I'm looking for an algorithm which computes all permutations of a bitstring of given length (n) and amount of bits set (k). For example while n=4 and k=2 the algorithm shall output:
1100
1010
1001
0011
0101
0110
I'm aware of Gosper's Hack which generates the needed permutations in lexicographic order. But i need them to be generated in such a manner, that two consecutive permutations differ in only two (or at least a constant number of) bitpositions (like in the above example).
Another bithack to do that would be awesome, but also a algorithmic description would help me alot.
Walking bit algorithm
To generate permutations of a binary sequence by swapping exactly one set bit with an unset bit in each step (i.e. the Hamming distance between consecutive permutations equals two), you can use this "walking bit" algorithm; the way it works is similar to creating the (reverse) lexicographical order, but the set bits walk right and left alternately, and as a result some parts of the sequence are mirrored. This is probably better explained with an example:
Recursive implementation
A recursive algorithm would receive a sequence of n bits, with k bits set, either all on the left or all on the right. It would then keep a 1 at the end, recurse with the rest of the sequence, move the set bit and keep 01 at the end, recurse with the rest of the bits, move the set bit and keep 001 at the end, etc... until the last recursion with only set bits. As you can see, this creates alternating left-to-right and right-to-left recursions.
When the algorithm is called with a sequence with only one bit set, this is the deepest recursion level, and the set bit walks from one end to the other.
Code example 1
Here's a simple recursive JavaScript implementation:
function walkingBits(n, k) {
var seq = [];
for (var i = 0; i < n; i++) seq[i] = 0;
walk (n, k, 1, 0);
function walk(n, k, dir, pos) {
for (var i = 1; i <= n - k + 1; i++, pos += dir) {
seq[pos] = 1;
if (k > 1) walk(n - i, k - 1, i%2 ? dir : -dir, pos + dir * (i%2 ? 1 : n - i))
else document.write(seq + "<BR>");
seq[pos] = 0;
}
}
}
walkingBits(7,3);
Translated into C++ that could be something like this:
#include <iostream>
#include <string>
void walkingBits(int n, int k, int dir = 1, int pos = 0, bool top = true) {
static std::string seq;
if (top) seq.resize(n, '0');
for (int i = 1; i <= n - k + 1; i++, pos += dir) {
seq[pos] = '1';
if (k > 1) walkingBits(n - i, k - 1, i % 2 ? dir : -dir, pos + dir * (i % 2 ? 1 : n - i), false);
else std::cout << seq << '\n';
seq[pos] = '0';
}
if (top) seq.clear();
}
int main() {
walkingBits(7, 3);
}
(See also [this C++11 version][3], written by VolkerK in response to a question about the above code.)
(Rextester seems to have been hacked, so I've pasted Volker's code below.)
#include <iostream>
#include <vector>
#include <functional>
void walkingBits(size_t n, size_t k) {
std::vector<bool> seq(n, false);
std::function<void(const size_t, const size_t, const int, size_t)> walk = [&](const size_t n, const size_t k, const int dir, size_t pos){
for (size_t i = 1; i <= n - k + 1; i++, pos += dir) {
seq[pos] = true;
if (k > 1) {
walk(n - i, k - 1, i % 2 ? dir : -dir, pos + dir * (i % 2 ? 1 : n - i));
}
else {
for (bool v : seq) {
std::cout << v;
}
std::cout << std::endl;;
}
seq[pos] = false;
}
};
walk(n, k, 1, 0);
}
int main() {
walkingBits(7, 3);
return 0;
}
Code example 2
Or, if you prefer code where elements of an array are actually being swapped:
function walkingBits(n, k) {
var seq = [];
for (var i = 0; i < n; i++) seq[i] = i < k ? 1 : 0;
document.write(seq + "<BR>");
walkRight(n, k, 0);
function walkRight(n, k, pos) {
if (k == 1) for (var p = pos + 1; p < pos + n; p++) swap(p - 1, p)
else for (var i = 1; i <= n - k; i++) {
[walkLeft, walkRight][i % 2](n - i, k - 1, pos + i);
swap(pos + i - 1, pos + i + (i % 2 ? 0 : k - 1));
}
}
function walkLeft(n, k, pos) {
if (k == 1) for (var p = pos + n - 1; p > pos; p--) swap(p - 1, p)
else for (var i = 1; i <= n - k; i++) {
[walkRight, walkLeft][i % 2](n - i, k - 1, pos);
swap(pos + n - i - (i % 2 ? 1 : k), pos + n - i);
}
}
function swap(a, b) {
var c = seq[a]; seq[a] = seq[b]; seq[b] = c;
document.write(seq + "<BR>");
}
}
walkingBits(7,3);
Code example 3
Here the recursion is rolled out into an iterative implementation, with each of the set bits (i.e. each of the recursion levels) represented by an object {o,d,n,p} which holds the offset from the leftmost position, the direction the set bit is moving in, the number of bits (i.e. the length of this part of the sequence), and the current position of the set bit within this part.
function walkingBits(n, k) {
var b = 0, seq = [], bit = [{o: 0, d: 1, n: n, p: 0}];
for (var i = 0; i < n; i++) seq.push(0);
while (bit[0].p <= n - k) {
seq[bit[b].o + bit[b].p * bit[b].d] = 1;
while (++b < k) {
bit[b] = {
o: bit[b-1].o + bit[b-1].d * (bit[b-1].p %2 ? bit[b-1].n-1 : bit[b-1].p+1),
d: bit[b-1].d * (bit[b-1].p %2 ? -1 : 1),
n: bit[b-1].n - bit[b-1].p - 1,
p: 0
}
seq[bit[b].o + bit[b].p * bit[b].d] = 1;
}
document.write(seq + "<BR>");
b = k - 1;
do seq[bit[b].o + bit[b].p * bit[b].d] = 0;
while (++bit[b].p > bit[b].n + b - k && b--);
}
}
walkingBits(7, 3); // n >= k > 0
Transforming lexicographical order into walking bit
Because the walking bit algorithm is a variation of the algorithm to generate the permutations in (reverse) lexicographical order, each permutation in the lexicographical order can be transformed into its corresponding permutation in the walking bit order, by mirroring the appropriate parts of the binary sequence.
So you can use any algorithm (e.g. Gosper's Hack) to create the permutations in lexicographical or reverse lexicographical order, and then transform each one to get the walking bit order.
Practically, this means iterating over the binary sequence from left to right, and if you find a set bit after an odd number of zeros, reversing the rest of the sequence and iterating over it from right to left, and so on...
Code example 4
In the code below the permutations for n,k = 7,3 are generated in reverse lexicographical order, and then transformed one-by-one:
function lexi2walk(lex) {
var seq = [], ofs = 0, pos = 0, dir = 1;
for (var i = 0; i < lex.length; ++i) {
if (seq[ofs + pos * dir] = lex[i]) {
if (pos % 2) ofs -= (dir *= -1) * (pos + lex.length - 1 - i)
else ofs += dir * (pos + 1);
pos = 0;
} else ++pos;
}
return seq;
}
function revLexi(seq) {
var max = true, pos = seq.length, set = 1;
while (pos-- && (max || !seq[pos])) if (seq[pos]) ++set; else max = false;
if (pos < 0) return false;
seq[pos] = 0;
while (++pos < seq.length) seq[pos] = set-- > 0 ? 1 : 0;
return true;
}
var s = [1,1,1,0,0,0,0];
document.write(s + " → " + lexi2walk(s) + "<br>");
while (revLexi(s)) document.write(s + " → " + lexi2walk(s) + "<br>");
Homogeneous Gray path
The permutation order created by this algorithm is similar, but not identical, to the one created by the "homogeneous Gray path for combinations" algorithm described by D. Knuth in The Art of Computer Programming vol. 4a, sect. 7.2.1.3, formula (31) & fig. 26c.
This is easy to achieve with recursion:
public static void nextPerm(List<Integer> list, int num, int index, int n, int k) {
if(k == 0) {
list.add(num);
return;
}
if(index == n) return;
int mask = 1<<index;
nextPerm(list, num^mask, index+1, n, k-1);
nextPerm(list, num, index+1, n, k);
}
Running this with the client:
public static void main(String[] args) {
ArrayList<Integer> list = new ArrayList<Integer>();
nextPerm(list, 0, 0, 4, 2);
}
Output:
0011
0101
1001
0110
1010
1100
The idea is to start with the initial number, and consider changing a bit, one index at a time, and to keep track of how many times you changed the bits. Once you changed the bits k times (when k == 0), store the number and terminate the branch.
Related
Dynamic Programming Change Problem (Limited Coins).
I'm trying to create a program that takes as INPUT:
int coinValues[]; //e.g [coin1,coin2,coin3]
int coinLimit[]; //e.g [2 coin1 available,1 coin2 available,...]
int amount; //the amount we want change for.
OUTPUT:
int DynProg[]; //of size amount+1.
And output should be an Array of size amount+1 of which each cell represents the optimal number of coins we need to give change for the amount of the cell's index.
EXAMPLE: Let's say that we have the cell of Array at index: 5 with a content of 2.
This means that in order to give change for the amount of 5(INDEX), you need 2(cell's content) coins (Optimal Solution).
Basically I need exactly the output of the first array of this video(C[p])
. It's exactly the same problem with the big DIFFERENCE of LIMITED COINS.
Link to Video.
Note: See the video to understand, ignore the 2nd array of the video, and have in mind that I don't need the combinations, but the DP array, so then I can find which coins to give as change.
Thank you.
Consider the next pseudocode:
for every coin nominal v = coinValues[i]:
loop coinLimit[i] times:
starting with k=0 entry, check for non-zero C[k]:
if C[k]+1 < C[k+v] then
replace C[k+v] with C[k]+1 and set S[k+v]=v
Is it clear?
O(nk) solution from an editorial I wrote a while ago:
We start with the basic DP solution that runs in O(k*sum(c)). We have our dp array, where dp[i][j] stores the least possible number of coins from the first i denominations that sum to j. We have the following transition: dp[i][j] = min(dp[i - 1][j - cnt * value[i]] + cnt) for cnt from 0 to j / value[i].
To optimize this to an O(nk) solution, we can use a deque to memorize the minimum values from the previous iteration and make the transitions O(1). The basic idea is that if we want to find the minimum of the last m values in some array, we can maintain an increasing deque that stores possible candidates for the minimum. At each step, we pop off values at the end of the deque greater than the current value before pushing the current value into the back deque. Since the current value is both further to the right and less than the values we popped off, we can be sure they will never be the minimum. Then, we pop off the first element in the deque if it is more than m elements away. The minimum value at each step is now simply the first element in the deque.
We can apply a similar optimization trick to this problem. For each coin type i, we compute the elements of the dp array in this order: For each possible value of j % value[i] in increasing order, we process the values of j which when divided by value[i] produces that remainder in increasing order. Now we can apply the deque optimization trick to find min(dp[i - 1][j - cnt * value[i]] + cnt) for cnt from 0 to j / value[i] in constant time.
Pseudocode:
let n = number of coin denominations
let k = amount of change needed
let v[i] = value of the ith denomination, 1 indexed
let c[i] = maximum number of coins of the ith denomination, 1 indexed
let dp[i][j] = the fewest number of coins needed to sum to j using the first i coin denominations
for i from 1 to k:
dp[0][i] = INF
for i from 1 to n:
for rem from 0 to v[i] - 1:
let d = empty double-ended-queue
for j from 0 to (k - rem) / v[i]:
let currval = rem + v[i] * j
if dp[i - 1][currval] is not INF:
while d is not empty and dp[i - 1][d.back() * v[i] + rem] + j - d.back() >= dp[i - 1][currval]:
d.pop_back()
d.push_back(j)
if d is not empty and j - d.front() > c[i]:
d.pop_front()
if d is empty:
dp[i][currval] = INF
else:
dp[i][currval] = dp[i - 1][d.front() * v[i] + rem] + j - d.front()
This is what you are looking for.
Assumptions made : Coin Values are in descending order
public class CoinChangeLimitedCoins {
public static void main(String[] args) {
int[] coins = { 5, 3, 2, 1 };
int[] counts = { 2, 1, 2, 1 };
int target = 9;
int[] nums = combine(coins, counts);
System.out.println(minCount(nums, target, 0, 0, 0));
}
private static int minCount(int[] nums, int target, int sum, int current, int count){
if(current > nums.length) return -1;
if(sum == target) return count;
if(sum + nums[current] <= target){
return minCount(nums, target, sum+nums[current], current+1, count+1);
} else {
return minCount(nums, target, sum, current+1, count);
}
}
private static int[] combine(int[] coins, int[] counts) {
int sum = 0;
for (int count : counts) {
sum += count;
}
int[] returnArray = new int[sum];
int returnArrayIndex = 0;
for (int i = 0; i < coins.length; i++) {
int count = counts[i];
while (count != 0) {
returnArray[returnArrayIndex] = coins[i];
returnArrayIndex++;
count--;
}
}
return returnArray;
}
}
You can check this question: Minimum coin change problem with limited amount of coins.
BTW, I created c++ program based above link's algorithm:
#include <iostream>
#include <map>
#include <vector>
#include <algorithm>
#include <limits>
using namespace std;
void copyVec(vector<int> from, vector<int> &to){
for(vector<int>::size_type i = 0; i < from.size(); i++)
to[i] = from[i];
}
vector<int> makeChangeWithLimited(int amount, vector<int> coins, vector<int> limits)
{
vector<int> change;
vector<vector<int>> coinsUsed( amount + 1 , vector<int>(coins.size()));
vector<int> minCoins(amount+1,numeric_limits<int>::max() - 1);
minCoins[0] = 0;
vector<int> limitsCopy(limits.size());
copy(limits.begin(), limits.end(), limitsCopy.begin());
for (vector<int>::size_type i = 0; i < coins.size(); ++i)
{
while (limitsCopy[i] > 0)
{
for (int j = amount; j >= 0; --j)
{
int currAmount = j + coins[i];
if (currAmount <= amount)
{
if (minCoins[currAmount] > minCoins[j] + 1)
{
minCoins[currAmount] = minCoins[j] + 1;
copyVec(coinsUsed[j], coinsUsed[currAmount]);
coinsUsed[currAmount][i] += 1;
}
}
}
limitsCopy[i] -= 1;
}
}
if (minCoins[amount] == numeric_limits<int>::max() - 1)
{
return change;
}
copy(coinsUsed[amount].begin(),coinsUsed[amount].end(), back_inserter(change) );
return change;
}
int main()
{
vector<int> coins;
coins.push_back(20);
coins.push_back(50);
coins.push_back(100);
coins.push_back(200);
vector<int> limits;
limits.push_back(100);
limits.push_back(100);
limits.push_back(50);
limits.push_back(20);
int amount = 0;
cin >> amount;
while(amount){
vector<int> change = makeChangeWithLimited(amount,coins,limits);
for(vector<int>::size_type i = 0; i < change.size(); i++){
cout << change[i] << "x" << coins[i] << endl;
}
if(change.empty()){
cout << "IMPOSSIBE\n";
}
cin >> amount;
}
system("pause");
return 0;
}
Code in c#
private static int MinCoinsChangeWithLimitedCoins(int[] coins, int[] counts, int sum)
{
var dp = new int[sum + 1];
Array.Fill(dp, int.MaxValue);
dp[0] = 0;
for (int i = 0; i < coins.Length; i++) // n
{
int coin = coins[i];
for (int j = 0; j < counts[i]; j++) //
{
for (int s = sum; s >= coin ; s--) // sum
{
int remainder = s - coin;
if (remainder >= 0 && dp[remainder] != int.MaxValue)
{
dp[s] = Math.Min(1 + dp[remainder], dp[s]);
}
}
}
}
return dp[sum] == int.MaxValue ? -1 : dp[sum];
}
Question is such that given a set of numbers we have to write a recursive program which prints all possible combination after pairing consecutive numbers or leaving them single.
<div>
Ex set 1,2,3,4,5,6
Output
<ul>
<li>1,2,3,4,5,6</li>
<li>12,3,4,5,6</li>
<li>1,23,4,5,6</li>
<li>1,2,34,5,6</li>
<li>1,2,3,45,6</li>
<li>1,2,3,4,56</li>
<li>12,34,5,6</li>
<li>12,3,45,6</li>
<li>12,3,4,56</li>
<li>1,23,45,6</li>
<li>1,23,4,56</li>
<li>1,2,34,56</li>
<li>12,34,56</li>
</div>
I use c++ to code.
Suppose the given set is a(a[0], a[1], ..., a[n - 1]), and the length of a is n
And the current answer is saved in b
void dfs(int pos, int depth)
{
if(pos >= n)
for(int i = 0; i < depth; ++i)
printf("%d%c", b[i], i == depth - 1 ? '\n' : ',');
else
{
b[depth] = a[pos];
dfs(pos + 1, depth + 1);
if(pos + 1 < n)
{
int c = 1, x = a[pos];
while(x) c *= 10, x /= 10;
b[depth] = a[pos] * c + a[pos + 1];
dfs(pos + 2, depth + 1);
}
}
}
Given a sorted list of numbers, I would like to find the longest subsequence where the differences between successive elements are geometrically increasing. So if the list is
1, 2, 3, 4, 7, 15, 27, 30, 31, 81
then the subsequence is 1, 3, 7, 15, 31. Alternatively consider 1, 2, 5, 6, 11, 15, 23, 41, 47 which has subsequence 5, 11, 23, 47 with a = 3 and k = 2.
Can this be solved in O(n2) time? Where n is the length of the list.
I am interested both in the general case where the progression of differences is ak, ak2, ak3, etc., where both a and k are integers, and in the special case where a = 1, so the progression of difference is k, k2, k3, etc.
Update
I have made an improvement of the algorithm that it takes an average of O(M + N^2) and memory needs of O(M+N). Mainly is the same that the protocol described below, but to calculate the possible factors A,K for ech diference D, I preload a table. This table takes less than a second to be constructed for M=10^7.
I have made a C implementation that takes less than 10minutes to solve N=10^5 diferent random integer elements.
Here is the source code in C: To execute just do: gcc -O3 -o findgeo findgeo.c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <memory.h>
#include <time.h>
struct Factor {
int a;
int k;
struct Factor *next;
};
struct Factor *factors = 0;
int factorsL=0;
void ConstructFactors(int R) {
int a,k,C;
int R2;
struct Factor *f;
float seconds;
clock_t end;
clock_t start = clock();
if (factors) free(factors);
factors = malloc (sizeof(struct Factor) *((R>>1) + 1));
R2 = R>>1 ;
for (a=0;a<=R2;a++) {
factors[a].a= a;
factors[a].k=1;
factors[a].next=NULL;
}
factorsL=R2+1;
R2 = floor(sqrt(R));
for (k=2; k<=R2; k++) {
a=1;
C=a*k*(k+1);
while (C<R) {
C >>= 1;
f=malloc(sizeof(struct Factor));
*f=factors[C];
factors[C].a=a;
factors[C].k=k;
factors[C].next=f;
a++;
C=a*k*(k+1);
}
}
end = clock();
seconds = (float)(end - start) / CLOCKS_PER_SEC;
printf("Construct Table: %f\n",seconds);
}
void DestructFactors() {
int i;
struct Factor *f;
for (i=0;i<factorsL;i++) {
while (factors[i].next) {
f=factors[i].next->next;
free(factors[i].next);
factors[i].next=f;
}
}
free(factors);
factors=NULL;
factorsL=0;
}
int ipow(int base, int exp)
{
int result = 1;
while (exp)
{
if (exp & 1)
result *= base;
exp >>= 1;
base *= base;
}
return result;
}
void findGeo(int **bestSolution, int *bestSolutionL,int *Arr, int L) {
int i,j,D;
int mustExistToBeBetter;
int R=Arr[L-1]-Arr[0];
int *possibleSolution;
int possibleSolutionL=0;
int exp;
int NextVal;
int idx;
int kMax,aMax;
float seconds;
clock_t end;
clock_t start = clock();
kMax = floor(sqrt(R));
aMax = floor(R/2);
ConstructFactors(R);
*bestSolutionL=2;
*bestSolution=malloc(0);
possibleSolution = malloc(sizeof(int)*(R+1));
struct Factor *f;
int *H=malloc(sizeof(int)*(R+1));
memset(H,0, sizeof(int)*(R+1));
for (i=0;i<L;i++) {
H[ Arr[i]-Arr[0] ]=1;
}
for (i=0; i<L-2;i++) {
for (j=i+2; j<L; j++) {
D=Arr[j]-Arr[i];
if (D & 1) continue;
f = factors + (D >>1);
while (f) {
idx=Arr[i] + f->a * f->k - Arr[0];
if ((f->k <= kMax)&& (f->a<aMax)&&(idx<=R)&&H[idx]) {
if (f->k ==1) {
mustExistToBeBetter = Arr[i] + f->a * (*bestSolutionL);
} else {
mustExistToBeBetter = Arr[i] + f->a * f->k * (ipow(f->k,*bestSolutionL) - 1)/(f->k-1);
}
if (mustExistToBeBetter< Arr[L-1]+1) {
idx= floor(mustExistToBeBetter - Arr[0]);
} else {
idx = R+1;
}
if ((idx<=R)&&H[idx]) {
possibleSolution[0]=Arr[i];
possibleSolution[1]=Arr[i] + f->a*f->k;
possibleSolution[2]=Arr[j];
possibleSolutionL=3;
exp = f->k * f->k * f->k;
NextVal = Arr[j] + f->a * exp;
idx=NextVal - Arr[0];
while ( (idx<=R) && H[idx]) {
possibleSolution[possibleSolutionL]=NextVal;
possibleSolutionL++;
exp = exp * f->k;
NextVal = NextVal + f->a * exp;
idx=NextVal - Arr[0];
}
if (possibleSolutionL > *bestSolutionL) {
free(*bestSolution);
*bestSolution = possibleSolution;
possibleSolution = malloc(sizeof(int)*(R+1));
*bestSolutionL=possibleSolutionL;
kMax= floor( pow (R, 1/ (*bestSolutionL) ));
aMax= floor(R / (*bestSolutionL));
}
}
}
f=f->next;
}
}
}
if (*bestSolutionL == 2) {
free(*bestSolution);
possibleSolutionL=0;
for (i=0; (i<2)&&(i<L); i++ ) {
possibleSolution[possibleSolutionL]=Arr[i];
possibleSolutionL++;
}
*bestSolution = possibleSolution;
*bestSolutionL=possibleSolutionL;
} else {
free(possibleSolution);
}
DestructFactors();
free(H);
end = clock();
seconds = (float)(end - start) / CLOCKS_PER_SEC;
printf("findGeo: %f\n",seconds);
}
int compareInt (const void * a, const void * b)
{
return *(int *)a - *(int *)b;
}
int main(void) {
int N=100000;
int R=10000000;
int *A = malloc(sizeof(int)*N);
int *Sol;
int SolL;
int i;
int *S=malloc(sizeof(int)*R);
for (i=0;i<R;i++) S[i]=i+1;
for (i=0;i<N;i++) {
int r = rand() % (R-i);
A[i]=S[r];
S[r]=S[R-i-1];
}
free(S);
qsort(A,N,sizeof(int),compareInt);
/*
int step = floor(R/N);
A[0]=1;
for (i=1;i<N;i++) {
A[i]=A[i-1]+step;
}
*/
findGeo(&Sol,&SolL,A,N);
printf("[");
for (i=0;i<SolL;i++) {
if (i>0) printf(",");
printf("%d",Sol[i]);
}
printf("]\n");
printf("Size: %d\n",SolL);
free(Sol);
free(A);
return EXIT_SUCCESS;
}
Demostration
I will try to demonstrate that the algorithm that I proposed is in average for an equally distributed random sequence. I’m not a mathematician and I am not used to do this kind of demonstrations, so please fill free to correct me any error that you can see.
There are 4 indented loops, the two firsts are the N^2 factor. The M is for the calculation of the possible factors table).
The third loop is executed only once in average for each pair. You can see this checking the size of the pre-calculated factors table. It’s size is M when N->inf. So the average steps for each pair is M/M=1.
So the proof happens to check that the forth loop. (The one that traverses the good made sequences is executed less that or equal O(N^2) for all the pairs.
To demonstrate that, I will consider two cases: one where M>>N and other where M ~= N. Where M is the maximum difference of the initial array: M= S(n)-S(1).
For the first case, (M>>N) the probability to find a coincidence is p=N/M. To start a sequence, it must coincide the second and the b+1 element where b is the length of the best sequence until now. So the loop will enter times. And the average length of this series (supposing an infinite series) is . So the total number of times that the loop will be executed is . And this is close to 0 when M>>N. The problem here is when M~=N.
Now lets consider this case where M~=N. Lets consider that b is the best sequence length until now. For the case A=k=1, then the sequence must start before N-b, so the number of sequences will be N-b, and the times that will go for the loop will be a maximum of (N-b)*b.
For A>1 and k=1 we can extrapolate to where d is M/N (the average distance between numbers). If we add for all A’s from 1 to dN/b then we see a top limit of:
For the cases where k>=2, we see that the sequence must start before , So the loop will enter an average of and adding for all As from 1 to dN/k^b, it gives a limit of
Here, the worst case is when b is minimum. Because we are considering minimum series, lets consider a very worst case of b= 2 so the number of passes for the 4th loop for a given k will be less than
.
And if we add all k’s from 2 to infinite will be:
So adding all the passes for k=1 and k>=2, we have a maximum of:
Note that d=M/N=1/p.
So we have two limits, One that goes to infinite when d=1/p=M/N goes to 1 and other that goes to infinite when d goes to infinite. So our limit is the minimum of both, and the worst case is when both equetions cross. So if we solve the equation:
we see that the maximum is when d=1.353
So it is demonstrated that the forth loops will be processed less than 1.55N^2 times in total.
Of course, this is for the average case. For the worst case I am not able to find a way to generate series whose forth loop are higher than O(N^2), and I strongly believe that they does not exist, but I am not a mathematician to prove it.
Old Answer
Here is a solution in average of O((n^2)*cube_root(M)) where M is the difference between the first and last element of the array. And memory requirements of O(M+N).
1.- Construct an array H of length M so that M[i - S[0]]=true if i exists in the initial array and false if it does not exist.
2.- For each pair in the array S[j], S[i] do:
2.1 Check if it can be the first and third elements of a possible solution. To do so, calculate all possible A,K pairs that meet the equation S(i) = S(j) + AK + AK^2. Check this SO question to see how to solve this problem. And check that exist the second element: S[i]+ A*K
2.2 Check also that exist the element one position further that the best solution that we have. For example, if the best solution that we have until now is 4 elements long then check that exist the element A[j] + AK + AK^2 + AK^3 + AK^4
2.3 If 2.1 and 2.2 are true, then iterate how long is this series and set as the bestSolution until now is is longer that the last.
Here is the code in javascript:
function getAKs(A) {
if (A / 2 != Math.floor(A / 2)) return [];
var solution = [];
var i;
var SR3 = Math.pow(A, 1 / 3);
for (i = 1; i <= SR3; i++) {
var B, C;
C = i;
B = A / (C * (C + 1));
if (B == Math.floor(B)) {
solution.push([B, C]);
}
B = i;
C = (-1 + Math.sqrt(1 + 4 * A / B)) / 2;
if (C == Math.floor(C)) {
solution.push([B, C]);
}
}
return solution;
}
function getBestGeometricSequence(S) {
var i, j, k;
var bestSolution = [];
var H = Array(S[S.length-1]-S[0]);
for (i = 0; i < S.length; i++) H[S[i] - S[0]] = true;
for (i = 0; i < S.length; i++) {
for (j = 0; j < i; j++) {
var PossibleAKs = getAKs(S[i] - S[j]);
for (k = 0; k < PossibleAKs.length; k++) {
var A = PossibleAKs[k][0];
var K = PossibleAKs[k][17];
var mustExistToBeBetter;
if (K==1) {
mustExistToBeBetter = S[j] + A * bestSolution.length;
} else {
mustExistToBeBetter = S[j] + A * K * (Math.pow(K,bestSolution.length) - 1)/(K-1);
}
if ((H[S[j] + A * K - S[0]]) && (H[mustExistToBeBetter - S[0]])) {
var possibleSolution=[S[j],S[j] + A * K,S[i]];
exp = K * K * K;
var NextVal = S[i] + A * exp;
while (H[NextVal - S[0]] === true) {
possibleSolution.push(NextVal);
exp = exp * K;
NextVal = NextVal + A * exp;
}
if (possibleSolution.length > bestSolution.length) {
bestSolution = possibleSolution;
}
}
}
}
}
return bestSolution;
}
//var A= [ 1, 2, 3,5,7, 15, 27, 30,31, 81];
var A=[];
for (i=1;i<=3000;i++) {
A.push(i);
}
var sol=getBestGeometricSequence(A);
$("#result").html(JSON.stringify(sol));
You can check the code here: http://jsfiddle.net/6yHyR/1/
I maintain the other solution because I believe that it is still better when M is very big compared to N.
Just to start with something, here is a simple solution in JavaScript:
var input = [0.7, 1, 2, 3, 4, 7, 15, 27, 30, 31, 81],
output = [], indexes, values, i, index, value, i_max_length,
i1, i2, i3, j1, j2, j3, difference12a, difference23a, difference12b, difference23b,
scale_factor, common_ratio_a, common_ratio_b, common_ratio_c,
error, EPSILON = 1e-9, common_ratio_is_integer,
resultDiv = $("#result");
for (i1 = 0; i1 < input.length - 2; ++i1) {
for (i2 = i1 + 1; i2 < input.length - 1; ++i2) {
scale_factor = difference12a = input[i2] - input[i1];
for (i3 = i2 + 1; i3 < input.length; ++i3) {
difference23a = input[i3] - input[i2];
common_ratio_1a = difference23a / difference12a;
common_ratio_2a = Math.round(common_ratio_1a);
error = Math.abs((common_ratio_2a - common_ratio_1a) / common_ratio_1a);
common_ratio_is_integer = error < EPSILON;
if (common_ratio_2a > 1 && common_ratio_is_integer) {
indexes = [i1, i2, i3];
j1 = i2;
j2 = i3
difference12b = difference23a;
for (j3 = j2 + 1; j3 < input.length; ++j3) {
difference23b = input[j3] - input[j2];
common_ratio_1b = difference23b / difference12b;
common_ratio_2b = Math.round(common_ratio_1b);
error = Math.abs((common_ratio_2b - common_ratio_1b) / common_ratio_1b);
common_ratio_is_integer = error < EPSILON;
if (common_ratio_is_integer && common_ratio_2a === common_ratio_2b) {
indexes.push(j3);
j1 = j2;
j2 = j3
difference12b = difference23b;
}
}
values = [];
for (i = 0; i < indexes.length; ++i) {
index = indexes[i];
value = input[index];
values.push(value);
}
output.push(values);
}
}
}
}
if (output !== []) {
i_max_length = 0;
for (i = 1; i < output.length; ++i) {
if (output[i_max_length].length < output[i].length)
i_max_length = i;
}
for (i = 0; i < output.length; ++i) {
if (output[i_max_length].length == output[i].length)
resultDiv.append("<p>[" + output[i] + "]</p>");
}
}
Output:
[1, 3, 7, 15, 31]
I find the first three items of every subsequence candidate, calculate the scale factor and the common ratio from them, and if the common ratio is integer, then I iterate over the remaining elements after the third one, and add those to the subsequence, which fit into the geometric progression defined by the first three items. As a last step, I select the sebsequence/s which has/have the largest length.
In fact it is exactly the same question as Longest equally-spaced subsequence, you just have to consider the logarithm of your data. If the sequence is a, ak, ak^2, ak^3, the logarithmique value is ln(a), ln(a) + ln(k), ln(a)+2ln(k), ln(a)+3ln(k), so it is equally spaced. The opposite is of course true. There is a lot of different code in the question above.
I don't think the special case a=1 can be resolved more efficiently than an adaptation from an algorithm above.
Here is my solution in Javascript. It should be close to O(n^2) except may be in some pathological cases.
function bsearch(Arr,Val, left,right) {
if (left == right) return left;
var m=Math.floor((left + right) /2);
if (Val <= Arr[m]) {
return bsearch(Arr,Val,left,m);
} else {
return bsearch(Arr,Val,m+1,right);
}
}
function findLongestGeometricSequence(S) {
var bestSolution=[];
var i,j,k;
var H={};
for (i=0;i<S.length;i++) H[S[i]]=true;
for (i=0;i<S.length;i++) {
for (j=0;j<i;j++) {
for (k=j+1;k<i;) {
var possibleSolution=[S[j],S[k],S[i]];
var K = (S[i] - S[k]) / (S[k] - S[j]);
var A = (S[k] - S[j]) * (S[k] - S[j]) / (S[i] - S[k]);
if ((Math.floor(K) == K) && (Math.floor(A)==A)) {
exp= K*K*K;
var NextVal= S[i] + A * exp;
while (H[NextVal] === true) {
possibleSolution.push(NextVal);
exp = exp * K;
NextVal= NextVal + A * exp;
}
if (possibleSolution.length > bestSolution.length)
bestSolution=possibleSolution;
K--;
} else {
K=Math.floor(K);
}
if (K>0) {
var NextPossibleMidValue= (S[i] + K*S[j]) / (K +1);
k++;
if (S[k]<NextPossibleMidValue) {
k=bsearch(S,NextPossibleMidValue, k+1, i);
}
} else {
k=i;
}
}
}
}
return bestSolution;
}
function Run() {
var MyS= [0.7, 1, 2, 3, 4, 5,6,7, 15, 27, 30,31, 81];
var sol = findLongestGeometricSequence(MyS);
alert(JSON.stringify(sol));
}
Small Explanation
If we take 3 numbers of the array S(j) < S(k) < S(i) then you can calculate a and k so that: S(k) = S(j) + a*k and S(i) = S(k) + a*k^2 (2 equations and 2 incognits). With that in mind, you can check if exist a number in the array that is S(next) = S(i) + a*k^3. If that is the case, then continue checknng for S(next2) = S(next) + a*k^4 and so on.
This would be a O(n^3) solution, but you can hava advantage that k must be integer in order to limit the S(k) points selected.
In case that a is known, then you can calculate a(k) and you need to check only one number in the third loop, so this case will be clearly a O(n^2).
I think this task is related with not so long ago posted Longest equally-spaced subsequence. I've just modified my algorithm in Python a little bit:
from math import sqrt
def add_precalc(precalc, end, (a, k), count, res, N):
if end + a * k ** res[1]["count"] > N: return
x = end + a * k ** count
if x > N or x < 0: return
if precalc[x] is None: return
if (a, k) not in precalc[x]:
precalc[x][(a, k)] = count
return
def factors(n):
res = []
for x in range(1, int(sqrt(n)) + 1):
if n % x == 0:
y = n / x
res.append((x, y))
res.append((y, x))
return res
def work(input):
precalc = [None] * (max(input) + 1)
for x in input: precalc[x] = {}
N = max(input)
res = ((0, 0), {"end":0, "count":0})
for i, x in enumerate(input):
for y in input[i::-1]:
for a, k in factors(x - y):
if (a, k) in precalc[x]: continue
add_precalc(precalc, x, (a, k), 2, res, N)
for step, count in precalc[x].iteritems():
count += 1
if count > res[1]["count"]: res = (step, {"end":x, "count":count})
add_precalc(precalc, x, step, count, res, N)
precalc[x] = None
d = [res[1]["end"]]
for x in range(res[1]["count"] - 1, 0, -1):
d.append(d[-1] - res[0][0] * res[0][1] ** x)
d.reverse()
return d
explanation
Traversing the array
For each previous element of the array calculate factors of the difference between current and taken previous element and then precalculate next possible element of the sequence and saving it to precalc array
So when arriving at element i there're already all possible sequences with element i in the precalc array, so we have to calculate next possible element and save it to precalc.
Currently there's one place in algorithm that could be slow - factorization of each previous number. I think it could be made faster with two optimizations:
more effective factorization algorithm
find a way not to see at each element of array, using the fact that array is sorted and there's already a precalculated sequences
Python:
def subseq(a):
seq = []
aset = set(a)
for i, x in enumerate(a):
# elements after x
for j, x2 in enumerate(a[i+1:]):
j += i + 1 # enumerate starts j at 0, we want a[j] = x2
bk = x2 - x # b*k (assuming k and k's exponent start at 1)
# given b*k, bruteforce values of k
for k in range(1, bk + 1):
items = [x, x2] # our subsequence so far
nextdist = bk * k # what x3 - x2 should look like
while items[-1] + nextdist in aset:
items.append(items[-1] + nextdist)
nextdist *= k
if len(items) > len(seq):
seq = items
return seq
Running time is O(dn^3), where d is the (average?) distance between two elements,
and n is of course len(a).
I have been trying to solve the following problem in interview street. Count Scorecards(30 points)
In a tournament, N players play against each other exactly once. Each game results in either of the player winning. There are no ties. You have given a scorecard containing the scores of each player at the end of the tournament. The score of a player is the total number of games the player won in the tournament. However, the scores of some players might have been erased from the scorecard. How many possible scorecards are consistent with the input scorecard?
Input:
The first line contains the number of cases T. T cases follow. Each case contains the number N on the first line followed by N numbers on the second line. The ith number denotes s_i, the score of the ith player. If the score of the ith player has been erased, it is represented by -1.
Output:
Output T lines, containing the answer for each case. Output each result modulo 1000000007.
Constraints:
1 <= T <= 20
1 <= N <= 40
-1 <= s_i < N
Sample Input:
5
3
-1 -1 2
3
-1 -1 -1
4
0 1 2 3
2
1 1
4
-1 -1 -1 2
Sample Output:
2
7
1
0
12
Explanation:
For the first case, there are 2 scorecards possible: 0,1,2 or 1,0,2.
For the second case, the valid scorecards are 1,1,1, 0,1,2, 0,2,1, 1,0,2, 1,2,0, 2,0,1, 2,1,0.
For the third case, the only valid scorecard is {0,1,2,3}.
For the fourth case, there is no valid scorecard. It is not possible for both players to have score 1.
I have tried to come up with generic functions approach, but i am really trying to nail down this problem using Dynamic programming. How can you think of recurrence relations for this problem?.
Here is the DP solution to the above problem
public static int[][] table; // stores the result of the overlapping sub problems
private static int N;
public static void main(String args[]) {
Scanner scanner = new Scanner(System.in);
int testCases = scanner.nextInt();
for (int i = 0; i < testCases; i++) {
N = scanner.nextInt();
int[] scores = new int[N];
for (int j = 0; j < N; j++) {
scores[j] = scanner.nextInt();
}
long result = process(scores) % 1000000007L;
System.out.println(result );
}
}
private static long process(int[] scores) {
int sum = 0;
int amongPlayers = 0; //count no of players whose score has been erased(-1)
for (int i = 0; i < N; i++) {
if (scores[i] != -1) {
sum += scores[i];
} else {
amongPlayers++;
}
}
int noGames = (N * (N -1)) /2; // total number of games
if (sum < noGames) {
int distribute = noGames - sum; // score needed to be distributed;
table = new int[distribute + 1 ][amongPlayers + 1];
for (int m = 0; m <= distribute; m++) {
for (int n = 0; n <= amongPlayers; n++) {
table[m][n] = -1;
}
}
return distribute(distribute, amongPlayers); // distrubute scores among players whose score is erased(-1)
}
else if(sum == noGames){
return 1;
}
return 0;
}
/**
* Dynamic programming recursive calls
* #param distribute
* #param amongPlayers
* #return
*/
private static int distribute(int distribute, int amongPlayers) {
if(distribute == 0 && amongPlayers == 0)
return 1;
if (amongPlayers <= 0)
return 0;
if(distribute == 0)
return 1;
int result = 0;
if (table[distribute][amongPlayers - 1] == -1) {
int zeroResult = distribute(distribute, amongPlayers - 1);
table[distribute][amongPlayers - 1] = zeroResult;
}
result += table[distribute][amongPlayers - 1];
for (int i = 1; i < N ; i++) { // A person could win maximum of N-1 games
if (distribute - i >= 0) {
if (table[distribute - i][amongPlayers - 1] == -1) {
int localResult = distribute(distribute - i,
amongPlayers - 1);
table[distribute - i][amongPlayers - 1] = localResult;
}
result += table[distribute - i][amongPlayers - 1];
}
}
return result;
}
Observations:
Sequence s[1], s[2], ..., s[n] to be consistent scorecard, these properties must hold:
s[i1] + s[i2] + .. + s[ik] >= k * (k — 1) / 2, where i1 < i2 < .. < ik (i.e for every subsequences of length k)
s[1] + s[2] + .. + s[n] = n * (n — 1) / 2
First of all we need to check not erased scores, just using 1 condition. Then put erased scores using dynamic programming.
Let's denote erased scores b[i], not erased scores a[i];
sum{i = 1 .. l} a[i] + sum{i = 1 .. k} b[i] >= (k + l) * (k + l - 1) / 2
sum{i = 1 .. l} a[i] + sum{i = 1 .. k} b[i] >= 0 + 1 + .. + (k + l - 1)
sum{i = 1 .. l} (a[i] - (k + i - 1)) + sum{i = 1 .. k} b[i] >= 0 + 1 + .. + (k - 1)
So we can pre calculate for every k, minimal value of sum{i = 1 .. l} (a[i] - (k + i - 1))/
Dynamic programming:
states:
dp[k][score][sum]: we know first k minimum erased scores, and their values not exceeds $score$, and sum is their sum.
transitions:
Skip score, dp[k][score][sum] += dp[k][score + 1][sum];
Put $i$ scores of value $score$ dp[k][score][sum] += C[m — k][i] * dp[k + i][score + 1][sum + i*score], where m number of erased scores, C[n][k] = combination.
my code
The total sum of the wins should be (N C 2)
Subtract the known values which are given in the input. Let the remaining sum (N C 2) - x be called S. Let the number of -1's in the input be Q.
The problem now boils down to finding the number of integral solutions of Q variables ranging from 0 to N-1 (max score possible) and sum of which is S
Let DP[q][s] denote the number of integral solutions of q variables whose sum is s
Then we have,
DP[q][s] = Sum (i=0 to N-1) DP[q-1][s-i]
DP[Q][S] gives the solution
EDIT:
Observation:
For x people remaining, the number of total wins should be at least x*(x-1)/2 (when they play each other). Thus, at any time for q people, s cannot exceed (N-q)(N-q-1)/2 = M
There should be one more constraint that DP[q][s] should be equal to 0 when s is greater than M
I'm trying to solve this assignment, too, and think it should be something like this:
The number of players (=N), the number of unknown cards (count the "-1") and the sum of the known cards (count all cards except "-1") are given. The total number of games possible should be 1 +2 +3 + ... + (players-1): The first player has (players-1) opponents, the second player (players-2) etc.
Now you can recursively calculate the sum of possible score cards:
Initialize an empty hashmap with (players, unknown cards, sum of known cards) as the key and the sum of possible score cards as the value.
If all cards are defined, then the answer is either 0 (if the sum of all cards equals the total number of games possible) or 1 (if the sum of all cards does not equal the total number of games possible).
If not all cards are defined, then run a for loop and set one unknown card to 0, 1, 2 ... (players-1) and try to read the result from the hashmap. If it is not in the hashmap call the method itself and save the result in the map.
The recursion code should be something like this:
def recursion(players: Int, games: Int, unknownCards: Int, knownScore: Int): Int = {
unknownCards match {
case 0 if knownScore != games => 0
case 0 if knownScore == games => 1
case _ =>
map.get(players, unknownCards, knownScore) getOrElse {
var sum = 0
for (i <- 0 until players) sum += main(players, games, unknownCards - 1, knownScore + i)
sum %= 1000000007
map.put((players, unknownCards, knownScore), sum)
sum
}
}
}
Try this
import java.util.Scanner;
public class Solution {
final private static int size = 780;
private static long[][] possibleSplits = new long[size][size];
static {
for(int i=0; i < size; ++i)
possibleSplits[i][0] = 1;
for(int j=0; j< size; ++j)
possibleSplits[0][j] = j+1;
for(int i=1; i< size; ++i)
for(int j=1; j < size; ++j)
{
possibleSplits[i][j] = (possibleSplits[i-1][j] + possibleSplits[i][j-1]) % 1000000007;
}
}
public long possibleWays = 0;
public Solution(int n, String scores)
{
long totalScores = 0;
int numOfErasedScores = 0;
for(String str : scores.split(" "))
{
int s = Integer.parseInt(str);
if (s < 0)
++numOfErasedScores;
else
totalScores += s;
}
long totalErasedScores = ncr(n,2) - totalScores;
if(totalErasedScores == 0)
++possibleWays;
else if (totalErasedScores > 0)
partition(n-1, totalErasedScores, numOfErasedScores);
}
private void partition(int possibleMax, long total, int split)
{
if (split == 0)
return;
possibleWays = possibleSplits[(int)total-1][split-1];
if (total > possibleMax)
possibleWays -= split;
}
public static void main(String[] args)
{
Scanner in = new Scanner(System.in);
int numberOfTestCases = Integer.parseInt(in.nextLine().trim());
for(int i=0; i< numberOfTestCases; ++i)
{
String str = in.nextLine().trim();
int numberOfPlayers = Integer.parseInt(str);
String playerScores = in.nextLine().trim();
long result = new Solution(numberOfPlayers, playerScores).possibleWays;
System.out.println(result % 1000000007);
}
in.close();
}
public static long ncr(int n, int r)
{
long result = 1;
for(int i= Math.max(n-r, r)+1;i<=n;++i)
result*= i;
result/= fact(Math.min(n-r,r));
return result;
}
public static long fact(int n)
{
long result = 1;
for(int i =2; i<= n; ++i)
result *= i;
return result;
}
}
Given a set of numbers, divide the numbers into two subsets such that difference between the sum of numbers in two subsets is minimal.
This is the idea that I have, but I am not sure if this is a correct solution:
Sort the array
Take the first 2 elements. Consider them as 2 sets (each having 1 element)
Take the next element from the array.
Decide in which set should this element go (by computing the sum => it should be minimum)
Repeat
Is this the correct solution? Can we do better?
The decision version of the problem you are describing is an NP-complete problem and it is called the partition problem. There are a number of approximations which provide, in many cases, optimal or, at least, good enough solutions.
The simple algorithm you described is a way playground kids would pick teams. This greedy algorithm performs remarkably well if the numbers in the set are of similar orders of magnitude.
The article The Easiest Hardest Problem, by American Scientist, gives an excellent analysis of the problem. You should go through and read it!
No, that doesn't work. There is no polynomial time solution (unless P=NP). The best you can do is just look at all different subsets. Have a look at the subset sum problem.
Consider the list [0, 1, 5, 6]. You will claim {0, 5} and {1, 6}, when the best answer is actually {0, 1, 5} and {6}.
No, Your algorithm is wrong. Your algo follows a greedy approach.
I implemented your approach and it failed over this test case:
(You may try here)
A greedy algorithm:
#include<bits/stdc++.h>
#define rep(i,_n) for(int i=0;i<_n;i++)
using namespace std;
#define MXN 55
int a[MXN];
int main() {
//code
int t,n,c;
cin>>t;
while(t--){
cin>>n;
rep(i,n) cin>>a[i];
sort(a, a+n);
reverse(a, a+n);
ll sum1 = 0, sum2 = 0;
rep(i,n){
cout<<a[i]<<endl;
if(sum1<=sum2)
sum1 += a[i];
else
sum2 += a[i];
}
cout<<abs(sum1-sum2)<<endl;
}
return 0;
}
Test case:
1
8
16 14 13 13 12 10 9 3
Wrong Ans: 6
16 13 10 9
14 13 12 3
Correct Ans: 0
16 13 13 3
14 12 10 9
The reason greedy algorithm fails is that it does not consider cases when taking a larger element in current larger sum set and later a much smaller in the larger sum set may result much better results. It always try to minimize current difference without exploring or knowing further possibilities, while in a correct solution you might include an element in a larger set and include a much smaller element later to compensate this difference, same as in above test case.
Correct Solution:
To understand the solution, you will need to understand all below problems in order:
0/1 Knapsack with Dynamic Programming
Partition Equal Subset Sum with DP
Solution
My Code (Same logic as this):
#include<bits/stdc++.h>
#define rep(i,_n) for(int i=0;i<_n;i++)
using namespace std;
#define MXN 55
int arr[MXN];
int dp[MXN][MXN*MXN];
int main() {
//code
int t,N,c;
cin>>t;
while(t--){
rep(i,MXN) fill(dp[i], dp[i]+MXN*MXN, 0);
cin>>N;
rep(i,N) cin>>arr[i];
int sum = accumulate(arr, arr+N, 0);
dp[0][0] = 1;
for(int i=1; i<=N; i++)
for(int j=sum; j>=0; j--)
dp[i][j] |= (dp[i-1][j] | (j>=arr[i-1] ? dp[i-1][j-arr[i-1]] : 0));
int res = sum;
for(int i=0; i<=sum/2; i++)
if(dp[N][i]) res = min(res, abs(i - (sum-i)));
cout<<res<<endl;
}
return 0;
}
Combinations over combinations approach:
import itertools as it
def min_diff_sets(data):
"""
Parameters:
- `data`: input list.
Return:
- min diff between sum of numbers in two sets
"""
if len(data) == 1:
return data[0]
s = sum(data)
# `a` is list of all possible combinations of all possible lengths (from 1
# to len(data) )
a = []
for i in range(1, len(data)):
a.extend(list(it.combinations(data, i)))
# `b` is list of all possible pairs (combinations) of all elements from `a`
b = it.combinations(a, 2)
# `c` is going to be final correct list of combinations.
# Let's apply 2 filters:
# 1. leave only pairs where: sum of all elements == sum(data)
# 2. leave only pairs where: flat list from pairs == data
c = filter(lambda x: sum(x[0])+sum(x[1])==s, b)
c = filter(lambda x: sorted([i for sub in x for i in sub])==sorted(data), c)
# `res` = [min_diff_between_sum_of_numbers_in_two_sets,
# ((set_1), (set_2))
# ]
res = sorted([(abs(sum(i[0]) - sum(i[1])), i) for i in c],
key=lambda x: x[0])
return min([i[0] for i in res])
if __name__ == '__main__':
assert min_diff_sets([10, 10]) == 0, "1st example"
assert min_diff_sets([10]) == 10, "2nd example"
assert min_diff_sets([5, 8, 13, 27, 14]) == 3, "3rd example"
assert min_diff_sets([5, 5, 6, 5]) == 1, "4th example"
assert min_diff_sets([12, 30, 30, 32, 42, 49]) == 9, "5th example"
assert min_diff_sets([1, 1, 1, 3]) == 0, "6th example"
The recursive approach is to generate all possible sums from all the values of array and to check
which solution is the most optimal one.
To generate sums we either include the i’th item in set 1 or don’t include, i.e., include in
set 2.
The time complexity is O(n*sum) for both time and space.T
public class MinimumSubsetSum {
static int dp[][];
public static int minDiffSubsets(int arr[], int i, int calculatedSum, int totalSum) {
if(dp[i][calculatedSum] != -1) return dp[i][calculatedSum];
/**
* If i=0, then the sum of one subset has been calculated as we have reached the last
* element. The sum of another subset is totalSum - calculated sum. We need to return the
* difference between them.
*/
if(i == 0) {
return Math.abs((totalSum - calculatedSum) - calculatedSum);
}
//Including the ith element
int iElementIncluded = minDiffSubsets(arr, i-1, arr[i-1] + calculatedSum,
totalSum);
//Excluding the ith element
int iElementExcluded = minDiffSubsets(arr, i-1, calculatedSum, totalSum);
int res = Math.min(iElementIncluded, iElementExcluded);
dp[i][calculatedSum] = res;
return res;
}
public static void util(int arr[]) {
int totalSum = 0;
int n = arr.length;
for(Integer e : arr) totalSum += e;
dp = new int[n+1][totalSum+1];
for(int i=0; i <= n; i++)
for(int j=0; j <= totalSum; j++)
dp[i][j] = -1;
int res = minDiffSubsets(arr, n, 0, totalSum);
System.out.println("The min difference between two subset is " + res);
}
public static void main(String[] args) {
util(new int[]{3, 1, 4, 2, 2, 1});
}
}
We can use Dynamic Programming (similar to the way we find if a set can be partitioned into two equal sum subsets). Then we find the max possible sum, which will be our first partition.
Second partition will be the difference of the total sum and firstSum.
Answer will be the difference of the first and second partitions.
public int minDiffernce(int set[]) {
int sum = 0;
int n = set.length;
for(int i=0; i<n; i++)
sum+=set[i];
//finding half of total sum, because min difference can be at max 0, if one subset reaches half
int target = sum/2;
boolean[][] dp = new boolean[n+1][target+1];//2
for(int i = 0; i<=n; i++)
dp[i][0] = true;
for(int i= 1; i<=n; i++){
for(int j = 1; j<=target;j++){
if(set[i-1]>j) dp[i][j] = dp[i-1][j];
else dp[i][j] = dp[i-1][j] || dp[i-1][j-set[i-1]];
}
}
// we now find the max sum possible starting from target
int firstPart = 0;
for(int j = target; j>=0; j--){
if(dp[n][j] == true) {
firstPart = j; break;
}
}
int secondPart = sum - firstPart;
return Math.abs(firstPart - secondPart);
}
One small change: reverse the order - start with the largest number and work down. This will minimize the error.
Are you sorting your subset into decending order or ascending order?
Think about it like this, the array {1, 3, 5, 8, 9, 25}
if you were to divide, you would have {1,8,9} =18 {3,5,25} =33
If it were sorted into descending order it would work out a lot better
{25,1}=26 {9,8,5,3}=25
So your solution is basically correct, it just needs to make sure to take the largest values first.
EDIT: Read tskuzzy's post. Mine does not work
This is a variation of the knapsack and subset sum problem.
In subset sum problem, given n positive integers and a value k and we have to find the sum of subset whose value is less than or equal to k.
In the above problem we have given an array, here we have to find the subset whose sum is less than or equal to total_sum(sum of array values).
So the
subset sum can be found using a variation in knapsack algorithm,by
taking profits as given array values. And the final answer is
total_sum-dp[n][total_sum/2]. Have a look at the below code for clear
understanding.
#include<iostream>
#include<cstdio>
using namespace std;
int main()
{
int n;
cin>>n;
int arr[n],sum=0;
for(int i=1;i<=n;i++)
cin>>arr[i],sum+=arr[i];
int temp=sum/2;
int dp[n+1][temp+2];
for(int i=0;i<=n;i++)
{
for(int j=0;j<=temp;j++)
{
if(i==0 || j==0)
dp[i][j]=0;
else if(arr[i]<=j)
dp[i][j]=max(dp[i-1][j],dp[i-1][j-arr[i]]+arr[i]);
else
{
dp[i][j]=dp[i-1][j];
}
}
}
cout<<sum-2*dp[n][temp]<<endl;
}
This can be solve using BST.
First sort the array say arr1
To start create another arr2 with the last element of arr1 (remove this ele from arr1)
Now:Repeat the steps till no swap happens.
Check arr1 for an element which can be moved to arr2 using BST such that the diff is less MIN diff found till now.
if we find an element move this element to arr2 and go to step1 again.
if we don't find any element in above steps do steps 1 & 2 for arr2 & arr1.
i.e. now check if we have any element in arr2 which can be moved to arr1
continue steps 1-4 till we don't need any swap..
we get the solution.
Sample Java Code:
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
/**
* Divide an array so that the difference between these 2 is min
*
* #author shaikhjamir
*
*/
public class DivideArrayForMinDiff {
/**
* Create 2 arrays and try to find the element from 2nd one so that diff is
* min than the current one
*/
private static int sum(List<Integer> arr) {
int total = 0;
for (int i = 0; i < arr.size(); i++) {
total += arr.get(i);
}
return total;
}
private static int diff(ArrayList<Integer> arr, ArrayList<Integer> arr2) {
int diff = sum(arr) - sum(arr2);
if (diff < 0)
diff = diff * -1;
return diff;
}
private static int MIN = Integer.MAX_VALUE;
private static int binarySearch(int low, int high, ArrayList<Integer> arr1, int arr2sum) {
if (low > high || low < 0)
return -1;
int mid = (low + high) / 2;
int midVal = arr1.get(mid);
int sum1 = sum(arr1);
int resultOfMoveOrg = (sum1 - midVal) - (arr2sum + midVal);
int resultOfMove = (sum1 - midVal) - (arr2sum + midVal);
if (resultOfMove < 0)
resultOfMove = resultOfMove * -1;
if (resultOfMove < MIN) {
// lets do the swap
return mid;
}
// this is positive number greater than min
// which mean we should move left
if (resultOfMoveOrg < 0) {
// 1,10, 19 ==> 30
// 100
// 20, 110 = -90
// 29, 111 = -83
return binarySearch(low, mid - 1, arr1, arr2sum);
} else {
// resultOfMoveOrg > 0
// 1,5,10, 15, 19, 20 => 70
// 21
// For 10
// 60, 31 it will be 29
// now if we move 1
// 71, 22 ==> 49
// but now if we move 20
// 50, 41 ==> 9
return binarySearch(mid + 1, high, arr1, arr2sum);
}
}
private static int findMin(ArrayList<Integer> arr1) {
ArrayList<Integer> list2 = new ArrayList<>(arr1.subList(arr1.size() - 1, arr1.size()));
arr1.remove(arr1.size() - 1);
while (true) {
int index = binarySearch(0, arr1.size(), arr1, sum(list2));
if (index != -1) {
int val = arr1.get(index);
arr1.remove(index);
list2.add(val);
Collections.sort(list2);
MIN = diff(arr1, list2);
} else {
// now try for arr2
int index2 = binarySearch(0, list2.size(), list2, sum(arr1));
if (index2 != -1) {
int val = list2.get(index2);
list2.remove(index2);
arr1.add(val);
Collections.sort(arr1);
MIN = diff(arr1, list2);
} else {
// no switch in both the cases
break;
}
}
}
System.out.println("MIN==>" + MIN);
System.out.println("arr1==>" + arr1 + ":" + sum(arr1));
System.out.println("list2==>" + list2 + ":" + sum(list2));
return 0;
}
public static void main(String args[]) {
ArrayList<Integer> org = new ArrayList<>();
org = new ArrayList<>();
org.add(1);
org.add(2);
org.add(3);
org.add(7);
org.add(8);
org.add(10);
findMin(org);
}
}
you can use bits to solve this problem by looping over all the possible combinations using bits:
main algorithm:
for(int i = 0; i < 1<<n; i++) {
int s = 0;
for(int j = 0; j < n; j++) {
if(i & 1<<j) s += arr[j];
}
int curr = abs((total-s)-s);
ans = min(ans, curr);
}
use long long for greater inputs.
but here I found a recursive and dynamic programming solution and I used both the approaches to solve the question and both worked for greater inputs perfectly fine. Hope this helps :) link to solution
Please check this logic which I have written for this problem. It worked for few scenarios I checked. Please comment on the solution,
Approach :
Sort the main array and divide it into 2 teams.
Then start making the team equal by shift and swapping elements from one array to other, based on the conditions mentioned in the code.
If the difference is difference of sum is less than the minimum number of the larger array(array with bigger sum), then shift the elements from the bigger array to smaller array.Shifting happens with the condition, that element from the bigger array with value less than or equal to the difference.When all the elements from the bigger array is greater than the difference, the shifting stops and swapping happens. I m just swapping the last elements of the array (It can be made more efficient by finding which two elements to swap), but still this worked. Let me know if this logic failed in any scenario.
public class SmallestDifference {
static int sum1 = 0, sum2 = 0, diff, minDiff;
private static List<Integer> minArr1;
private static List<Integer> minArr2;
private static List<Integer> biggerArr;
/**
* #param args
*/
public static void main(String[] args) {
SmallestDifference sm = new SmallestDifference();
Integer[] array1 = { 2, 7, 1, 4, 5, 9, 10, 11 };
List<Integer> array = new ArrayList<Integer>();
for (Integer val : array1) {
array.add(val);
}
Collections.sort(array);
CopyOnWriteArrayList<Integer> arr1 = new CopyOnWriteArrayList<>(array.subList(0, array.size() / 2));
CopyOnWriteArrayList<Integer> arr2 = new CopyOnWriteArrayList<>(array.subList(array.size() / 2, array.size()));
diff = Math.abs(sm.getSum(arr1) - sm.getSum(arr2));
minDiff = array.get(0);
sm.updateSum(arr1, arr2);
System.out.println(arr1 + " : " + arr2);
System.out.println(sum1 + " - " + sum2 + " = " + diff + " : minDiff = " + minDiff);
int k = arr2.size();
biggerArr = arr2;
while (diff != 0 && k >= 0) {
while (diff != 0 && sm.findMin(biggerArr) < diff) {
sm.swich(arr1, arr2);
int sum1 = sm.getSum(arr1), sum2 = sm.getSum(arr2);
diff = Math.abs(sum1 - sum2);
if (sum1 > sum2) {
biggerArr = arr1;
} else {
biggerArr = arr2;
}
if (minDiff > diff || sm.findMin(biggerArr) > diff) {
minDiff = diff;
minArr1 = new CopyOnWriteArrayList<>(arr1);
minArr2 = new CopyOnWriteArrayList<>(arr2);
}
sm.updateSum(arr1, arr2);
System.out.println("Shifting : " + sum1 + " - " + sum2 + " = " + diff + " : minDiff = " + minDiff);
}
while (k >= 0 && minDiff > array.get(0) && minDiff != 0) {
sm.swap(arr1, arr2);
diff = Math.abs(sm.getSum(arr1) - sm.getSum(arr2));
if (minDiff > diff) {
minDiff = diff;
minArr1 = new CopyOnWriteArrayList<>(arr1);
minArr2 = new CopyOnWriteArrayList<>(arr2);
}
sm.updateSum(arr1, arr2);
System.out.println("Swapping : " + sum1 + " - " + sum2 + " = " + diff + " : minDiff = " + minDiff);
k--;
}
k--;
}
System.out.println(minArr1 + " : " + minArr2 + " = " + minDiff);
}
private void updateSum(CopyOnWriteArrayList<Integer> arr1, CopyOnWriteArrayList<Integer> arr2) {
SmallestDifference sm1 = new SmallestDifference();
sum1 = sm1.getSum(arr1);
sum2 = sm1.getSum(arr2);
}
private int findMin(List<Integer> biggerArr2) {
Integer min = biggerArr2.get(0);
for (Integer integer : biggerArr2) {
if(min > integer) {
min = integer;
}
}
return min;
}
private int getSum(CopyOnWriteArrayList<Integer> arr) {
int sum = 0;
for (Integer val : arr) {
sum += val;
}
return sum;
}
private void swap(CopyOnWriteArrayList<Integer> arr1, CopyOnWriteArrayList<Integer> arr2) {
int l1 = arr1.size(), l2 = arr2.size(), temp2 = arr2.get(l2 - 1), temp1 = arr1.get(l1 - 1);
arr1.remove(l1 - 1);
arr1.add(temp2);
arr2.remove(l2 - 1);
arr2.add(temp1);
System.out.println(arr1 + " : " + arr2);
}
private void swich(CopyOnWriteArrayList<Integer> arr1, CopyOnWriteArrayList<Integer> arr2) {
Integer e;
if (sum1 > sum2) {
e = this.findElementJustLessThanMinDiff(arr1);
arr1.remove(e);
arr2.add(e);
} else {
e = this.findElementJustLessThanMinDiff(arr2);
arr2.remove(e);
arr1.add(e);
}
System.out.println(arr1 + " : " + arr2);
}
private Integer findElementJustLessThanMinDiff(CopyOnWriteArrayList<Integer> arr1) {
Integer e = arr1.get(0);
int tempDiff = diff - e;
for (Integer integer : arr1) {
if (diff > integer && (diff - integer) < tempDiff) {
e = integer;
tempDiff = diff - e;
}
}
return e;
}
}
A possible solution here- https://stackoverflow.com/a/31228461/4955513
This Java program seems to solve this problem, provided one condition is fulfilled- that there is one and only one solution to the problem.
I'll convert this problem to subset sum problem
let's take array int[] A = { 10,20,15,5,25,33 };
it should be divided into {25 20 10} and { 33 20 } and answer is 55-53=2
Notations : SUM == sum of whole array
sum1 == sum of subset1
sum2 == sum of subset1
step 1: get sum of whole array SUM=108
step 2: whichever way we divide our array into two part one thing will remain true
sum1+ sum2= SUM
step 3: if our intention is to get minimum sum difference then sum1 and sum2 should be near SUM/2 (example sum1=54 and sum2=54 then diff=0 )
steon 4: let's try combinations
sum1 = 54 AND sum2 = 54 (not possible to divide like this)
sum1 = 55 AND sum2 = 53 (possible and our solution, should break here)
sum1 = 56 AND sum2 = 52
sum1 = 57 AND sum2 = 51 .......so on
pseudo code
SUM=Array.sum();
sum1 = SUM/2;
sum2 = SUM-sum1;
while(true){
if(subSetSuMProblem(A,sum1) && subSetSuMProblem(A,sum2){
print "possible"
break;
}
else{
sum1++;
sum2--;
}
}
Java code for the same
import java.util.ArrayList;
import java.util.List;
public class MinimumSumSubsetPrint {
public static void main(String[] args) {
int[] A = {10, 20, 15, 5, 25, 32};
int sum = 0;
for (int i = 0; i < A.length; i++) {
sum += A[i];
}
subsetSumDynamic(A, sum);
}
private static boolean subsetSumDynamic(int[] A, int sum) {
int n = A.length;
boolean[][] T = new boolean[n + 1][sum + 1];
// sum2[0][0]=true;
for (int i = 0; i <= n; i++) {
T[i][0] = true;
}
for (int i = 1; i <= n; i++) {
for (int j = 1; j <= sum; j++) {
if (A[i - 1] > j) {
T[i][j] = T[i - 1][j];
} else {
T[i][j] = T[i - 1][j] || T[i - 1][j - A[i - 1]];
}
}
}
int sum1 = sum / 2;
int sum2 = sum - sum1;
while (true) {
if (T[n][sum1] && T[n][sum2]) {
printSubsets(T, sum1, n, A);
printSubsets(T, sum2, n, A);
break;
} else {
sum1 = sum1 - 1;
sum2 = sum - sum1;
System.out.println(sum1 + ":" + sum2);
}
}
return T[n][sum];
}
private static void printSubsets(boolean[][] T, int sum, int n, int[] A) {
List<Integer> sumvals = new ArrayList<Integer>();
int i = n;
int j = sum;
while (i > 0 && j > 0) {
if (T[i][j] == T[i - 1][j]) {
i--;
} else {
sumvals.add(A[i - 1]);
j = j - A[i - 1];
i--;
}
}
System.out.println();
for (int p : sumvals) {
System.out.print(p + " ");
}
System.out.println();
}
}
Here is recursive approach
def helper(arr,sumCal,sumTot,n):
if n==0:
return abs(abs(sumCal-sumTot)-sumCal)
return min(helper(arr,sumCal+arr[n-1],sumTot,n-1),helper(arr,sumCal,sumTot,n-1))
def minimum_subset_diff(arr,n):
sum=0
for i in range(n):
sum+=arr[i]
return helper(arr,0,sum,n)
Here is a Top down Dynamic approach to reduce the time complexity
dp=[[-1]*100 for i in range(100)]
def helper_dp(arr,sumCal,sumTot,n):
if n==0:
return abs(abs(sumCal-sumTot)-sumCal)
if dp[n][sumTot]!=-1:
return dp[n][sumTot]
return min(helper_dp(arr,sumCal+arr[n-1],sumTot,n-1),helper_dp(arr,sumCal,sumTot,n-1))
def minimum_subset_diff_dp(arr,n):
sum=0
for i in range(n):
sum+=arr[i]
return helper_dp(arr,0,sum,n)
int ModDiff(int a, int b)
{
if(a < b)return b - a;
return a-b;
}
int EqDiv(int *a, int l, int *SumI, int *SumE)
{
static int tc = 0;
int min = ModDiff(*SumI,*SumE);
for(int i = 0; i < l; i++)
{
swap(a,0,i);
a++;
int m1 = EqDiv(a, l-1, SumI,SumE);
a--;
swap(a,0,i);
*SumI = *SumI + a[i];
*SumE = *SumE - a[i];
swap(a,0,i);
a++;
int m2 = EqDiv(a,l-1, SumI,SumE);
a--;
swap(a,0,i);
*SumI = *SumI - a[i];
*SumE = *SumE + a[i];
min = min3(min,m1,m2);
}
return min;
}
call the function with SumI =0 and SumE= sumof all the elements in a.
This O(n!) solution does compute the way we can divide the given array into 2 parts such the difference is minimum.
But definitely not practical due to the n! time complexity looking to improve this using DP.
#include<bits/stdc++.h>
using namespace std;
bool ison(int i,int x)
{
if((i>>x) & 1)return true;
return false;
}
int main()
{
// cout<<"enter the number of elements : ";
int n;
cin>>n;
int a[n];
for(int i=0;i<n;i++)
cin>>a[i];
int sumarr1[(1<<n)-1];
int sumarr2[(1<<n)-1];
memset(sumarr1,0,sizeof(sumarr1));
memset(sumarr2,0,sizeof(sumarr2));
int index=0;
vector<int>v1[(1<<n)-1];
vector<int>v2[(1<<n)-1];
for(int i=1;i<(1<<n);i++)
{
for(int j=0;j<n;j++)
{
if(ison(i,j))
{
sumarr1[index]+=a[j];
v1[index].push_back(a[j]);
}
else
{
sumarr2[index]+=a[j];
v2[index].push_back(a[j]);
}
}index++;
}
int ans=INT_MAX;
int ii;
for(int i=0;i<index;i++)
{
if(abs(sumarr1[i]-sumarr2[i])<ans)
{
ii=i;
ans=abs(sumarr1[i]-sumarr2[i]);
}
}
cout<<"first partitioned array : ";
for(int i=0;i<v1[ii].size();i++)
{
cout<<v1[ii][i]<<" ";
}
cout<<endl;
cout<<"2nd partitioned array : ";
for(int i=0;i<v2[ii].size();i++)
{
cout<<v2[ii][i]<<" ";
}
cout<<endl;
cout<<"minimum difference is : "<<ans<<endl;
}
Many answers mentioned about getting an 'approximate' solution in a very acceptable time bound . But since it is asked in an interview , I dont expect they need an approximation algorithm. Also I dont expect they need a naive exponential algorithm either.
Coming to the problem , assuming the maximum value of sum of numbers is known , it can infact be solved in polynomial time using dynamic programming. Refer this link
https://people.cs.clemson.edu/~bcdean/dp_practice/dp_4.swf
HI I think This Problem can be solved in Linear Time on a sorted array , no Polynomial Time is required , rather than Choosing Next Element u can choose nest two Element and decide which side which element to go. in This Way
in this way minimize the difference, let suppose
{0,1,5,6} ,
choose {0,1}
{0} , {1}
choose 5,6
{0,6}, {1,5}
but still that is not exact solution , now at the end there will be difference of sum in 2 array let suppose x
but there can be better solution of difference of (less than x)
for that Find again 1 greedy approach over sorted half sized array
and move x/2(or nearby) element from 1 set to another or exchange element of(difference x/2) so that difference can be minimized***