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The Minimum Size Subarray Sum problem:
given an array of n positive integers and a positive integer s, find the minimal length of a subarray of which the sum ≥ s. If there isn't one, return 0 instead.
For example, given the array [2,3,1,2,4,3] and s = 7,
the subarray [4,3] has the minimal length under the problem constraint.
The following is my solution:
public int minSubArrayLen(int s, int[] nums) {
long sum = 0;
int a = 0;
if (nums.length < 1)
return 0;
Arrays.sort(nums);
for (int i = nums.length-1; i >= 0; i--) {
sum += nums[i];
a++;
if (sum>=s)
break;
}
if (sum < s) {
return 0;
}
return a;
}
This solution was not accepted because it did not pass the following test case:
697439
[5334,6299,4199,9663,8945,3566,9509,3124,6026,6250,7475,5420,9201,9501,38,5897,4411,6638,9845,161,9563,8854,3731,5564,5331,4294,3275,1972,1521,2377,3701,6462,6778,187,9778,758,550,7510,6225,8691,3666,4622,9722,8011,7247,575,5431,4777,4032,8682,5888,8047,3562,9462,6501,7855,505,4675,6973,493,1374,3227,1244,7364,2298,3244,8627,5102,6375,8653,1820,3857,7195,7830,4461,7821,5037,2918,4279,2791,1500,9858,6915,5156,970,1471,5296,1688,578,7266,4182,1430,4985,5730,7941,3880,607,8776,1348,2974,1094,6733,5177,4975,5421,8190,8255,9112,8651,2797,335,8677,3754,893,1818,8479,5875,1695,8295,7993,7037,8546,7906,4102,7279,1407,2462,4425,2148,2925,3903,5447,5893,3534,3663,8307,8679,8474,1202,3474,2961,1149,7451,4279,7875,5692,6186,8109,7763,7798,2250,2969,7974,9781,7741,4914,5446,1861,8914,2544,5683,8952,6745,4870,1848,7887,6448,7873,128,3281,794,1965,7036,8094,1211,9450,6981,4244,2418,8610,8681,2402,2904,7712,3252,5029,3004,5526,6965,8866,2764,600,631,9075,2631,3411,2737,2328,652,494,6556,9391,4517,8934,8892,4561,9331,1386,4636,9627,5435,9272,110,413,9706,5470,5008,1706,7045,9648,7505,6968,7509,3120,7869,6776,6434,7994,5441,288,492,1617,3274,7019,5575,6664,6056,7069,1996,9581,3103,9266,2554,7471,4251,4320,4749,649,2617,3018,4332,415,2243,1924,69,5902,3602,2925,6542,345,4657,9034,8977,6799,8397,1187,3678,4921,6518,851,6941,6920,259,4503,2637,7438,3893,5042,8552,6661,5043,9555,9095,4123,142,1446,8047,6234,1199,8848,5656,1910,3430,2843,8043,9156,7838,2332,9634,2410,2958,3431,4270,1420,4227,7712,6648,1607,1575,3741,1493,7770,3018,5398,6215,8601,6244,7551,2587,2254,3607,1147,5184,9173,8680,8610,1597,1763,7914,3441,7006,1318,7044,7267,8206,9684,4814,9748,4497,2239]
The expected answer is 132 but my output was 80.
Does anyone have any idea what went wrong with my algorithm/code?
I will simply explain the flaw in the logic rather giving the correct logic to handle the problem statement
You are taking the numbers in a specific sequence and then adding them for comparison. Quite easily the case can be different where you take numbers in random order to get the exact sum.
For example [2,3,1,2,4,3] and s = 7.
Based on your logic
Step 1-> Sort the numbers and you get [1,2,2,3,3,4]
Step 2-> You pick last 2 number (3,4) to get your sum 7
Lets change the sum to 8
From Step 2-> You get 3+3+4 = 10 so u break out of the loop. After this step you return a = 2
Flaw here is 4+3+1 also makes 8 something your logic skips.
Same way 3+3+2 is also possible solution to achieve 8.
You sorting the array is first flaw in the logic itself. If you consider subarray of existing arrangement, sorting changes the arrangement therefore you will never be able to get the expected solution.
I came across a problem from a recent competition.
I was unable to figure out a solution, and no editorial for the question is yet available.
Question Link
I am quoting the problem statement here also in case the link doesn't work.
Find the number of integers n which are greater than or equal to A and less than or equal to B (A<= n <=B) and the decimal representation of 2^n ends in n.
Ex: 2^36 = 68719476736 which ends in “36”.
INPUT
The first line contains an integer T i.e. number of test cases. T lines follow, each containing two integers A and B.
Constraints
1 <= T <= 10^5
A<=B
A,B <= 10^150
OUTPUT
Print T lines each containing the answer to the corresponding testcase.
Sample Input
2
36 36
100 500
Sample Output
1
0
As often happens on programming competitions I have come up with an heuristics I have not proven, but seems plausible. I have written a short program to find the numbers up to 1000000 and they are:
36
736
8736
48736
948736
Thus my theory is the following - each consecutive number is suffixed with the previous one and only adds one digit. Hope this will set you on the right track for the problem. Note that if my assumption is right than you only need to find 150 numbers and finding each consecutive number requires checking 9 digits that may be added.
A general advice for similar problems - always try to find the first few numbers and think of some relation.
Also often it happens on a competition that you come up with a theory like the one I propose above, but have no time to prove it. You can't afford the time to prove it. Simply hope you are right and code.
EDIT: I believe I was able to prove my conjecture above(in fact I have missed some numbers -see end of the post). First let me point out that as v3ga states in a comment the algorithm above works up until 75353432948736 as no digit can be prepended to make the new number "interesting" as per the definition you give. However I completely missed another option - you may prepend some number of 0 and then add a non-zero digit.
I will now proof a lemma:
Lemma: if a1a2...an is an interesting number and n is more than 3, then a2...an also is interesting.
Proof:
2a1a2...an = 2a1*10n - 1*2a2a2...an
Now I will prove that 2a1*10n - 1*2a2a2...an is comparable to 2a2a2...an modulo 10n-1.
To do that lets prove that 2a1*10n - 1*2a2a2...an - 2a2a2...an is divisible by 10n-1.
2a1*10n - 1*2a2a2...an - 2a2a2...an =
2a2a2...an * (2a1*10n - 1 - 1)
a2a2...an is more than n-1 for the values we consider.
Thus all that's left to prove to have 10n-1 dividing the difference is that 5n-1 divides 2a1*10n - 1 - 1.
For this I will use Euler's theorem:
2phi(5n-1) = 1 (modulo 5n-1).
Now phi(5n-1) = 4*(5n-2) and for n >= 3 4*(5n-2) will divide a1*10n - 1(actually even solely 10n - 1).
Thus 2a1*10n - 1 gives remainder 1 modulo 5n-1 and so 5n-1 divides 2a1*10n - 1 - 1.
Consequently 10n-1 divides 2a2a2...an * (2a1*10n - 1 - 1) and so the last n - 1 digits of 2a1a2a2...an and 2a2a3a4...an are the same.
Now as a1a2a2...an is interesting the last n digits of 2a1a2a2...an are a1a2a2...an and so the last n-1 digits of 2a2a3a4...an are a2a3a4...an and consequently a2a3a4...an is also interesting. QED.
Use this lemma and you will be able to solve the problem. Please note that you may also prepend some zeros and then add a non-zero number.
In general, you can try solving these problems by finding some pattern in the output. Our team got this problem accepted at the contest. Our approach was to find a general pattern in the values that satisfy the criteria. If you print the first few such digits, then you will find the following pattern
36
736
8736
48736
948736
Thus the next number after 948736 should be of 7 digits and can be any one of 1948736, 2948736, 3948736, 4948736, 5948736, 6948736, 7948736, 8948736, 9948736. Thus check which value is valid and you have the next number. Continuing in this fashion you can back yourself to get all the 150 numbers.
But there is a problem here. There will be some numbers that do not immediately follow from the previous number by appending '1' to '9'. To counter this you can now start appending values from 10 to 99 and now check if there is a valid number or not. If there is still no valid number, then again try appending numbers from 100 to 999.
Now employing this hack, you will get all the 137 values that satisfy the criterion given in the question and easily answer all the queries. For example, working java code that implements this is shown here. It prints all the 137 values.
import java.io.*;
import java.math.*;
import java.util.*;
class Solution
{
public static void main(String[] args)throws java.lang.Exception{
new Solution().run();
}
void run()throws java.lang.Exception{
BigInteger[] powers = new BigInteger[152];
powers[0] = one;
for(int i=1; i<=150; i++){
powers[i] = powers[i-1].multiply(ten);
}
BigInteger[] answers = new BigInteger[152];
answers[2] = BigInteger.valueOf(36);
answers[3] = BigInteger.valueOf(736);
int last = 3;
for(int i=4; i<=150; i++){
int dif = i-last;
BigInteger start = ten.pow(dif-1);
BigInteger end = start.multiply(ten);
while(start.compareTo(end) < 0){
BigInteger newVal = powers[last].multiply(start);
newVal = newVal.add(answers[last]);
BigInteger modPow = pow(two, newVal, powers[i]);
if(modPow.equals(newVal)){
answers[i] = newVal;
System.out.println(answers[i]);
last = i;
break;
}
start = start.add(one);
}
}
}
BigInteger pow(BigInteger b, BigInteger e, BigInteger mod){
if(e.equals(zero)){
return one;
}
if(e.mod(two).equals(zero)){
BigInteger x = pow(b, e.divide(two), mod);
x = x.multiply(x).mod(mod);
return x;
}else{
BigInteger x = pow(b, e.divide(two), mod);
x = x.multiply(x).mod(mod);
x = x.multiply(two).mod(mod);
return x;
}
}
BigInteger ten = BigInteger.valueOf(10);
BigInteger zero = BigInteger.ZERO;
BigInteger one = BigInteger.ONE;
BigInteger two = BigInteger.valueOf(2);
}
This is very interesting property. During the contest, I found that 36 was the only number under 500 checking with python...
The property is : 2^36 last two digits are 36, last three digits are 736, so next number is 736. 2^736 has last three digits as 736, and next number is 8376...
And the series is : 36 , 736 , 8736 , 48736 , 948736 ...
And then started with BigInt class in C++.
But alas there was no time, and 4th problem wasn't solved. But after the contest, we did it in python.
here's link : Ideone it!
def powm(i):
j = 10
a = 1
while i:
if i % 2:
a = a * j
i /= 2
j *= j
return a
def power(n, i):
m = powm(i)
y = 1
x = 2
while n:
if n % 2 == 1:
y = y * x % m
x = x * x % m
n /= 2
return y
mylist = []
mylist.append(power(36, 2))
n = mylist[0]
print(n)
for i in range(3, 170):
p = power(n, i)
print p
if p != n:
mylist.append(p)
n = p
t = input()
while t:
x = raw_input().split(" ")
a = int(x[0])
b = int(x[1])
i = 0
#while i <= 150:
#print mylist[i]
#i += 1
#print power(8719476736,14)
while mylist[i] < a:
i += 1
ans = 0
while mylist[i] <= b:
i += 1
ans += 1
print ans
t -= 1
The final digits start to repeat after 20 increments. So for any n with the final digit 1, the final digit of the answer will be 2. So most values of n can be eliminated immediately.
2^1 = 2
2^21 = 2097152
2^101 = 2535301200456458802993406410752
2^2 = 4
2^22 = 4194304
2^42 = 4398046511104
In fact only two possibilities share a final digit:
2^14 = 16384
2^16 = 65536
2^34 = 17179869184
2^36 = 68719476736
If n is 14+20x or 16+20x, then it might work, so you'll need to check it. Otherwise, it cannot work.
I am not very good with such problems. But modular exponentiation appears to be key in your case.
Repeat for all n in the range A to B:
1. Find k, the no of digits in n. This can be done in O(logn)
2. Find 2^n (mod 10^k) using modular exponentiation and check if it is equal to n. This'll take O(n) time. (actually, O(n) multiplications)
EDIT
Actually, don't repeat the whole process for each n. Given 2^n (mod 10^k), we can find 2^(n+1) (mod 10^k) in constant time. Use this fact to speed it up further
EDIT - 2
This doesn't work for such large range.
Started to learn recursion and I am stuck with this simple problem. I believe that there are more optimized ways to do this but first I'm trying to learn the bruteforce approach.
I have bag A and bag B and have n items each one with some time (a float with two decimal places). The idea is to distribute the items by the two bags and obtain the minimum difference in the two bags. The idea is to try all possible outcomes.
I thought only in one bag (lets say bag A) since the other bag will contain all the items that are not in the bag A and therefore the difference will be the absolute value of total times sum - 2 * sum of the items time that are in the bag A.
I'm calling my recursive function like this:
min = total_time;
recursive(0, items_number - 1, 0);
And the code for the function is this:
void recursive(int index, int step, float sum) {
sum += items_time[index];
float difference = fabs(total_time - 2 * sum);
if (min > difference) {
min = difference;
}
if (!(min == 0.00 || step == 1 || sum > middle_time)) {
int i;
for (i = 0; i < items_number; i++) {
if (i != index) {
recursive(i, step - 1, sum);
}
}
}
}
Imagine I have 4 items with the times 1.23, 2.17 , 2.95 , 2.31
I'm getting the result 0.30. I believe that this is the correct result but I'm almost certain that if it is is pure change because If I try with bigger cases the program stops after a while. Probably because the recursion tree gets to bigger.
Can someone point me in some direction?
Okay, after the clarification, let me (hopefully) point you to a direction:
Let's assume that you know what n is, mentioned in n items. In your example, it was 2n is 4, making n = 2. Let's pick another n, let it be 3 this time, and our times shall be:
1.00
2.00
3.00
4.00
5.00
6.00
Now, we can already tell what the answer is; what you had said is all correct, optimally each of the bags will have their n = 3 times summed up to middle_time, which is 21 / 2 = 10.5 in this case. Since integers may never sum up to numbers with decimal points, 10.5 : 10.5 may never be achieved in this example, but 10 : 11 can, and you can have 10 through 6.00 + 3.00 + 1.00 (3 elements), so... yeah, the answer is simply 1.
How would you let a computer calculate it? Well; recall what I said at the beginning:
Let us assume that you know what n is.
In that case a naive programmer would probably simply put all those inside 2 or 3 nested for loops. 2 if he/she knew that the other half will be determined when you pick a half (by simply fixing the very first element in our group, since that element is to be included in one of the groups), like you also know; 3 if he/she didn't know that. Let's make it with 2:
...
float difference;
int i;
for ( i = 1; i < items_number; i++ ) {
sum = items_time[0] + items_time[i];
int j;
for ( j = i + 1; j < items_number; j++ ) {
sum += items_time[j];
difference = fabs( total_time - 2 * sum );
if ( min > difference ) {
min = difference;
}
}
}
...
Let me comment about the code a little for faster understanding: On the first cycle, it will add up the 0th time, the 1st time and then the 2nd time as you may see; then it will do the same check you had made (calculate the difference and compare the it with min). Let us call this the 012 group. The next group that will be checked will be 013, then 014, then 015; then 023, and so on... Each possible combination that will split the 6 into two 3s will be checked.
This operation shouldn't be any tiresome for the computer to issue. Even with this simple approach, the maximum amount of tries will be the amount of combinations of 3 you could have with 6 unique elements divided by 2. In maths, people denote this as C(6, 3), which evaluates to (6 * 5 * 4) / (3 * 2 * 1) = 20; divided by 2, so it's 10.
My guess is that the computer wouldn't make it a problem even if n was 10, making the amount of combinations as high as C(20, 10) / 2 = 92 378. It would, however, be a problem for you to write down 9 nested for loops by hand...
Anyway, the good thing is, you can recursively nest these loops. Here I will end my guidance. Since you apparently are studying for the recursion already, it wouldn't be good for me to offer a solution at this point. I can assure you that it is do-able.
Also the version I have made on my end can do it within a second for up to items_number = 22, without having made any optimizations; simply with brute force. That makes 352 716 combinations, and my machine is just a simple Windows tablet...
Your problem is called the Partition Problem. It is NP-hard and after some point, it will take a very long time to complete: the tree gets exponentially bigger as the number of cases to test grows.
The partition problem is well known and well documented over the internet. There exists some optimized solution
Your approach is not the naive brute-force approach, which would just walk through the list of items and put it into bag A and bag B recursively, chosing the case with the minimum difference, for example:
double recurse(double arr[], int n, double l, double r)
{
double ll, rr;
if (n == 0) return fabs(l - r);
ll = recurse(arr + 1, n - 1, l + *arr, r);
rr = recurse(arr + 1, n - 1, l, r + *arr);
if (ll > rr) return rr;
return ll;
}
(This code is very naive - it doesn't quite early on clearly non-optimal cases and it also wastes time by calculating every case twice with bags A and B swapped. it is brute force, however.)
You maximum recursion depth is the numer of items n, you call the recursive function 2^n - 1 times.
In your code, you can put the same item into a bag over and over:
for (i = 0; i < number_of_pizzas; i++) {
if (i != index) {
recursive(i, step - 1, sum);
}
}
This loop prevents you from treating the current item, but will happily treat items that have been put into the bag in earlier recursions for a second (or third) time. If you want to use that approach, you must keep a state of which item is in which bag.
Also, I don't understand your step. You start with step - 1 and stop recursion when step == 1. That means you are considering n - 2 items. I understand that the other items are in the other bag, but that's a weird condition that won't let you find the solution to, say, {8.0, 2.4, 2.4, 2.8}.
This question already has answers here:
Algorithm to find elements best fitting in a particular amount
(5 answers)
how do you calculate the minimum-coin change for transaction?
(3 answers)
Closed 9 years ago.
So here is the problem:
Given input = [100 80 66 25 4 2 1], I need to find the best combination to give me 50.
Looking at this, the best would be 25+25 = 50, so I need 2 elements from the array.
Other combinations include 25+4+4+4+4+4+4+1 and 25+4+4+4+4+4+2+2+1.. etc etc
I need to find all the possibilities which gives me the sum on a value I want.
EDIT: As well as the best possibility (one with least number of terms)
Here is what I have done thus far:
First build a new array (simple for loop which cycles through all elements and stores in a new temp array), check for all elements higher than my array (so for input 50, the elements 100,80,66 are higher, so discard them and then my new array is [25 4 2 1]). Then, from this, I need to check combinations.
The first thing I do is a simple if statement checking if any array elements EXACTLY match the number I want. So if I want 50, I check if 50 is in the array, if not, I need to find combinations.
My problem is, I'm not entirely sure how to find every single combination. I have been struggling trying to come up with an algorithm for a while but I always just end up getting stumped.
Any help/tips would be much appreciated.
PS - we can assume the array is always sorted in order from LARGEST to SMALLEST value.
This is the kind of problem that dynamic programming is meant to solve.
Create an array with with indices, 1 to 50. Set each entry to -1. For each element that is in your input array, set that element in the array to 0. Then, for each integer n = 2 to 50, find all possible ways to sum to n. The number of sums required is the minimum of the two addends plus 1. At the end, get the element at index 50.
Edit: Due to a misinterpretation of the question, I first answered with an efficient way to calculate the number of possibilities (instead of the possibilities themself) to get N using values from a given set. That solution can be found at the bottom of this post as a reference for other people, but first I'll give a proper answer to your questions.
Generate all possibilities, count them and give the shortest one
When generating a solution, you consider each element from the input array and ask yourself "should I use this in my solution or not?". Since we don't know the answer until after the calculation, we'll just have to try out both using it and not using it, as can be seen in the recursion step in the code below.
Now, to avoid duplicates and misses, we need to be a bit careful with the parameters for the recursive call. If we use the current element, we should also allow it to be used in the next step, because the element may be used as many times as possible. Therefore, the first parameter in this recursive call is i. However, if we decide to not use the element, we should not allow it to be used in the next step, because that would be a duplicate of the current step. Therefore, the first parameter in this recursive call is i+1.
I added an optional bound (from "branch and bound") to the algorithm, that will stop expanding the current partial solution if it is known that this solution will never be shorter then the shortest solution found so far.
package otherproblems;
import java.util.Deque;
import java.util.LinkedList;
public class GeneratePossibilities
{
// Input
private static int n = 50;
// If the input array is sorted ascending, the shortest solution is
// likely to be found somewhere at the end.
// If the input array is sorted descending, the shortest solution is
// likely to be found somewhere in the beginning.
private static int[] input = {100, 80, 66, 25, 4, 2, 1};
// Shortest possibility
private static Deque<Integer> shortest;
// Number of possibilities
private static int numberOfPossibilities;
public static void main(String[] args)
{
calculate(0, n, new LinkedList<Integer>());
System.out.println("\nAbove you can see all " + numberOfPossibilities +
" possible solutions,\nbut this one's the shortest: " + shortest);
}
public static void calculate(int i, int left, Deque<Integer> partialSolution)
{
// If there's nothing left, we reached our target
if (left == 0)
{
System.out.println(partialSolution);
if (shortest == null || partialSolution.size() < shortest.size())
shortest = new LinkedList<Integer>(partialSolution);
numberOfPossibilities++;
return;
}
// If we overshot our target, by definition we didn't reach it
// Note that this could also be checked before making the
// recursive call, but IMHO this gives a cleaner recursion step.
if (left < 0)
return;
// If there are no values remaining, we didn't reach our target
if (i == input.length)
return;
// Uncomment the next two lines if you don't want to keep generating
// possibilities when you know it can never be a better solution then
// the one you have now.
// if (shortest != null && partialSolution.size() >= shortest.size())
// return;
// Pick value i. Note that we are allowed to pick it again,
// so the argument to calculate(...) is i, not i+1.
partialSolution.addLast(input[i]);
calculate(i, left-input[i], partialSolution);
// Don't pick value i. Note that we are not allowed to pick it after
// all, so the argument to calculate(...) is i+1, not i.
partialSolution.removeLast();
calculate(i+1, left, partialSolution);
}
}
Calculate the number of possibilities efficiently
This is a nice example of dynamic programming. What you need to do is figure out how many possibilities there are to form the number x, using value y as the last addition and using only values smaller than or equal to y. This gives you a recursive formula that you can easily translate to a solution using dynamic programming. I'm not quite sure how to write down the mathematics here, but since you weren't interested in them anyway, here's the code to solve your question :)
import java.util.Arrays;
public class Possibilities
{
public static void main(String[] args)
{
// Input
int[] input = {100, 80, 66, 25, 4, 2, 1};
int n = 50;
// Prepare input
Arrays.sort(input);
// Allocate storage space
long[][] m = new long[n+1][input.length];
for (int i = 1; i <= n; i++)
for (int j = 0; j < input.length; j++)
{
// input[j] cannot be the last value used to compose i
if (i < input[j])
m[i][j] = 0;
// If input[j] is the last value used to compose i,
// it must be the only value used in the composition.
else if (i == input[j])
m[i][j] = 1;
// If input[j] is the last value used to compose i,
// we need to know the number of possibilities in which
// i - input[j] can be composed, which is the sum of all
// entries in column m[i-input[j]].
// However, to avoid counting duplicates, we only take
// combinations that are composed of values equal or smaller
// to input[j].
else
for (int k = 0; k <= j; k++)
m[i][j] += m[i-input[j]][k];
}
// Nice output of intermediate values:
int digits = 3;
System.out.printf(" %"+digits+"s", "");
for (int i = 1; i <= n; i++)
System.out.printf(" %"+digits+"d", i);
System.out.println();
for (int j = 0; j < input.length; j++)
{
System.out.printf(" %"+digits+"d", input[j]);
for (int i = 1; i <= n; i++)
System.out.printf(" %"+digits+"d", m[i][j]);
System.out.println();
}
// Answer:
long answer = 0;
for (int i = 0; i < input.length; i++)
answer += m[n][i];
System.out.println("\nThe number of possibilities to form "+n+
" using the numbers "+Arrays.toString(input)+" is "+answer);
}
}
This is the integer knapsack problem, which is one your most common NP-complete problems out there; if you are into algorithm design/study check those out. To find the best I think you have no choice but to compute them all and keep the smallest one.
For the correct solution there is a recursive algorithm that is pretty simple to put together.
import org.apache.commons.lang.ArrayUtils;
import java.util.*;
public class Stuff {
private final int target;
private final int[] steps;
public Stuff(int N, int[] steps) {
this.target = N;
this.steps = Arrays.copyOf(steps, steps.length);
Arrays.sort(this.steps);
ArrayUtils.reverse(this.steps);
this.memoize = new HashMap<Integer, List<Integer>>(N);
}
public List<Integer> solve() {
return solveForN(target);
}
private List<Integer> solveForN(int N) {
if (N == 0) {
return new ArrayList<Integer>();
} else if (N > 0) {
List<Integer> temp, min = null;
for (int i = 0; i < steps.length; i++) {
temp = solveForN(N - steps[i]);
if (temp != null) {
temp.add(steps[i]);
if (min == null || min.size() > temp.size()) {
min = temp;
}
}
}
return min;
} else {
return null;
}
}
}
It is based off the fact that to "get to N" you to have come from N - steps[0], or N - steps1, ...
Thus you start from your target total N and subtract one of the possible steps, and do it again until you are at 0 (return a List to specify that this is a valid path) or below (return null so that you cannot return an invalid path).
The complexity of this correct solution is exponential! Which is REALLY bad! Something like O(k^M) where M is the size of the steps array and k a constant.
To get a solution to this problem in less time than that you will have to use a heuristic (approximation) and you will always have a certain probability to have the wrong answer.
You can make your own implementation faster by memorizing the shortest combination seen so far for all targets (so you do not need to recompute recur(N, _, steps) if you already did). This approach is called Dynamic Programming. I will let you do that on your own (very fun stuff and really not that complicated).
Constraints of this solution : You will only find the solution if you guarantee that the input array (steps) is sorted in descending order and that you go through it in that order.
Here is a link to the general Knapsack problem if you also want to look approximation solutions: http://en.wikipedia.org/wiki/Knapsack_problem
You need to solve each sub-problem and store the solution. For example:
1 can only be 1. 2 can be 2 or 1+1. 4 can be 4 or 2+2 or 2+1+1 or 1+1+1+1. So you take each sub-solution and store it, so when you see 25=4+4+4+4+4+4+1, you already know that each 4 can also be represented as one of the 3 combinations.
Then you have to sort the digits and check to avoid duplicate patterns since, for example, (2+2)+(2+2)+(2+2)+(1+1+1+1)+(1+1+1+1)+(1+1+1+1) == (2+1+1)+(2+1+1)+(2+1+1)+(2+1+1)+(2+1+1)+(2+1+1). Six 2's and twelve 1's in both cases.
Does that make sense?
Recursion should be the easiest way to solve this (Assuming you really want to find all the solutions to the problem). The nice thing about this approach is, if you want to just find the shortest solution, you can add a check on the recursion and find just that, saving time and space :)
Assuming an element i of your array is part of the solution, you can solve the subproblem of finding the elements that sums to n-i. If we add an ordering to our solution, for example the numbers in the sum must be from the greater to the smallest, we have a way to find unique solutions.
This is a recursive solution in C#, it should be easy to translate it in java.
public static void RecursiveSum(int n, int index, List<int> lst, List<int> solution)
{
for (int i = index; i < lst.Count; i++)
{
if (n == 0)
{
Console.WriteLine("");
foreach (int j in solution)
{
Console.Write(j + " ");
}
}
if (n - lst[i] >= 0)
{
List<int> tmp = new List<int>(solution);
tmp.Add(lst[i]);
RecursiveSum(n - lst[i], i, lst, tmp);
}
}
}
You call it with
RecursiveSum(N,0,list,new List<int>());
where N is the sum you are looking for, 0 shouldn't be changed, list is your list of allowed numbers, and the last parameter shouldn't be changed either.
The problem you pose is interesting but very complex. I'd approach this by using something like OptaPlanner(formerly Drools Planner). It's difficult to describe a full solution to this problem without spending significant time, but with optaplanner you can also get "closest fit" type answers and can have incremental "moves" that would make solving your problem more efficient. Good luck.
This is a solution in python: Ideone link
# Start of tsum function
def tsum(currentSum,total,input,record,n):
if total == N :
for i in range(0,n):
if record[i]:
print input[i]
i = i+1
for i in range(i,n):
if record[i]:
print input[i]
print ""
return
i=currentSum
for i in range(i,n):
if total+input[i]>sum :
continue
if i>0 and input[i]==input[i-1] and not record[i-1] :
continue
record[i]=1
tsum(i+1,total+input[i],input,record,l)
record[i]=0
# end of function
# Below portion will be main() in Java
record = []
N = 5
input = [3, 2, 2, 1, 1]
temp = list(set(input))
newlist = input
for i in range(0, len(list(set(input)))):
val = N/temp[i]
for j in range(0, val-input.count(temp[i])):
newlist.append(temp[i])
# above logic was to create a newlist/input i.e [3, 2, 2, 1, 1, 1, 1, 1]
# This new list contains the maximum number of elements <= N
# for e.g appended three 1's as sum of new three 1's + existing two 1's <= N(5) where as
# did not append another 2 as 2+2+2 > N(5) or 3 as 3+3 > N(5)
l = len(input)
for i in range(0,l):
record.append(0)
print "all possibilities to get N using values from a given set:"
tsum(0,0,input,record,l)
OUTPUT: for set [3, 2, 2, 1, 1] taking small set and small N for demo purpose. But works well for higher N value as well.
For N = 5
all possibilities to get N using values from a given set:
3
2
3
1
1
2
2
1
2
1
1
1
1
1
1
1
1
For N = 3
all possibilities to get N using values from a given set:
3
2
1
1
1
1
Isn't this just a search problem? If so, just search breadth-first.
abstract class Numbers {
abstract int total();
public static Numbers breadthFirst(int[] numbers, int total) {
List<Numbers> stack = new LinkedList<Numbers>();
if (total == 0) { return new Empty(); }
stack.add(new Empty());
while (!stack.isEmpty()) {
Numbers nums = stack.remove(0);
for (int i : numbers) {
if (i > 0 && total - nums.total() >= i) {
Numbers more = new SomeNumbers(i, nums);
if (more.total() == total) { return more; }
stack.add(more);
}
}
}
return null; // No answer.
}
}
class Empty extends Numbers {
int total() { return 0; }
public String toString() { return "empty"; }
}
class SomeNumbers extends Numbers {
final int total;
final Numbers prev;
SomeNumbers(int n, Numbers prev) {
this.total = n + prev.total();
this.prev = prev;
}
int total() { return total; }
public String toString() {
if (prev.getClass() == Empty.class) { return "" + total; }
return prev + "," + (total - prev.total());
}
}
What about using the greedy algorithm n times (n is the number of elements in your array), each time popping the largest element off the list. E.g. (in some random pseudo-code language):
array = [70 30 25 4 2 1]
value = 50
sort(array, descending)
solutions = [] // array of arrays
while length of array is non-zero:
tmpValue = value
thisSolution = []
for each i in array:
while tmpValue >= i:
tmpValue -= i
thisSolution.append(i)
solutions.append(thisSolution)
array.pop_first() // remove the largest entry from the array
If run with the set [70 30 25 4 2 1] and 50, it should give you a solutions array like this:
[[30 4 4 4 4 4]
[30 4 4 4 4 4]
[25 25]
[4 4 4 4 4 4 4 4 4 4 4 4 2]
[2 ... ]
[1 ... ]]
Then simply pick the element from the solutions array with the smallest length.
Update: The comment is correct that this does not generate the correct answer in all cases. The reason is that greedy isn't always right. The following recursive algorithm should always work:
array = [70, 30, 25, 4, 3, 1]
def findSmallest(value, array):
minSolution = []
tmpArray = list(array)
while len(tmpArray):
elem = tmpArray.pop(0)
tmpValue = value
cnt = 0
while tmpValue >= elem:
cnt += 1
tmpValue -= elem
subSolution = findSmallest(tmpValue, tmpArray)
if tmpValue == 0 or subSolution:
if not minSolution or len(subSolution) + cnt < len(minSolution):
minSolution = subSolution + [elem] * cnt
return minSolution
print findSmallest(10, array)
print findSmallest(50, array)
print findSmallest(49, array)
print findSmallest(55, array)
Prints:
[3, 3, 4]
[25, 25]
[3, 4, 4, 4, 4, 30]
[30, 25]
The invariant is that the function returns either the smallest set for the value passed in, or an empty set. It can then be used recursively with all possible values of the previous numbers in the list. Note that this is O(n!) in complexity, so it's going to be slow for large values. Also note that there are numerous optimization potentials here.
I made a small program to help with one solution. Personally, I believe the best would be a deterministic mathematical solution, but right now I lack the caffeine to even think on how to implement it. =)
Instead, I went with a SAR approach. Stop and Reverse is a technique used on stock trading (http://daytrading.about.com/od/stou/g/SAR.htm), and is heavily used to calculate optimal curves with a minimal of inference. The Wikipedia entry for parabolical SAR goes like this:
'The Parabolic SAR is calculated almost independently for each trend
in the price. When the price is in an uptrend, the SAR emerges below
the price and converges upwards towards it. Similarly, on a
downtrend, the SAR emerges above the price and converges
downwards.'
I adapted it to your problem. I start with a random value from your series. Then the code enters a finite number of iterations.
I pick another random value from the series stack.
If the new value plus the stack sum is inferior to the target, then the value is added; if superior, then decreased.
I can go on for as much as I want until I satisfy the condition (stack sum = target), or abort if the cycle can't find a valid solution.
If successful, I record the stack and the number of iterations. Then I redo everything.
An EXTREMELY crude code follows. Please forgive the hastiness. Oh, and It's in C#. =)
Again, It does not guarantee that you'll obtain the optimal path; it's a brute force approach. It can be refined; detect if there's a perfect match for a target hit, for example.
public static class SAR
{
//I'm considering Optimal as the smallest signature (number of members).
// Once set, all future signatures must be same or smaller.
private static Random _seed = new Random();
private static List<int> _domain = new List<int>() { 100, 80, 66, 24, 4, 2, 1 };
public static void SetDomain(string domain)
{
_domain = domain.Split(',').ToList<string>().ConvertAll<int>(a => Convert.ToInt32(a));
_domain.Sort();
}
public static void FindOptimalSAR(int value)
{
// I'll skip some obvious tests. For example:
// If there is no odd number in domain, then
// it's impossible to find a path to an odd
// value.
//Determining a max path run. If the count goes
// over this, it's useless to continue.
int _maxCycle = 10;
//Determining a maximum number of runs.
int _maxRun = 1000000;
int _run = 0;
int _domainCount = _domain.Count;
List<int> _currentOptimalSig = new List<int>();
List<String> _currentOptimalOps = new List<string>();
do
{
List<int> currSig = new List<int>();
List<string> currOps = new List<string>();
int _cycle = 0;
int _cycleTot = 0;
bool _OptimalFound = false;
do
{
int _cursor = _seed.Next(_domainCount);
currSig.Add(_cursor);
if (_cycleTot < value)
{
currOps.Add("+");
_cycleTot += _domain[_cursor];
}
else
{
// Your situation doesn't allow for negative
// numbers. Otherwise, just enable the two following lines.
// currOps.Add("-");
// _cycleTot -= _domain[_cursor];
}
if (_cycleTot == value)
{
_OptimalFound = true;
break;
}
_cycle++;
} while (_cycle < _maxCycle);
if (_OptimalFound)
{
_maxCycle = _cycle;
_currentOptimalOps = currOps;
_currentOptimalSig = currSig;
Console.Write("Optimal found: ");
for (int i = 0; i < currSig.Count; i++)
{
Console.Write(currOps[i]);
Console.Write(_domain[currSig[i]]);
}
Console.WriteLine(".");
}
_run++;
} while (_run < _maxRun);
}
}
And this is the caller:
String _Domain = "100, 80, 66, 25, 4, 2, 1";
SAR.SetDomain(_Domain);
Console.WriteLine("SAR for Domain {" + _Domain + "}");
do
{
Console.Write("Input target value: ");
int _parm = (Convert.ToInt32(Console.ReadLine()));
SAR.FindOptimalSAR(_parm);
Console.WriteLine("Done.");
} while (true);
This is my result after 100k iterations for a few targets, given a slightly modified series (I switched 25 for 24 for testing purposes):
SAR for Domain {100, 80, 66, 24, 4, 2, 1}
Input target value: 50
Optimal found: +24+24+2.
Done.
Input target value: 29
Optimal found: +4+1+24.
Done.
Input target value: 75
Optimal found: +2+2+1+66+4.
Optimal found: +4+66+4+1.
Done.
Now with your original series:
SAR for Domain {100, 80, 66, 25, 4, 2, 1}
Input target value: 50
Optimal found: +25+25.
Done.
Input target value: 75
Optimal found: +25+25+25.
Done.
Input target value: 512
Optimal found: +80+80+66+100+1+80+25+80.
Optimal found: +66+100+80+100+100+66.
Done.
Input target value: 1024
Optimal found: +100+1+80+80+100+2+100+2+2+2+25+2+100+66+25+66+100+80+25+66.
Optimal found: +4+25+100+80+100+1+80+1+100+4+2+1+100+1+100+100+100+25+100.
Optimal found: +80+80+25+1+100+66+80+80+80+100+25+66+66+4+100+4+1+66.
Optimal found: +1+100+100+100+2+66+25+100+66+100+80+4+100+80+100.
Optimal found: +66+100+100+100+100+100+100+100+66+66+25+1+100.
Optimal found: +100+66+80+66+100+66+80+66+100+100+100+100.
Done.
Cons: It is worth mentioning again: This algorithm does not guarantee that you will find the optimal values. It makes a brute-force approximation.
Pros: Fast. 100k iterations may initially seem a lot, but the algorithm starts ignoring long paths after it detects more and more optimized paths, since it lessens the maximum allowed number of cycles.
Description:
A positive integer m is said to a pure number if and only if m can be
expressed as q-th power of a prime p (q >= 1). Here your job is easy,
for a given positive integer k, find the k-th pure number.
Input:
The input consists of multiple test cases. For each test case, it
contains a positive integer k (k<5,000,000). Process to end of file.
Output:
For each test case, output the k-th pure number in a single line. If
the answer is larger than 5,000,000, just output -1.
Sample input:
1
100
400000
Sample output:
2
419
-1
Original page: http://acm.whu.edu.cn/learn/problem/detail?problem_id=1092
Can anyone give me some suggestion on the solution to this?
You've already figured out all the pure numbers, which is the tricky part. Sort the ones less than 5 million and then look up each input in turn in the resulting array.
To optimize you need to efficiently find all primes up to 5 million (note q >= 1 in the problem description: every prime is a pure number), for which you will want to use some kind of sieve (sieve of Erathosthenes will do, look it up).
You could probably adapt the sieve to leave in powers of primes, but I expect that it would not take long to sieve normally and then put the powers back in. You only have to compute powers of primes p where p <= the square root of 5 million, which is 2236, so this shouldn't take long compared with finding the primes.
Having found the numbers with a sieve, you no longer need to sort them, just copy the marked values from the sieve to a new array.
Having now looked at your actual code: your QuickSort routine is suspect. It performs badly for already-sorted data and your array will have runs of sorted numbers in it. Try qsort instead, or if you're supposed to do everything yourself then you need to read up on pivot choice for quicksort.
Try following approach:
static void Main(string[] args)
{
int max = 5000000;
int[] dp = new int[max];
for (int i = 2; i < max; i++)
{
if (dp[i] == 0)
{
long t = i;
while (t < max)
{
dp[t] = 1;
t *= i;
}
int end = max / i;
for (int j = 2; j < end; j++)
if (dp[i * j] == 0)
dp[i * j] = 2;
}
}
int[] result = new int[348978];
int pointer = 1;
for (int i = 2; i < max; i++)
{
if (dp[i] == 1)
result[pointer++] = i;
}
}
Into array as "1" marked pure numbers.
As "2" marked non pure(prime) numbers.
For each output check array ranges if it inside output result[index] if not output should be -1.