving the resulting value into *p++.
I am given this problem:
We are going over recursion in my class and I do not quite understand it, I was wondering if someone can help me with this problem
let c(n) be the number of different group integers that can be chosen from the integers 1 through n-1, so that the integers in each group add up to n (for example, n=4=[1+1+1+1]=[1+1+2]=[2+2]). Write a recursive definition for c(n) under the following variations:
a) You count permutations. For example, 1,2,1 and 1,1,2 are two groups that each add up to 4
b)you ignore permutations
I know permutations is how many ways you can arrange a set of numbers, so is my code below correct? I get an answer of 7?
Here is my code for part a:
int recurse (int n);
int main(){
int a=4;
int sum_perm;
sum_perm=recurse(a);
cout<<sum_perm-1<<endl;
//Can I do -1 here because it should be from a group of integers from 1 to n-1?
return 0;
}
int recurse(int n)
{
int sum = 1;
if (n == 1){
return 1;
}
for(int i = 1; i < n; i++){
sum += recurse(n - i);
}
return sum;
}
For part B, if I am not counting permutations, what am I counting?
Here is my code for part b:
int without (int n, int max);
int main(){
int a=4, b =3;
int sum_without;
sum_without=without(a,b);
cout<<sum_without<<endl;
system("Pause");
return 0;
}
int without(int n, int max)
{
if(n == 1 || max == 1){
return 1;
}
else if (n == max){
return 1 + without(n, n-1);
}
else{
return without(n,max-1) + without(n-max, max);
}
}
You don't show any code to generate the combinations of numbers that produce a sum. Link to wiki article about partitions .
In this case, the goal is to count the number of combinations and/or permutations, which might be possible without actually generating a set of combinations. Not sure if recursion helps here, but you can convert any loop into recursion if you pass enough variables.
Example "partitions"
1 combination that sums to 1:
1
2 combinations that sum to 2:
1 1
2
3 combinations that sum to 3:
1 1 1
1 2
3
5 combinations that sum to 4:
1 1 1 1
1 1 2
1 3
2 2
4
7 combinations that sum to 5:
1 1 1 1 1
1 1 1 2
1 1 3
1 2 2
1 4
2 3
5
11 combinations of numbers that sum to 6:
1 1 1 1 1 1
1 1 1 1 2
1 1 1 3
1 1 2 2
1 1 4
1 2 3
2 2 2
1 5
2 4
3 3
6
I would recommend combinations directly being considered. While it seems like the more difficult case a simple rule makes it trivial.
Numbers calculated are in decreasing order
This requires you to track the last number, but ensures you don't calculate 1 5 and then 5 1, as the former is impossible.
This question already exists:
Recursion all combinations of lower triangle C++
Closed 8 years ago.
I recently posted a poor question on how to use recursion to estimate all combinations of lower triangle in C++. I managed to find a recursive algorithm that given an array of size n, generates and prints all possible combinations of r elements in array. I've employed this function using Rcpp in R. I've then written a loop around this function to get all the subsets of combinations r to r + n.
#include <Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
int recursive(IntegerVector arr, IntegerVector data, int start, int end, int index, int r)
{
if (index == r)
{
for (int j=0; j<r; j++)
printf("%d ", data[j]);
printf("\n");
}
for (int i=start; i<=end && end-i+1 >= r-index; i++)
{
data[index] = arr[i];
recursive(arr, data, i+1, end, index+1, r);
}
}
R code with five groups:
Rcpp::sourceCpp('recursive2.cpp')
nComm <- 5
r <- c(2:nComm)
n <- nComm
arr <- c(1:nComm)
dat <- c(1:nComm)
for(i in 1:(n-1)){
recursive(arr, dat, 0, n-1, 0, r[i])
}
1 2
1 3
1 4
1 5
2 3
2 4
2 5
3 4
3 5
4 5
1 2 3
1 2 4
1 2 5
1 3 4
1 3 5
1 4 5
2 3 4
2 3 5
2 4 5
3 4 5
1 2 3 4
1 2 3 5
1 2 4 5
1 3 4 5
2 3 4 5
1 2 3 4 5
Currently, this just prints the subsets of combinations I need to estimate my dissimilarities. I'd like to be able to remove the loop and use it as a single Rcpp function/script. With the end goal to be able to use the subsets (currently printed combinations) as way to subset rows in an array. Which will be used to calculate the intersect between vectors. So 1 2 will be used to compare rows 1 and 2 in an array. And so forth.