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the maximum n digit number possible in K steps

Can somebody help me with this problem?
Statement: - What is the maximum possible n digit number starting from 0 we can make in K steps
using only 2 operations:-
multiplying by 3 or incrementing by 2.
EXAMPLE :
N =2 K = 5;
-> (0->2->6->8->24->72) 72 IS THE ANSWER
N = 2 , K = 51 -> (0->2->6->8->10->30->32->96->98). 98 is the maximum we can get so need to check for rest of the moves.
My 2 state-recursive solution:-
public static void largestNDigitNumber(long[] highest, long maxValue, long k, long currentValue) {
if (highest[0] == (maxValue - 2)) return; //no need to do anything as we get 98 as highest.
if (k < 0) return; //checking for steps
if (highest[0] < currentValue && currentValue <= (maxValue - 2)) {
highest[0] = currentValue;
}
largestNDigitNumber(highest, maxValue, (k - 1), (currentValue * 3));
largestNDigitNumber(highest, maxValue, (k - 1), (currentValue + 2));
}
public static void main(String[] args) {
int n = 2;
long k = 51;
long maxValue = (long) Math.pow(10, n);
long[] highest = new long[1];
largestNDigitNumber(highest, maxValue, (k - 1), 2);
if (highest[0] < (long) Math.pow(10, (n - 1))) {
System.out.println("-1"); // if it is not possible to make n digit in given steps
} else System.out.println(highest[0]);
}
when "k" is small it is giving the correct answer but for bigger values of "k", it does not show any input. for n=2 and k = 51, it does not show anything.
please help me to improve this code

The question is equivalent to asking what is the largest base 3 number that is less than 10^n/2, and has digit sum plus length less than or equal to k+1. (The answer is then double the base 3 number).
For example, N=2 K=5. What's the largest base 3 number that's less than 50, with length plus digit sum less than or equal to 6. Answer: 1100 (36 decimal), so the answer to the original question is 36*2=72.
For N=2, K=51, the largest base-3 number that's less than 50 is 2001 (49 decimal) and has length sum plus digit sum = 7, which is way less than K+1.
Given this representation, it's easy to solve the problem in O(n) time (in fact, you can solve it using pencil and paper). The length d of the base-3 number is as large as possible such that 3^d < 10^n/2 and d<=K. Then fill in the digits of the number greedily from the most-significant first until you have digit sum K+1-d (or you run out of digits).
Equivalence
First note that without loss of generality you can assume you never have three +2 operations in a row, since that can be done more efficiently by inserting a single +2 operation to before the most recent *3 (or simply replacing it by +2 * 3 if there's no *3 operation). Suppose you have represented the current number as a doubled base-3 number. A +2 operation corresponds to adding 1 to the bottom digit (this never overflows into the next column thanks to the observation above). A *3 operation moves all the digits up one column, introducing a 0 as the bottom digit. Note that because the number is doubled, the +2 operation adds just 1 to the base-3 number!
From this, you can see that you can count the number of operations from observation of the doubled base-3 number. Because *3 introduces a new digit, and +2 increases the digit sum by 1, so the number of operations is equal to the number of digits plus 1, plus the digit sum.
As an example. Suppose you have the doubled base-3 number 2 * 2101, then this is equivalent to 2 * (1+3*3*(1+3*(1+1)))) = (2 + 3*3*(2+3*(2+2))).

I tried something like this. it seems to work fine.
getMaxNumber(2, 5) ==> 72
getMaxNumber(2, 51) ==> 98
private int getMaxNumber(int n, int k){
int N = 0;
for (int i = 0; i < n; i++) {
N = N * 10 + 9;
}
int[] result = new int[1];
helper(N, k, 0, 0, result);
return result[0];
}
private void helper(int N, int K, int n, int k, int[] result){
if(n > N) return;
if(k <= K){
result[0] = Math.max(result[0], n);
}
if(n > 0)
helper(N, K, n * 3, k + 1, result);
helper(N, K, n + 2, k + 1, result);
}

Keeping with the style of your original recursive method. I modified it a bit to produce a working solution:
public static long largestNDigitNumber(int n, long currentK, long maxK, long currentValue) {
if (currentK > maxK || n < 1 || maxK < 1) return 0;
if (currentValue >= Math.pow(10, n))
return 0;
long c1 = largestNDigitNumber(n, currentK + 1, maxK, currentValue * 3);
long c2 = largestNDigitNumber(n, currentK + 1, maxK, currentValue + 2);
if (c1 == 0 && c2 == 0)
return currentValue;
return c1 > c2 ? c1 : c2;
}
public static void main(String[] args) {
int n = 2;
long k = 51;
long largest = largestNDigitNumber(n, 0, k, 0);
System.out.println(largest); //98
}
This recursive method returns values here instead of using an array. Hence the check if one returned value is bigger than the other or they are both 0 before returning.

Both the +2 and *3 operations preserve odd/even parity, so starting from 0 we can only reach even numbers. We could start our search at the highest even number: 8, 98, 998, 9998 etc. and see what the shortest distance to 0 is.
If we are looking for the shortest distance, then there are less choices to make. If the current number is a multiple of 3 then there are two choices, either we divide by 3 or subtract 2. Otherwise the only choice is to subtract 2. I suspect that in the majority of cases, dividing by 3 is the better option, so that might be the first to try to keep the tree smaller.
If the minimum number of steps is less than K then as many divide by 3 operations as needed can be used to make the correct K
If the minimum number of steps is equal to K then the problem is solved.
If the minimum number of steps is more than K then you need to pick a lower starting number. Some even numbers will already have been covered as part of the initial calculation. You get those 'for free', provide you include a small amount of record keeping. You only need to examine large even numbers that were missed earlier due to a 'divide by 3' step.

calculate Check Number in Java

Not sure if anyone can explain this to me or help me.
I have a 15 Digit Number of which I want to multiply each even number by 2 unless the even number is greater than 9. If it is this needs to be subtracted by 9 to give me an integer that again I can multiply by 2. Once I have all the even numbers multiplied by 2 i need to add them all together with the odd numbers.
Does that make sense.
UPDATE ***
so i have a number say 49209999856459. for that number I am looking to get the even integer so for example the first even one would be 4 then the second would be 2 and so on.
If one of those even numbers are multiplied by 2 then it might be above 9 so I want to subtract 9 to then use the remainder as the even number in its place.
SO !!!
Multiply by 2 the value of each even digit starting from index 0 and then each even index. In each case, if the resulting value is greater than 9, subtract 9 from it (which reduces larger values to a single digit). Leave the values of the digits at the odd indexes unchanged.
public String calculateCheckNumber()
String firstFifteen = longNumber.substring(0,15) ;
int i, checkSum = 0, totalSum = 0;
for (i = 0; i<firstFifteen.length(); i += 2) {
while (i < 9)
i *= 2;
if (i > 9)
i -= 9 ;
}
Was one option I was trying but it honestly I cant seem to get my head around it.
Any Help would be greatly appreciated.

Well, here is one approach. This uses the ternary (?:) operator to condense the operations. Edited base on clarification from the OP. The example you gave is actually a 14 digit string. But the following will work with any number of digits if they start out in a string. If you have a long value, then you can create the character array using:
long v = 49209999856459L;
char[] d = Long.toString(v).toCharArray();
Here is the main algorithm.
String s = "49209999856459";
int sum = 0;
char[] d = s.toCharArray();
for (int i = 0; i < d.length; i++) {
int v = d[i] - '0';
// The even digit will only be greater than 9 after
// doubling if it is >= 5 before.
sum += ((i % 2) == 1) ? v : (v >= 5) ? v+v-9 : v+v;
}
System.out.println(sum);
Prints
86

How could we get within a certain range, the maximum number of times, an int is divisible by 2

I am doing the following programming exercise: Strongest even number in an interval. The statement is:
A strongness of an even number is the number of times we can
successively divide by 2 until we reach an odd number starting with an
even number n.
For example, if n = 12, then
12 / 2 = 6
6 / 2 = 3
we divided successively 2 times and we reached 3, so the strongness of
12 is 2.
If n = 16 then
16 / 2 = 8
8 / 2 = 4
4 / 2 = 2
2 / 2 = 1
we divided successively 4 times and we reached 1, so the strongness of
16 is 4 Task
Given a closed interval [n, m], return the even number that is the
strongest in the interval. If multiple solutions exist return the
smallest strongest even number.
Note that programs must run within the alloted server time; a naive
solution will probably time out. Constraints
1 <= n < m <= INT_MAX Examples
for the input [1, 2] return 2 (1 has strongness 0, 2 has strongness 1)
for the input [5, 10] return 8 (5, 7, 9 have strongness 0; 6, 10 have
strongness 1; 8 has strongness 2)
for the input [48, 56] return 48
First I thought to store in a map each number as a key, and the number of times it is divisible by 2, as a value:
import java.util.*;
public class StrongestEvenNumber {
public static int strongestEven/*💪*/(int n, int m) {
System.out.println("n: "+n);
System.out.println("m: "+m);
Map<Integer, Integer> map = new HashMap<Integer, Integer>();
for(int i = n, number = 0, strongness = 0; i <= m; i++){
for(number = i, strongness = 0; number % 2 == 0; strongness++){
number /= 2;
}
map.put(i, strongness);
}
Map.Entry<Integer, Integer> maxEntry = null;
for(Map.Entry<Integer,Integer> entry : map.entrySet()){
if(maxEntry == null || entry.getValue().compareTo(maxEntry.getValue()) > 0){
maxEntry = entry;
}
}
return maxEntry.getKey();
}
}
However, with large numbers, it runs out of heap memory space, or execution time runs out. For example with:
n: 1180381085
m: 2074186600
Java heap space runs out.
And with:
n: 324243
m: 897653214
Execution time runs out. The execution time exceeds 16000 ms
Then I tried to just store the number which is the most times divisible by 2:
import java.util.*;
public class StrongestEvenNumber {
public static int strongestEven/*💪*/(int n, int m) {
System.out.println("n: "+n);
System.out.println("m: "+m);
int maxStrongness = 0, maxNumber = 0;
for(int i = n, number = 0, strongness = 0; i <= m; i++){
for(number = i, strongness = 0; number % 2 == 0; strongness++){
number /= 2;
}
if(strongness > maxStrongness){
maxStrongness = strongness;
maxNumber = i;
}
}
return maxNumber;
}
}
Indeed it solves the heap space difficulty, however the execution time runs out behaviour stills happening.
For example with:
n: 200275492
m: 1590463313
The execution time exceeds 16000 ms
I have also read:
Finding Key associated with max Value in a Java Map
Get the key for the maximum value in a HashMap using Collections
https://math.stackexchange.com/questions/2589831/how-many-times-can-i-divide-a-number-by-another
Number of times all the numbers in an array are divisible by 2
optimize code to get the number of integers within given range that are divisible by integer

Well, the strongness of a value x is n when x is represented as
x = k * 2**n
knowing this we can check all powers of 2 (i.e. 1, 2, 4, 8, ...) if we can find any k such that
from <= k * 2**n <= to
Code:
private static int strongestEven(int from, int to) {
if (to < from)
return -1; // Or throw exception
// best power of 2 we can insert between [to..from] as k * best
int best = 1;
while (true) {
int ceiling = from / best + (from % best == 0 ? 0 : 1);
int floor = to / best;
if (ceiling > floor) {
best = best / 2;
return best * (to / best);
}
best *= 2;
}
}
Tests:
[ 1, 2] => 2
[ 5, 10] => 8
[48, 56] => 48
[80, 100] => 96
[97, 100] => 100
Finally,
[1180381085, 1590463313] => 1342177280
we have 1342177280 == 5 * 268435456 == 5 * 2**28 so the strongest number within [1180381085, 1590463313] range has strongness 28
Please, note, that the algorithm has O(log(to)) time complexity that's why will do even if we turn all int into long

The strongness is actually the number of trailing zeros in the binary representation of the number. You can use the java.lang.Integer.numberOfTrailingZeros to get it.
And as you want to test the even numbers, you can skipp the odd numbers in your loop.
public class StrongestEvenNumber {
public static int strongestEven(int n, int m) {
int maxStrongness = 0, maxNumber = 0;
for(int i = n%2==0?n:n+1, strongness = 0; i <= m; i=i+2){
strongness = Integer.numberOfTrailingZeros(i);
if(strongness > maxStrongness){
maxStrongness = strongness;
maxNumber = i;
}
}
return maxNumber;
}
This runs in the allocated time:
Completed in 13190ms

How to get the kth term in N Combination R [closed]

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How do I get the kth combination inNCR. without iterating through all possible outcomes. e.g. say I have 3C2 for 3 positions and 2identical-items. I am aware it's [011],[101] and [110]. how do I get e.g. the 2nd term(k=1) which is [101] using a method?
constraints(R < N k >= 0 and k < P where P = NCR).
NB:[101] is the 2nd term(in ascending/lexicographical order) because 011 = 3,101 = 5 ,110 = 6
in decimal. so basically the goal is to get what number k in NCR is,
because every kth output from NCR can be represented as a number.

Yes, you are correct when you say:
because every kth output from NCR can be represented as a number.
There is a bijection from the set of integers 1 to # of combs/perms to the entire set of combs/perms. Finding the specific index of a particular comb/perm is sometimes referred to as getting the rank. According to the example that you have in your question, these are ordinary permutations. Moreover when you mention ascending order, you are referring to the lexicographical order.
It is a straightforward exercise in counting to obtain the nth ordinary permutation of a given set. We first need to obtain the total number of permutations using the well established formula:
P(n, r) = n! / (n - r)!
This next part is the key observation that allows us to quickly obtain each element of our target permutation.
If we look at all permutations of our set of n choose r, there will be n groups that are only different by a permutation of the n elements.
For example, if we look at the first two group of the permutations of [0 1 2 3] choose 3, we have:
[,0] [,1] [,2]
[0,] 0 1 2
[1,] 0 1 3
[2,] 0 2 1
[3,] 0 2 3
[4,] 0 3 1
[5,] 0 3 2
[6,] 1 0 2
[7,] 1 0 3
[8,] 1 2 0
[9,] 1 2 3
[10,] 1 3 0
[11,] 1 3 2
Note that the last permutations are simply the first 6 permutations of the set [1 0 2 3].. that is, 0 is mapped to 1, 1 is mapped to 0, and the final 2 elements are mapped to themselves.
This pattern continues as we move to the right only instead of n identical groups, we will get n - 1 similar groups for the second column, n -2 for the third, and so on.
So to determine the first element of our permutation, we need to determine the 1st group. We do that by simply dividing the number of permutations by n. For our example above of permutations of 4 choose 3, if we were looking for the 15th permutation, we have the following for the first element:
Possible indices : [0 1 2 3]
P(4, 3) = 24
24 / 4 = 6 (elements per group)
15 / 6 = 2 (integer division) 2 means the 3rd element here (base zero)
Now that we have used the 3rd element, we need to remove it from our array of possible indices. How do we get the next element?
Easy, we get our next subindex by subtracting the product of the group we just found and the elements per group from our original index.
Possible indices : [0 1 3]
Next index is 15 - 6 * 2 = 3
Now, we just repeat this until we have filled all entries:
Possible indices : [0 1 3]
Second element
6 / 3 = 2 (elements per group)
3 / 2 = 1
Next index is 3 - 3 * 1 = 0
Possible indices : [0 3]
Third element
2 / 2 = 1
0 / 1 = 0
So our 15th element is : [2 1 0]
Here is a C++ implementation that should be pretty easy to translate to Java:
double NumPermsNoRep(int n, int k) {
double result = 1;
double i, m = n - k;
for (i = n; i > m; --i)
result *= i;
return result;
}
std::vector<int> nthPermutation(int n, int r, double myIndex) {
int j = 0, n1 = n;
double temp, index1 = myIndex;
std::vector<int> res(r);
temp = NumPermsNoRep(n, r);
std::vector<int> indexVec(n);
std::iota(indexVec.begin(), indexVec.end(), 0);
for (int k = 0; k < r; ++k, --n1) {
temp /= n1;
j = (int) std::trunc(index1 / temp);
res[k] = indexVec[j];
index1 -= (temp * (double) j);
indexVec.erase(indexVec.begin() + j);
}
}
These concepts extends to other types of combinatorial problems, such as finding the nth combination, or permutation with repetition, etc.

The time complexity is O(kn), space is O(n)
public static void main(String[] args) {
//n = 4, r = 2, k = 3
int[] ret1 = getKthPermutation(4, 2, 3);
//ret1 is [1,0,0,1]
//n = 3, r = 2, k = 1
int[] ret2 = getKthPermutation(3, 2, 1);
//ret2 is [1,0,1]
}
static int[] getKthPermutation(int n, int r, int k) {
int[] array = new int[n];
setLastN(array, r, 1);
int lastIndex = n - 1;
for(int count = 0; count < k; count++) {
int indexOfLastOne = findIndexOfLast(array, lastIndex, 1);
int indexOfLastZero = findIndexOfLast(array, indexOfLastOne, 0);
array[indexOfLastOne] = 0;
array[indexOfLastZero] = 1;
//shortcut: swap the part after indexOfLastZero to keep them sorted
int h = indexOfLastZero + 1;
int e = lastIndex;
while(h < e) {
int temp = array[h];
array[h] = array[e];
array[e] = temp;
h++;
e--;
}
}
return array;
}
//starting from `from`, and traveling the array forward, find the first `value` and return its index.
static int findIndexOfLast(int[] array, int from, int value) {
for(int i = from; i > -1; i--)
if(array[i] == value) return i;
return -1;
}
//set the last n elements of an array to `value`
static void setLastN(int[] array, int n, int value){
for(int i = 0, l = array.length - 1; i < n; i++)
array[l - i] = value;
}
This is an adaption of the very typical "find the kth permation" algorithm.
I will try to explain the general idea (yours is a special case as there are only two types of elements: 0 and 1).
Lets say I have [2,1,6,4,7,5]. What is the next smallest permutation that is bigger than the current one? Why do I concern the next smallest permutation bigger than current one? Because if you start with the smallest permutation [1,2,4,5,6,7] and you repeat the action (find the smallest bigger than current) k times, you will find k+1 th smallest permutation.
Now, since the one I am looking for needs to be bigger than current one, I need to increment the current one. To keep the incrementation as small as possible, I am going to try to modify 5 (last one). Now, I cannot just change 5 to a random value, I can only swap it with some digit before it.
If I swap 5 with a bigger number before it, say 7, then I will get [2,1,6,4,5,7], which is smaller than current one. Now obviously I need to swap 5 with some smaller digit before it, but which one? If I swap 5 with 2, I get [5,1,6,4,7,2], this increment is too big. I need to swap 5 with a "lower digit" to keep the increment as small as possible. Thats leads us to find the first(lowest) digit (from right to left) that is smaller than 5. In this case I would need to swap 5 with 4 and get [2,1,6,5,7,4]. This way, I can make the impact of "swap" small. Now the prefix is decided [2,1,6,5. There is no smaller prefix. We need to deal with suffix 7,4]. Clearly, if we sort the suffix and make it 4,7], then we are done.
In our case, there are two differences:
1. we need to swap the last 1, because you cannot make the permutation bigger by swapping the a zero with any digit before it.
2. we can always sort the suffix using a shortcut as shown in the code. I will leave it to you:)

public static String lexicographicPermutation(String str, long n) {
final long[] factorials = { 1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800, 39916800, 479001600 };
n--;
char[] arr = str.toCharArray();
for (int i = 0; i < arr.length - 1; i++) {
long fact = factorials[arr.length - i - 2];
long p = i + n / fact;
n %= fact;
for (int j = i + 1; j <= p; j++)
swap(arr, i, j);
}
return new String(arr);
}
private static void swap(char[] arr, int i, int j) {
char tmp = arr[i];
arr[i] = arr[j];
arr[j] = tmp;
}
You can replace STR with required string. In the given example, 1st permutation is "abcdefghijklm" (this is a string with 13 chars), 13!st permutation is reverse string "mlkjihgfedcba" and 100st permutation is "abcfklgmeihjd".
To realise this soulution just google Factorial number system. This is a key to solve this problem. This is a Project Euler: Problem 24.
Demo:
for(int i = 1; i <= 6; i++)
System.out.println(lexicographicPermutation("110", i));
1 - 110
2 - 101
3 - 110
4 - 101
5 - 011
6 - 011
for(int i = 1; i <= 6; i++)
System.out.println(lexicographicPermutation("abc", i));
1 - abc
2 - acb
3 - bac
4 - bca
5 - cab
6 - cba

How to find the next lower integer (with the same number of 1s)

How to find the next lower binary number for an integer (same number of 1s)? For example: if given input number n = 10 (1010), the function should return 9 (1001), or n = 14 (1110) then return 13 (1101), or n = 22 (10110) then return 21 (10101), n = 25 (11001) then return 22 (10110)... etc.

You can do this.
static int nextLower(int n) {
int bc = Integer.bitCount(n);
for (int i = n - 1; i > 0; i--)
if (Integer.bitCount(i) == bc)
return i;
throw new RuntimeException(n+" is the lowest with a bit count of "+bc);
}
Of course if this is homework you are going to have trouble convincing someone you wrote this ;)

For the sake of clarity, in this answer I will use the term 'cardinality' to indicate the number of 1s in the binary representation of a number.
One (obvious) way is to run a downwards loop, and seek for the first number with the same cardinality as your input (just like Peter Lawrey suggested).
I don't think this is inefficient, because I guess the output number is always pretty close to the input. More precisely, all you have to do is to find the rightmost '10' bit sequence, and change it to '01'. Then replace the right part with a number having all 1s at its left, as many as you can, without breaking the postcondition. This brings us to another solution, which consists in converting the number to a binary string (like user2573153 showed you), performing the replacement (with a regular expression, maybe), and then converting back to int.
A slightly faster version of Peter's algorithm should be the following, which performs on integers the manipulation I proposed you for strings:
static int nextLower(int n) {
int fixPart = 0;
int shiftCount = 0;
while ((n & 3) != 2) {
if (n == 0) {
throw new IllegalArgumentException(
fixPart + " is the lowest number with its cardinality");
}
fixPart |= (n & 1) << shiftCount;
shiftCount += 1;
n /= 2;
}
int fixZeros = shiftCount - Integer.bitCount(fixPart);
return ((n ^ 3) << shiftCount) | (((1 << shiftCount) - 1) & ~((1 << fixZeros) - 1));
}
which is O(log n) rather than O(n), but it's definitely harder to understand, and may also be practically slower, due to its complexity. Anyway, you could only notice a difference if you try with some huge difficult number.
EDIT I tried a little benchmark, and found that this code is 67% faster than Peter Lawrey's when applied consecutively to all numbers from 2 to 100,000,000. I don't think this is enough to justify the increased code complexity.

I like such binary task, so to find next lower number you should find right most 1 followed by 0 and exchange them,. UPDATE: you need to "reorder" the rest part of number with 1s at left and 0s at right
10 1010 ->
9 1001
14 1110 ->
13 1101
25 11001 ->
22 10110
here is sample code:
int originalValue = 25;
int maskToCheck = 2; // in binary 10b
int clearingMask = 1;
int settingMask = 0;
int zeroCount = 0;
while (maskToCheck > 0)
{
if ( (originalValue&(maskToCheck|(maskToCheck>>1))) == maskToCheck ) // we found such
{
int newValue = originalValue&(~maskToCheck); // set 1 with 0
newValue = newValue&(~clearingMask)|(settingMask<<zeroCount); // clear all the rest bits, and set most valuable ones
newValue = newValue|(maskToCheck>>1); // set 0 with 1
System.out.println("for " + originalValue + " we found " + newValue);
break;
}
else
{
if ( (originalValue&(maskToCheck>>1)) > 0) // we have 1 bit in cleared part
settingMask = (settingMask<<1) | 1;
else
zeroCount++;
maskToCheck = maskToCheck<<1; // try next left bits
clearingMask = (clearingMask<<1)|1;
}
}