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Here is the complete problem statement:
Given a rope of length n, you need to find the maximum number of pieces
you can make such that the length of every piece is in set {a, b, c} for
the given three values a, b, c
I know that the optimal solution can be achieved through Dynamic Programming, however, I have not learned that topic yet and I need to solve this problem recursively. With recursion, the main thing is to identify a subproblem and that's what I'm mainly having difficulty with doing. Can anyone give me an intuitive way to think of this problem? Sort of like a higher level description of the recursion if that makes sense. Is there an easier problem similar to this that I can try first that would help me solve this?
Thanks in advance.
It's already quite simple, with recursion we can just check all posibilities, in one step we can either cut away a piece of length a, b, or c so from problem of size n we get sup-problem of smaller size n-x
Of course we need a base case, so when n=0 we have succeeded so we can return 0, in case of n < 0 we have failed so we can return some negative infinity constant
Sample pseudo-code:
int solve(int n){
if(n < 0) return -123456789; //-Infinity
if(n == 0) return 0;
return 1 + max(solve(n-a), solve(n-b), solve(n-c));
}
going to dynamic programming is as simple as setting up memo lookup table
int solve(int n){
if(n < 0) return -123456789; //-Infinity
if(n == 0) return 0;
if(n in memo)return memo[n]
return memo[n] = 1 + max(solve(n-a), solve(n-b), solve(n-c));
}
int maxcut(int n, int a,int b,int c)
{
if(n==0) return 0;
if(n<0) return 1;
int result = max( maxcut(n-a,a,b,c), maxcut(n-b,a,b,c), maxcur(n-c,a,b,c));
if(res == -1) return -1;
return(result+1)
}
The way we should tackle the recursion problem is:
Finding the recursion case (Finding the subproblems)
Finding the base case (The last subproblem case we cannot break in subproblems)
Specific to this problem :
Recursion case: Cutting rope we all the possible values till we cannot break it further smaller subproblem.
Base case: a. It can be completely cut. (valid try)
b.It can't be completely cut. (invalid try)
int maxcut(int n, int a,int b,int c)
{
if(n==0) return 0; //base case a.
if(n<0) return -1; //base case b.
int result = max( maxcut(n-a,a,b,c), maxcut(n-b,a,b,c), maxcur(n-c,a,b,c)); //subproblems for all the cases
if(res == -1) return -1; // boundry coundtion
return(result+1); //to count the valid conditions and return to parent
}
here is the complete code for your problem
#include <iostream>
using namespace std;
int max(int a, int b, int c)
{
if (a > b)
{
if (a > c)
{
return a;
}
else
{
return c;
}
}
else
{
if (b > c)
{
return b;
}
else
{
return c;
}
}
}
int maxpiece(int l, int a, int b, int c)
{
int r;
if (l == 0)
{
return 0;
}
if (l<0)
{
return -1;
}
r = max(maxpiece(l-a, a, b, c), maxpiece(l-b, a, b, c), maxpiece(l-c, a, b, c));
if (r == -1)
return -1;
return r + 1;
}
int main()
{
int lenth;
cout << "enter rope lenth ";
cin >> lenth;
int p1, p2, p3;
cout << endl
<< "enter the only three parameters in which rope can be cut ";
cin >> p1 >> p2 >> p3;
cout << endl
<<"ans = "<< maxpiece(lenth, p1, p2, p3);
}
int cutRope(int n, int a, int b, int c){
// Base cases
if(n == 0) return 0;
if(n < 0) return -1;
int res = max(max(cutRope(n - a, a, b, c), cutRope(n - b, a, b, c)), cutRope(n - c, a, b, c));
if(res == -1) return -1;
return res + 1;
}
int main() {
cout << cutRope(23, 11, 9, 12) << endl;
return 0;
}
Let the Roots of a first degree polynomial( Q(x) ) be x0 = -b/a. Since the range of the variable a and b is large, x0 can be large as well for it to be stored in a variable(x0).
so, it is converted to some unique number using some operation with mod
int x0 = mul(mod - b, rev(a));
problem link: hackerank problem
Can someone please explain how this line of code works and the math behind this operation?
the whole code-
#include <bits/stdc++.h>
using namespace std;
#define forn(i,n) for (int i = 0; i < int(n); ++i)
typedef long long ll;
const int inf = int(1e9) + int(1e5);
const ll infl = ll(2e18) + ll(1e10);
const int mod = 1e9 + 7;
int udd(int &a, int b) {
a += b;
if (a >= mod)
a -= mod;
return a;
}
int add(int a, int b) {
return udd(a, b);
}
int mul(ll a, ll b) {
return a * b % mod;
}
//============didnt understand this step
int bin(int a, int d) {
int b = 1;
while (d) {
if (d & 1)
b = mul(b, a);
d >>= 1;
a = mul(a, a);
}
return b;
}
int rev(int a) {
assert(a != 0);
return bin(a, mod - 2);
}
const int maxn = 100100;
int px[maxn];
int c[maxn];
struct Fenwick {
int a[maxn];
int t[maxn];
void set(int pos, int val) {
int delta = add(val, mod - a[pos]);
a[pos] = val;
delta = mul(delta, px[pos]);
for (int i = pos; i < maxn; i |= i + 1) {
udd(t[i], delta);
}
}
int get(int r) {
int res = 0;
for (int i = r - 1; i >= 0; i = (i & (i + 1)) - 1)
udd(res, t[i]);
return res;
}
int get(int l, int r) {
return add(get(r), mod - get(l));
}
} fw;
int main() {
#ifdef LOCAL
assert(freopen("test.in", "r", stdin));
#endif
int n, a, b, q;
cin >> n >> a >> b >> q;
//========what does this line do?
int x0 = mul(mod - b, rev(a));
px[0] = 1;
for (int i = 1; i < n; ++i)
px[i] = mul(px[i - 1], x0);
forn (i, n) {
cin >> c[i];
fw.set(i, c[i]);
}
forn (i, q) {
int t, a, b;
cin >> t >> a >> b;
if (t == 1) {
fw.set(a, b);
} else {
++b;
int s = fw.get(a, b);
if (x0 == 0)
s = fw.a[a];
cout << (s == 0 ? "Yes" : "No") << '\n';
}
}
}
bin is the halving-and-squaring implementation for the (in this case modular) power function a^d % mod, so that the modular inverse in rev can be computed via the little theorem of Fermat.
can anyone provide the memoized approach for longest common substring between two strings.I know the bottom solution but I am not able to think in top-down manner.
Expected time complexity-O(n^2)
TOP-DOWN APPROACH
#include <iostream>
#include <algorithm>
#include <cstring>
using namespace std;
string X, Y; //input strings
int ans, dp[105][105]; // ans : answer
int LCS(int n, int m) //our function return value of (n,m) state
{ // so that we can use the result in (n+1,m+1) state
if(n == 0 || m == 0) return 0; //in case of match in (n+1,m+1) state
if(dp[n][m] != -1) return dp[n][m];
LCS(n-1,m); //to visit all n*m states (try example: X:ASDF
LCS(n,m-1); // we call these states first Y:ASDFF)
if(X[n-1] == Y[m-1])
{
dp[n][m] = LCS(n-1,m-1) + 1;
ans = max(ans, dp[n][m]);
return dp[n][m];
}
return dp[n][m] = 0;
}
int main()
{
int t; cin>>t;
while(t--)
{
int n, m; cin>>n>>m; //length of strings
cin>>X>>Y;
memset(dp, -1, sizeof dp);
ans = 0;
LCS(n, m);
cout<<ans<<'\n';
}
return 0;
}
Memoization with recursion works with top-down approach.
Taking LCS example using DP from Cormen into consideration below is the pseudo code describing how it will work.
MEMOIZED-LCS-LENGTH(X,Y)
m<-length[X]
n<-length[Y]
for(i<-1 to m)
do for(j<-1 to n)
c[i,j]<- -1
for(i<-1 to m)
c[i,0]<-0
for(j<-1 to n)
c[0,j]<-0
return RECURSIVE-LCS-LENGTH(X,Y,1,1)
RECURSIVE-LCS-LENGTH(X,Y,i,j)
if(c[i,j]!=-1)
return c[i,j]
//Above 2 line fetches the result if already present, instead of computing it again.
if(x[i]==y[j])
then c[i,j]<-RECURSIVE-LCS-LENGTH(X,Y,i+1,j+1)+1
else
c1<- RECURSIVE-LCS-LENGTH(X,Y,i+1,j)
c2<-RECURSIVE-LCS-LENGTH(X,Y,i,j+1)
if(c1<c2)
then c[i,j]<-c1
else c[i,j]<-c2
return c[i,j]
Java Solution:
class Solution {
public int findLength(int[] A, int[] B) {
int[][] cache = new int[A.length][B.length];
Arrays.stream(cache).forEach(a->Arrays.fill(a,-1));
int[] res = new int[1];
findLength(0, 0, A, B, cache, res);
return res[0];
}
public static int findLength(int a, int b, int[] A, int[] B, int[][] cache, int[] res){
if( a >= A.length || b >= B.length )
return 0;
if(cache[a][b] != -1){
return cache[a][b];
}
if(A[a] == B[b]){
cache[a][b] = 1 + findLength(a+1,b+1,A,B,cache,res);
// remember you can not return here: why? see case: s1 = 1,2,3 s2=1,4,1,2,3
}
// try out other possiblities and update cache
findLength(a+1,b,A,B,cache,res);
findLength(a,b+1,A,B,cache,res);
//you can avoid this and find max value at end in cache
res[0] = Math.max(res[0],cache[a][b]);
//at this point cache might have -1 or updated value, if its -1 make it to 0 as this location is visited and no common substring is there from here
cache[a][b] = Math.max(0,cache[a][b]);
return cache[a][b];
}
}
Recursion plus memoization in python. Please note this code is partially accepted on Hackerearth and Geeksforgeeks.For larger test cases, it is giving MLE.
import sys
sys.setrecursionlimit(1000000)
maxlen=0
t=None
def solve(s1, s2, n, m):
global maxlen, t
if n<=0 or m<=0:
return 0
if t[n][m]!=-1:
return t[n][m]
if s1[n-1]==s2[m-1]:
temp=1+solve(s1, s2, n-1, m-1)
maxlen=max(maxlen, temp)
t[n][m]=temp
return temp
t[n][m]=0
return 0
class Solution:
def longestCommonSubstr(self, S1, S2, n, m):
global maxlen, t
maxlen=0
t=[[-1]*(m+1) for i in range(n+1)]
for i in range(n+1):
for j in range(m+1):
solve(S1, S2, i, j)
return maxlen
if __name__=='__main__':
S1=input().strip()
S2=input().strip()
n=len(S1)
m=len(S2)
ob = Solution()
print(ob.longestCommonSubstr(S1, S2, n, m))
An easy solution is described below. Here memo[n][m] does not store the length of
greatest substring but you can store the greatest substring in pointer maxi as follows:
#include<iostream>
#include<string>
using namespace std;
int lcs(string X,string Y,int n,int m,int *maxi,int memo[][8]) {
if(n==0||m==0) {
return 0;
}
int k=0;
int j=0;
if(memo[n-1][m-1]!=-1) {
return memo[n-1][m-1];
}
if(X[n-1]==Y[m-1]) {
memo[n-1][m-1] = 1+lcs(X,Y,n-1,m-1,maxi,memo);
if(*maxi<memo[n-1][m-1])
*maxi=memo[n-1][m-1];
}
else {
memo[n-1][m-1]=0;
}
int l = lcs(X,Y,n-1,m,maxi,memo);
int i = lcs(X,Y,n,m-1,maxi,memo);
return memo[n-1][m-1];
}
int main()
{
int n,m;
string X = "abcdxyze";
//string X = "abcd";
string Y = "xyzabcde";
n=X.length();
m=Y.length();
int memo[n][8];
for(int i=0;i<n;i++) {
for(int j=0;j<m;j++) {
memo[i][j]=-1;
}
}
int maxi=0;
int k = lcs(X,Y,n,m,&maxi,memo);
cout << maxi;
return 0;
}
class Solution {
public:
int t[1005][1005];
int maxC = 0;
int recur_memo(vector<int>& nums1, vector<int>& nums2, int m, int n) {
if(t[m][n] != -1)
return t[m][n];
if(m == 0 || n == 0)
return 0;
int max_substring_ending_here = 0;
//Example : "abcdezf" "abcdelf"
//You see that wowww, string1[m-1] = string2[n-1] = 'f' and you happily
go for (m-1, n-1)
//But you see, in future after a gap of 'l' and 'z', you will find
"abcde" and "abcde"
if(nums1[m-1] == nums2[n-1]) {
max_substring_ending_here = 1 + recur_memo(nums1, nums2, m-1, n-1);
}
//May be you find better results if you do (m-1, n) and you end up
updating maxC with some LAAARGEST COMMON SUBSTRING LENGTH
int decrease_m = recur_memo(nums1, nums2, m-1, n); //stage (m-1, n)
//OR,
//May be you find better results if you do (m, n-1) and you end up
updating maxC with some LAAARGEST COMMON SUBSTRING LENGTH
int decrease_n = recur_memo(nums1, nums2, m, n-1); //stage (m, n-1)
//Like I said, you need to keep on finding the maxC in every call you
make throughout your journey.
maxC = max({maxC, max_substring_ending_here, decrease_m, decrease_n});
//BUT BUT BUT, you need to return the best you found at this stage (m, n)
return t[m][n] = max_substring_ending_here;
}
int findLength(vector<int>& nums1, vector<int>& nums2) {
int m = nums1.size();
int n = nums2.size();
memset(t, -1, sizeof(t));
recur_memo(nums1, nums2, m, n); //resurive+memoization
return maxC;
}
};
Link : https://leetcode.com/problems/maximum-length-of-repeated-subarray/discuss/1169215/(1)-Recursive%2BMemo-(2)-Bottom-Up-(C%2B%2B)
Here is a recursive and top-down approach:
public int lcsSubstr(char[] s1, char[] s2, int m, int n, int c) {
if (m == 0 || n == 0) {
return c;
}
if (s1[m-1] == s2[n-1]) {
c = lcsSubstr(s1, s2, m-1, n-1, c+1);
} else {
c2 = Math.max(lcsSubstr(s1, s2, m, n - 1, 0), lcsSubstr(s1, s2, m-1, n, 0));
}
return Math.max(c, c2);
}
public int lcsSubstrMemo(char[] s1, char[] s2, int m, int n, int c, int[][] t) {
if(m == 0 || n == 0) {
return c;
}
if (t[m-1][n-1] != -1) return t[m-1][n-1];
if(s1[m - 1] == s2[n - 1]) {
c = lcsSubstr(s1, s2, m - 1, n - 1, c + 1);
} else {
c2 = Math.max(lcsSubstr(s1, s2, m, n - 1, 0), lcsSubstr(s1, s2, m - 1, n, 0));
}
t[m - 1][n - 1] = Math.max(c, c2);
return t[m-1][n-1];
}
Memoization refers to caching the solutions to subproblems in order to use them later. In the longest common subsequence problem, you try to match substrings of two subsequences to see if they match, maintaining in memory the longest one yet found. Here is the solution in Java you are looking for (memoized version of LCS):
public class LongestCommonSubsequence {
private static HashMap<Container, Integer> cache = new HashMap<>();
private static int count=0, total=0;
public static void main(String sargs[]){
Scanner scanner = new Scanner(System.in);
String x=scanner.nextLine();
String y=scanner.nextLine();
int max=0;
String longest="";
for(int j=0;j<x.length();j++){
String common=commonSubsequence(j,0, x, y);
if(max<common.length()){
max=common.length();
longest=common;
}
}
for(int j=0;j<y.length();j++){
String common=commonSubsequence(j,0, y, x);
if(max<common.length()){
max=common.length();
longest=common;
}
}
System.out.println(longest);
System.out.println("cache used "+count+" / "+total);
}
public static String commonSubsequence(final int startPositionX, int startPositionY, String x, String y){
StringBuilder commonSubsequence= new StringBuilder();
for(int i=startPositionX;i<x.length();i++){
Integer index=find(x.charAt(i),startPositionY,y);
if(index!=null){
commonSubsequence.append(x.charAt(i));
if(index!=y.length()-1)
startPositionY=index+1;
else
break;
}
}
return commonSubsequence.toString();
}
public static Integer find(char query, int startIndex, String target){
Integer pos=cache.get(new Container(query, startIndex));
total++;
if(pos!=null){
count++;
return pos;
}else{
for(int i=startIndex;i<target.length();i++){
if(target.charAt(i)==query){
cache.put(new Container(query, startIndex), i);
return i;
}
}
return null;
}
}
}
class Container{
private Character toMatch;
private Integer indexToStartMatch;
public Container(char t, int i){
toMatch=t;
indexToStartMatch=i;
}
#Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime
* result
+ ((indexToStartMatch == null) ? 0 : indexToStartMatch
.hashCode());
result = prime * result + ((toMatch == null) ? 0 : toMatch.hashCode());
return result;
}
#Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (getClass() != obj.getClass())
return false;
Container other = (Container) obj;
if (indexToStartMatch == null) {
if (other.indexToStartMatch != null)
return false;
} else if (!indexToStartMatch.equals(other.indexToStartMatch))
return false;
if (toMatch == null) {
if (other.toMatch != null)
return false;
} else if (!toMatch.equals(other.toMatch))
return false;
return true;
}
}
I recently participated in a competition where I was asked this question. Given an array with lengths what is the area of the biggest rectangle that can be made using ALL the lengths. The lengths can be added but not broken in between.
Example:
[ 4,2,4,4,6,8 ] given this array the best we can do is make a rectangle of sides 8 and 6 like this.
giving an area of 8 * 6 = 48.
I am a beginner and even after a long hard think about how to do it I am unable to get anywhere. I am not looking for a solution but any clue to nudge me in the right direction would be appreciated.
TIA
Edit: Somebody pointed out(comment deleted now) that its difficult to explain the solution with just hints and not posting some code. Kindly post code if necessary.
The problem is NP-Hard, thus the backtracking solution [or other exponential solution as suggested by #vhallac] will be your best shot, since there is not known [and if P!=NP, there is no existing] polynomial solution for this kind of problem.
NP-Hardness proof:
First, we know that a rectangle consists of 4 edges, that are equal in pairs [e1=e2,e3=e4].
We will show that if there is a polynomial algorithm A to this problem, we can also solve the Partition Problem, by the following algorithm:
input: a group of numbers S=a1,a2,...,an
output: true if and only if the numbers can be partitioned
algorithm:
sum <- a1 + a2 + .. + an
lengths <- a1, a2 , ... , an , (sum*5), (sum*5)
activate A with lengths.
if A answered there is any rectangle [solution is not 0], answer True
else answer False
Correctness:
(1) if there is a partition to S, let it be S1,S2, there is also a rectangle with edges: (sum*5),(sum*5),S1,S2, and the algorithm will yield True.
(2) if the algorithm yields True, there is a rectangle available in lengths, since a1 + a2 + ... + an < sum*5, there are 2 edges with length sum*5, since the 2 other edges must be made using all remaining lengths [as the question specified], each other edge is actually of length (a1 + a2 + ... + an)/2, and thus there is a legal partition to the problem.
Conclusion: There is a reduction PARTITION<=(p) this problem, and thus, this problem is NP-Hard
EDIT:
the backtracking solution is pretty simple, get all possible rectangles, and check each of them to see which is the best.
backtracking solution: pseudo-code:
getAllRectangles(S,e1,e2,e3,e4,sol):
if S == {}:
if legalRectangle(e1,e2,e3,e4):
sol.add((e1,e2,e3,e4))
else: //S is not empty
elem <- S[0]
getAllRectangles(S-elem,e1+elem,e2,e3,e4,sol)
getAllRectangles(S-elem,e1,e2+elem,e3,e4,sol)
getAllRectangles(S-elem,e1,e2,e3+elem,e4,sol)
getAllRectangles(S-elem,e1,e2,e3,e4+elem,sol)
getRectangle(S):
RECS <- new Set
getAllRectangles(S,{},{},{},{},RECS)
getBest(RECS)
EDIT2:
As discussed in the comments, this answer shows not only this is hard to find the BEST rectangle, it is also hard to find ANY rectangle, making this problem hard for heuristic solutions as well.
Here is one solution to the problem in Python. It is not optimized at all. I even check 2, 4 after I check 4,2, for example. But for showing how you can find a solution, I think it is good enough.
def all_but(lst, pos):
return lst[0:pos]+lst[pos+1:]
def find_sets_with_len(segs, l):
for i in range(0, len(segs)):
val = segs[i]
if (val == l):
yield [val], all_but(segs, i)
if (val < l):
for soln, rest in find_sets_with_len(all_but(segs, i), l - val):
yield [val]+soln, rest
def find_rect(segs, l1, l2):
for side1, rest1 in find_sets_with_len(segs, l1):
for side2, rest2 in find_sets_with_len(rest1, l1):
for side3, rest3 in find_sets_with_len(rest2, l2):
return [side1, side2, side3, rest3]
def make_rect(segs):
tot_len = sum(segs)
if (tot_len %2) == 0:
opt_len=tot_len/4
for l in range(opt_len, 0, -1):
sides = find_rect(segs, l, tot_len/2-l)
if sides is not None:
print(sides)
return sides
print("Can't find any solution")
make_rect([4,2,4,4,6,8])
The idea is simple: first, calculate the optimal length (that is, the length to make a square), then search everything starting off with the optimal length, and go down to 1 for one side. For each length, enumerate all sets for one side of the claculated length, then enumerate all sets for the opposite side (of the same length), then if I can find one more set of the remaining length (that is total_len/2 minus the side length I am looking at), then I've got the best solution. This happens in find_rect() function.
Well, I get little bit bored so play around with Java to have some experience, can be poorly coded and without tuning, as I am trying to increase my coding skill, comments are welcome. My computer able to answer me for small arrays:)
Output looks like:
Largest rectangle range is ; 48
-------------------------------------------------
input values; [ 4,2,4,4,6,8,9 ]
-------------------------------------------------
Array details of the rectangle:
A1: [ 6 ]
B1: [ 8 ]
A2: [ 2,4 ]
B2: [ 4,4 ]
combination.class using Kenneth algorithm;
import java.math.BigInteger;
public class Combination {
/**
* Burak
*/
private int[] a;
private int n;
private int r;
private BigInteger numLeft;
private BigInteger total;
public Combination (int n, int r) {
if (r > n) {
throw new IllegalArgumentException ();
}
if (n < 1) {
throw new IllegalArgumentException ();
}
this.n = n;
this.r = r;
a = new int[r];
BigInteger nFact = getFactorial (n);
BigInteger rFact = getFactorial (r);
BigInteger nminusrFact = getFactorial (n - r);
total = nFact.divide (rFact.multiply (nminusrFact));
reset ();
}
//------
// Reset
//------
public void reset () {
for (int i = 0; i < a.length; i++) {
a[i] = i;
}
numLeft = new BigInteger (total.toString ());
}
//------------------------------------------------
// Return number of combinations not yet generated
//------------------------------------------------
public BigInteger getNumLeft () {
return numLeft;
}
//-----------------------------
// Are there more combinations?
//-----------------------------
public boolean hasMore () {
return numLeft.compareTo (BigInteger.ZERO) == 1;
}
//------------------------------------
// Return total number of combinations
//------------------------------------
public BigInteger getTotal () {
return total;
}
//------------------
// Compute factorial
//------------------
private static BigInteger getFactorial (int n) {
BigInteger fact = BigInteger.ONE;
for (int i = n; i > 1; i--) {
fact = fact.multiply (new BigInteger (Integer.toString (i)));
}
return fact;
}
//--------------------------------------------------------
// Generate next combination (algorithm from Rosen p. 286)
//--------------------------------------------------------
public int[] getNext () {
if (numLeft.equals (total)) {
numLeft = numLeft.subtract (BigInteger.ONE);
return a;
}
int i = r - 1;
while (a[i] == n - r + i) {
i--;
}
a[i] = a[i] + 1;
for (int j = i + 1; j < r; j++) {
a[j] = a[i] + j - i;
}
numLeft = numLeft.subtract (BigInteger.ONE);
return a;
}
}
And main Combinator.class;
import java.util.*;
public class Combinator {
/**
* #param args
*/
private static int[] ad;
private static int[] bd;
private static String a1;
private static String a2;
private static String b1;
private static String b2;
private static int bestTotal =0;
public static void main(String[] args) {
int[] array={4,2,4,4,6,8,9};
getBestCombination(array, 1);
if(bestTotal <= 0){
System.out.println("System couldnt create any rectangle.");
}else{
System.out.println("Largest rectangle range is ; " + bestTotal);
System.out.println("-------------------------------------------------");
System.out.println("input values; " + parseArrayToString(array));
System.out.println("-------------------------------------------------");
System.out.println("Array details of the rectangle:");
System.out.println("A1: " + a1);
System.out.println("B1: " + b1);
System.out.println("A2: " + a2);
System.out.println("B2: " + b2);
}
}
private static void getBestCombination(int[] array, int level){
int[] a;
int[] b;
int bestPerimeter = getTotal(array,true);
Vector<Vector<Integer>> results = null;
for(int o=array.length-1;o>=1;o--){
for(int u=bestPerimeter;u>=1;u--){
results = Combinator.compute (array, o, u);
if(results.size() > 0){
for(int i=0;i<results.size();i++){
a = new int[results.elementAt(i).size()];
for(int j = 0;j<results.elementAt(i).size();j++){
a[j] = results.elementAt(i).elementAt(j);
}
b = removeItems(array, results.elementAt(i));
if(level == 1){
getBestCombination(a,2);
getBestCombination(b,3);
}else if(level == 2){
ad = a;
bd = b;
}else{
getBestCombination(a,4);
getBestCombination(b,4);
if(getTotal(ad, false) == getTotal(a, false) && getTotal(bd, false) == getTotal(b, false)){
if(bestTotal<(getTotal(ad, false)*getTotal(bd, false))){
bestTotal = getTotal(ad, false)*getTotal(bd, false);
a1 = parseArrayToString(ad);
a2 = parseArrayToString(a);
b1 = parseArrayToString(bd);
b2 = parseArrayToString(b);
}
}else if(getTotal(ad, false) == getTotal(b, false) && getTotal(bd, false) == getTotal(a, false)){
if(bestTotal<(getTotal(ad, false)*getTotal(bd, false))){
bestTotal = getTotal(ad, false)*getTotal(bd, false);
a1 = parseArrayToString(ad);
a2 = parseArrayToString(b);
b1 = parseArrayToString(bd);
b2 = parseArrayToString(a);
}
}
}
}
}
}
}
}
private static String parseArrayToString(int[] items){
String s = "[ ";
for(int i=0;i<items.length;i++){
if(i!=items.length-1){
s = s + items[i] + ",";
}else{
s = s + items[i];
}
}
s = s + " ]";
return s;
}
#SuppressWarnings("rawtypes")
private static int[] removeItems(int[] array, Vector items){
ArrayList<Integer> res = new ArrayList<Integer>();
for(int i=0;i<array.length;i++){
res.add(array[i]);
}
for(int u = 0;u<items.size();u++){
res.remove(items.elementAt(u));
}
int[] results = new int[res.size()];
for(int o=0;o<res.size();o++){
results[o] = res.get(o);
}
return results;
}
private static int getTotal(int[] array,boolean bestPerimeter){
int sum = 0;
for (int i = 0; i < array.length; i++) {
sum += array[i];
}
if(bestPerimeter == true){
if(sum%2!=0){
sum = sum -1;
}
sum = sum/2;
}
//System.out.println(sum);
return sum;
}
#SuppressWarnings("rawtypes")
private static int getSum (Vector v) {
int sum = 0;
Integer n;
for (int i = 0; i < v.size (); i++) {
n = (Integer) v.elementAt(i);
sum += n.intValue ();
}
return sum;
}
#SuppressWarnings({ "rawtypes", "unchecked" })
public static Vector<Vector<Integer>> compute (int[] array, int atATime, int desiredTotal) {
int[] indices;
Combination gen = new Combination (array.length, atATime);
Vector results = new Vector ();
Vector combination;
int sum;
Integer intObj;
while (gen.hasMore ()) {
combination = new Vector ();
indices = gen.getNext ();
for (int i = 0; i < indices.length; i++) {
intObj = new Integer (array[indices[i]]);
combination.addElement (intObj);
}
sum = getSum (combination);
if (sum == desiredTotal) {
Collections.sort (combination);
if (!results.contains (combination)) {
results.addElement (combination);
}
}
}
return results;
}
}
what is the fastest way to compute the greatest common divisor of n numbers?
Without recursion:
int result = numbers[0];
for(int i = 1; i < numbers.length; i++){
result = gcd(result, numbers[i]);
}
return result;
For very large arrays, it might be faster to use the fork-join pattern, where you split your array and calculate gcds in parallel. Here is some pseudocode:
int calculateGCD(int[] numbers){
if(numbers.length <= 2){
return gcd(numbers);
}
else {
INVOKE-IN-PARALLEL {
left = calculateGCD(extractLeftHalf(numbers));
right = calculateGCD(extractRightHalf(numbers));
}
return gcd(left,right);
}
}
You may want to sort the numbers first and compute the gcd recursively starting from the smallest two numbers.
C++17
I have written this function for calculating gcd of n numbers by using C++'s inbuilt __gcd(int a, int b) function.
int gcd(vector<int> vec, int vsize)
{
int gcd = vec[0];
for (int i = 1; i < vsize; i++)
{
gcd = __gcd(gcd, vec[i]);
}
return gcd;
}
To know more about this function visit this link .
Also refer to Dijkstra's GCD algorithm from the following link. It works without division. So it could be slightly faster (Please correct me if I am wrong.)
You should use Lehmer's GCD algorithm.
How about the following using Euclidean algorithm by subtraction:
function getGCD(arr){
let min = Math.min(...arr);
let max= Math.max(...arr);
if(min==max){
return min;
}else{
for(let i in arr){
if(arr[i]>min){
arr[i]=arr[i]-min;
}
}
return getGCD(arr);
}
}
console.log(getGCD([2,3,4,5,6]))
The above implementation takes O(n^2) time. There are improvements that can be implemented but I didn't get around trying these out for n numbers.
If you have a lot of small numbers, factorization may be actually faster.
//Java
int[] array = {60, 90, 45};
int gcd = 1;
outer: for (int d = 2; true; d += 1 + (d % 2)) {
boolean any = false;
do {
boolean all = true;
any = false;
boolean ready = true;
for (int i = 0; i < array.length; i++) {
ready &= (array[i] == 1);
if (array[i] % d == 0) {
any = true;
array[i] /= d;
} else all = false;
}
if (all) gcd *= d;
if (ready) break outer;
} while (any);
}
System.out.println(gcd);
(works for some examples, but not really tested)
Use the Euclidean algorithm :
function gcd(a, b)
while b ≠ 0
t := b;
b := a mod b;
a := t;
return a;
You apply it for the first two numbers, then the result with the third number, etc... :
read(a);
read(b);
result := gcd(a, b);
i := 3;
while(i <= n){
read(a)
result := gcd(result, a);
}
print(result);
Here below is the source code of the C program to find HCF of N numbers using Arrays.
#include<stdio.h>
int main()
{
int n,i,gcd;
printf("Enter how many no.s u want to find gcd : ");
scanf("%d",&n);
int arr[n];
printf("\nEnter your numbers below :- \n ");
for(i=0;i<n;i++)
{
printf("\nEnter your %d number = ",i+1);
scanf("%d",&arr[i]);
}
gcd=arr[0];
int j=1;
while(j<n)
{
if(arr[j]%gcd==0)
{
j++;
}
else
{
gcd=arr[j]%gcd;
i++;
}
}
printf("\nGCD of k no.s = %d ",gcd);
return 0;
}
For more refer to this website for further clarification.......
You can use divide and conquer. To calculate gcdN([]), you divide the list into first half and second half. if it only has one num for each list. you calculate using gcd2(n1, n2).
I just wrote a quick sample code. (assuming all num in the list are positive Ints)
def gcdN(nums):
n = len(nums)
if n == 0: return "ERROR"
if n == 1: return nums[0]
if n >= 2: return gcd2(gcdN(nums[:n//2]), gcdN(nums[n//2:]))
def gcd2(n1, n2):
for num in xrange(min(n1, n2), 0, -1):
if n1 % num == 0 and n2 % num == 0:
return num
Here's a gcd method that uses the property that gcd(a, b, c) = gcd(a, gcd(b, c)).
It uses BigInteger's gcd method since it is already optimized.
public static BigInteger gcd(BigInteger[] parts){
BigInteger gcd = parts[0];
for(int i = 1; i < parts.length; i++)
gcd = parts[i].gcd(gcd);
return gcd;
}
//Recursive solution to get the GCD of Two Numbers
long long int gcd(long long int a,long long int b)<br>
{
return b==0 ? a : gcd(b,a%b);
}
int main(){
long long int a,b;
cin>>a>>b;
if(a>b) cout<<gcd(a,b);
else cout<<gcd(b,a);
return 0;
}
import java.io.*;
import java.util.*;
import java.text.*;
import java.math.*;
import java.util.regex.*;
class GCDArray{
public static int [] extractLeftHalf(int [] numbers)
{
int l =numbers.length/2;
int arr[] = Arrays.copyOf(numbers, l+1);
return arr;
}
public static int [] extractRightHalf(int [] numbers)
{
int l =numbers.length/2;
int arr[] = Arrays.copyOfRange(numbers,l+1, numbers.length);
return arr;
}
public static int gcd(int[] numbers)
{
if(numbers.length==1)
return numbers[0];
else {
int x = numbers[0];
int y = numbers[1];
while(y%x!=0)
{
int rem = y%x;
y = x;
x = rem;
}
return x;
}
}
public static int gcd(int x,int y)
{
while(y%x!=0)
{
int rem = y%x;
y = x;
x = rem;
}
return x;
}
public static int calculateGCD(int[] numbers){
if(numbers.length <= 2){
return gcd(numbers);
}
else {
int left = calculateGCD(extractLeftHalf(numbers));
int right = calculateGCD(extractRightHalf(numbers));
return gcd(left,right);
}
}
public static void main(String[] args) {
Scanner sc = new Scanner(System.in);
int n = sc.nextInt();
int arr[] = new int[n];
for(int i=0;i<n;i++){
arr[i]=sc.nextInt();
}
System.out.println(calculateGCD(arr));
}
}
**
Above is the java working code ..... the pseudo code of which is
already mention by https://stackoverflow.com/users/7412/dogbane
**
A recursive JavaScript (ES6) one-liner for any number of digits.
const gcd = (a, b, ...c) => b ? gcd(b, a % b, ...c) : c.length ? gcd(a, ...c) : Math.abs(a);
This is what comes off the top of my head in Javascript.
function calculateGCD(arrSize, arr) {
if(!arrSize)
return 0;
var n = Math.min(...arr);
for (let i = n; i > 0; i--) {
let j = 0;
while(j < arrSize) {
if(arr[j] % i === 0) {
j++;
}else {
break;
}
if(j === arrSize) {
return i;
}
}
}
}
console.log(generalizedGCD(4, [2, 6, 4, 8]));
// Output => 2
Here was the answer I was looking for.
The best way to find the gcd of n numbers is indeed using recursion.ie gcd(a,b,c)=gcd(gcd(a,b),c). But I was getting timeouts in certain programs when I did this.
The optimization that was needed here was that the recursion should be solved using fast matrix multiplication algorithm.