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How do I count numbers that contain one digit, but not another?

I recently came across an interview question which although had an immediately obvious solution, I struggled to find a more efficient one.
The actual question involved counting numbers from a to b (up to 2^64) which satisfied having either the digit 6 or 8, but not both. They called it a 'lucky number'. So for example:
126 - lucky
88 - lucky
856 - not lucky
The obvious thought was to brute force it by testing each number between a and b as a string, to check for the relevant characters. However, this was prohibitively slow as expected.
A much better solution that I tried, involved first computing all the 'lucky numbers' which had the number of digits between the number of digits that a and b have (by counting possible combinations):
long n = 0;
for (int occurrences = 1; occurrences <= maxDigits; occurrences++) {
n += (long) Math.pow(8, digits - occurrences) * choose(digits, occurrences);
}
return 2 * n;
and then using the brute force method to compute the number of extra lucky numbers that I had counted. So for example, if a = 3 and b = 21, I could count the number of 1 and 2 digit lucky numbers, then subtract the count of those in [1, 3) and (21, 99].
However, although this was a massive improvement, the brute force element still slowed it down way too much for most cases.
I feel like there must be something I am missing, as the rest of the interview questions were relatively simple. Does anyone have any idea of a better solution?
Although I have tagged this question in Java, help in any other languages or pseudocode would be equally appreciated.

I would say you are at the right track. The gut feeling is that dealing with the a and b separately is easier. Making a function count_lucky_numbers_below(n) allows
return count_lucky_numbers_below(b) - count_lucky_numbers_below(a);
The combinatorial approach is definitely a way to go (just keep in mind that the sum is actually equal to 9**n - 8**n, and there is no need to compute the binomial coefficients).
The final trick is to recurse down by a numbeer of digits.
Lets say n is an N-digit number, and the most significant digit is 5. Each set of N-digit numbers starting with a smaller digit contributes S = 9**(N-1) - 8**(N-1) to the total; you immediately have 5*S of lucky numbers. To deal with the remainder, you need to compute the lucky numbers for the N-1-digit tail.
Of course, care must be taken if the most significant digit is above 5. You need to special case it being 6 or 8, but it doesn't seem to be too complicated.

In the end the answer from #user58697 pushed me in the right direction towards finding a solution. With my (albeit extremely primitive) benchmark, it handles 1 to 2^63 - 1 in less than 2 nanoseconds, so it is definitely fast enough. However it is still more verbose than I would have liked, especially given that I was originally expected to write it in half an hour, so I feel like there is still an easier solution that gives comparable performance.
long countLuckyNumbersBetween(long a, long b) {
return countLuckyNumbersBelow(b) - countLuckyNumbersBelow(a - 1);
}
long countLuckyNumbersBelow(long n) {
return countNumbers(n, 6, 8) + countNumbers(n, 8, 6);
}
/**
* Counts the natural numbers in [0, {to}] that have {including} as a digit, but not {excluding}.
* {excluding} should be in (0, 9] or -1 to exclude no digit.
*/
long countNumbers(long to, int including, int excluding) {
if (including == -1) return 0;
if (to < 10) {
if (to >= including) {
return 1;
} else {
return 0;
}
}
int nSignificand = significand(to);
int nDigits = countDigits(to);
long nTail = to % (long) Math.pow(10, nDigits - 1);
// The count of numbers in [0, 10^(nDigits-1)) that include and exclude the relevant digits
long bodyCount;
if (excluding == -1) {
bodyCount = (long) (Math.pow(10, nDigits - 1) - Math.pow(9, nDigits - 1));
} else {
bodyCount = (long) (Math.pow(9, nDigits - 1) - Math.pow(8, nDigits - 1));
}
long count = 0;
for (int i = 0; i < nSignificand; i++) {
if (i == including) {
if (excluding == -1) {
count += Math.pow(10, nDigits - 1);
} else {
count += Math.pow(9, nDigits - 1);
}
} else if (i != excluding) {
count += bodyCount;
}
}
if (nSignificand == including) {
count += 1 + nTail - countNumbers(nTail, excluding, -1);
} else if (nSignificand != excluding) {
count += countNumbers(nTail, including, excluding);
}
return count;
}
int significand(long n) {
while (n > 9) n /= 10;
return (int) n;
}
int countDigits(long n) {
if (n <= 1) {
return 1;
} else {
return (int) (Math.log10(n) + 1);
}
}

Here is another approach:
264 = 18446744073709551616
We can represent the number as a sum of components (one component per every digit position):
18446744073709551616 associated range of numbers
———————————————————— ———————————————————————————————————————————
0xxxxxxxxxxxxxxxxxxx => [00000000000000000000;09999999999999999999]
17xxxxxxxxxxxxxxxxxx => [10000000000000000000;17999999999999999999]
183xxxxxxxxxxxxxxxxx => [18000000000000000000;18399999999999999999]
1843xxxxxxxxxxxxxxxx => [18400000000000000000;18439999999999999999]
18445xxxxxxxxxxxxxxx => [18440000000000000000;18445999999999999999]
...
1844674407370955160x => [18446744073709551600;18446744073709551609]
18446744073709551616 => [18446744073709551610;18446744073709551616]
If we could compute the amount of lucky numbers for every component, then the sum of the amounts for every component will be the total amount for 264.
Note that every component consists of a prefix followed by xs.
Imagine that we know how many lucky numbers there are in an n-digit xx..x (i.e. numbers [0..0 - 9..9]), let's call it N(n).
Now let's look at a component 18445x..x. where 18445 is a prefix and an n-digit xx..x.
In this component we look at all numbers from 18440xx..x to 18445xx..x.
For every item 1844dxx..x we look at the prefix 1844d:
if prefix contains no 6 or 8, then it's the same as x..x without prefix => N(n) special numbers
if prefix contains 6 and no 8, then x..x cannot contain 8 => 9ⁿ special numbers
if prefix contains 8 and no 6, then x..x cannot contain 6 => 9ⁿ special numbers
if prefix contains 6 and 8 => 0 special numbers
Now let's compute N(n) — the amount of lucky numbers in an n-digit xx..x (i.e. in [0..0 - 9..9]).
We can do it iteratively:
n=1: there are only 2 possible numbers: 8 and 6 => N(1)=2.
n=2: there are 2 groups:
8 present: 8x and x8 where x is any digit except 6
6 present: 6x and x6 where x is any digit except 8
=> N(2)=4*9=34.
n=3: let's fix the 1st digit:
0xx — 5xx, 7xx, 9xx => 8 * N(2)
6xx: xx are any 2 digits except 8 => 9²
8xx: xx are any 2 digits except 6 => 9²
=> N(3) = 8*N(2) + 2*9².
n=k+1 => N(k+1) = 7*N(k) + 2*9ᵏ
Here is an implementation (not 100% tested):
public final class Numbers {
public long countLuckyNumbersBelow(BigInteger num) {
if (num.compareTo(BigInteger.ZERO) < 0) {
throw new IllegalArgumentException("num < 0: " + num);
}
var numberText = num.toString();
var result = 0L;
for (var digitPosition = 0; digitPosition < numberText.length(); digitPosition++) {
result += countLuckyNumbersForComponent(numberText, digitPosition);
}
return result;
}
private long countLuckyNumbersForComponent(String numberText, int digitPosition) {
var prefixEndIdx = numberText.length() - 1 - digitPosition;
var prefixHas6s = containsChar(numberText, '6', prefixEndIdx);
var prefixHas8s = containsChar(numberText, '8', prefixEndIdx);
if (prefixHas6s && prefixHas8s) {
return 0;
}
var result = 0L;
for (var c = numberText.charAt(prefixEndIdx) - 1; c >= '0'; c--) {
var compNo6s = (!prefixHas6s) && (c != '6');
var compNo8s = (!prefixHas8s) && (c != '8');
if (compNo6s && compNo8s) {
result += countLuckyNumbers(digitPosition);
} else if (compNo6s || compNo8s) {
result += power9(digitPosition);
}
}
return result;
}
private static boolean containsChar(String text, char c, int endIdx) {
var idx = text.indexOf(c);
return (idx > 0) && (idx < endIdx);
}
private long[] countLuckyNumbersCache = {0L, 0L};
/**
* Computes how many lucky numbers are in an n-digit `xx..x`
*/
private long countLuckyNumbers(int numDigits) {
if (countLuckyNumbersCache[0] == numDigits) {
return countLuckyNumbersCache[1];
}
long N;
if (numDigits <= 1) {
N = (numDigits == 1) ? 2 : 0;
} else {
var prevN = countLuckyNumbers(numDigits - 1);
N = (8 * prevN) + (2 * power9(numDigits-1));
}
countLuckyNumbersCache[0] = numDigits;
countLuckyNumbersCache[1] = N;
return N;
}
private long[] power9Cache = {0L, 1L};
/**
* Computes 9<sup>power</sup>
*/
private long power9(int power) {
if (power9Cache[0] == power) {
return power9Cache[1];
}
long res = 1;
var p = power;
if (power > power9Cache[0]) {
p -= power9Cache[0];
res = power9Cache[1];
}
for (; p > 0; p--) {
res *= 9;
}
power9Cache[0] = power;
power9Cache[1] = res;
return res;
}
}
BTW it took me half a day, and I have no idea how is that possible to complete it in 30 minutes.
I guess your interviewers expected from you to demonstrate them your thought process.

Here is the result of my attempt.
First, let me explain a little bit what logic I used.
I used formula S = 9N — 8N (mentioned in the user58697's answer) to compute how many of N-digit numbers are lucky.
How to get this formula:
for N-digit numbers there are 10N numbers in total: N digits, each can take one of 10 values: [0-9].
if we only count numbers without 6, then each digit can only take one of 9 values [0-5,7-9] — it's 9N numbers in total
now we also want only numbers with 8.
We can easily compute how many numbers don't have both 6 and 8: digits in these numbers can only take one of 8 values [0-5,7,9] — it's 8N numbers in total.
As a result, there are S = 9N — 8N numbers which have 8 and no 6.
For numbers with 6 and without 8 the formula is the same.
Also numbers without 6 do not intersect with numbers without 8 — so we can just sum them.
And finally, since we know how to count lucky numbers for intervals [0;10N], we need to split the interval [0; our arbitrary number] into suitable sub-intervals.
For instance, we can split number 9845637 this way:
Sub-interval
Prefix
Digit
N-digit interval
0000000 - 8999999
0 - 8
000000 - 999999
9000000 - 9799999
9
0 - 7
00000 - 99999
9800000 - 9839999
98
0 - 3
0000 - 9999
9840000 - 9844999
984
0 - 4
000 - 999
9845000 - 9845599
9845
0 - 5
00 - 99
9845600 - 9845629
98456
0 - 2
0 - 9
9845630 - 9845637
Now we can compute the number for every sub-interval (just keep attention to digits in prefix — they might contains 8 or 6) and then just sum those numbers to get the final result.
Here is the code:
// Special value for 'requiredDigit': no required digit
private static char NIL = Character.MAX_VALUE;
public static long countLuckyNumbersUpTo(BigInteger number) {
if (number.compareTo(BigInteger.ZERO) < 0) {
throw new IllegalArgumentException("number < 0: " + number);
}
var numberAsDigits = number.toString();
return countNumbersUpTo(numberAsDigits, '6', '8') + countNumbersUpTo(numberAsDigits, '8', '6');
}
// count all numbers in [0;'numberAsDigits'] which have 'requiredDigit' and no 'excludeDigit'
private static long countNumbersUpTo(String numberAsDigits, char requiredDigit, char excludeDigit) {
var highDigit = numberAsDigits.charAt(0);
if (numberAsDigits.length() == 1) {
return (requiredDigit != NIL)
? ((highDigit >= requiredDigit) ? 1 : 0)
: numDigitsInInterval('0', highDigit, excludeDigit);
}
var tailDigits = numberAsDigits.substring(1);
var result = 0L;
// numbers where the highest digit is in [0;`highDigit`)
var numGoodDigits = numDigitsInInterval('0', (char) (highDigit - 1), excludeDigit);
var containsRequiredDigit = (requiredDigit != NIL) && (highDigit > requiredDigit);
if (containsRequiredDigit) {
result += totalNumbers(tailDigits.length(), NIL);
numGoodDigits--;
}
if (numGoodDigits > 0) {
result += numGoodDigits * totalNumbers(tailDigits.length(), requiredDigit);
}
// remaining numbers where the highest digit is `highDigit`
if (highDigit != excludeDigit) {
var newRequiredDigit = (highDigit == requiredDigit) ? NIL : requiredDigit;
result += countNumbersUpTo(tailDigits, newRequiredDigit, excludeDigit);
}
return result;
}
private static int numDigitsInInterval(char firstDigit, char lastDigit, char excludeDigit) {
var totalDigits = lastDigit - firstDigit + 1;
return (excludeDigit <= lastDigit) ? (totalDigits - 1) : totalDigits;
}
// total numbers with given requiredDigit in [0;10^numDigits)
private static long totalNumbers(int numDigits, char requiredDigit) {
return (requiredDigit == NIL) ? pow(9, numDigits) : (pow(9, numDigits) - pow(8, numDigits));
}
private static long pow(int base, int exponent) {
return BigInteger.valueOf(base).pow(exponent).longValueExact();
}

Down to Zero II

This is the question:
You are given Q queries. Each query consists of a single number N . You can perform any of the operations on in each move:
If we take 2 integers a and b where N=a*b (a ,b cannot be equal to 1), then we can change N=max(a,b)
Decrease the value of N by 1 .
Determine the minimum number of moves required to reduce the value of to .
Input Format
The first line contains the integer Q.
The next Q lines each contain an integer,N .
Output Format
Output Q lines. Each line containing the minimum number of moves required > to reduce the value of N to 0.
I have written the following code. This code is giving some wrong answers and also giving time limit exceed error . Can you tell what are the the mistakes present in my code ? where or what I am doing wrong here?
My code:
public static int downToZero(int n) {
// Write your code here
int count1=0;
int prev_i=0;
int prev_j=0;
int next1=0;
int next2=Integer.MAX_VALUE;
if (n==0){
return 0;
}
while(n!=0){
if(n==1){
count1++;
break;
}
next1=n-1;
outerloop:
for (int i=1;i<=n;i++){
for (int j=1;j<=n;j++){
if (i*j==n){
if (prev_i ==j && prev_j==i){
break outerloop;
}
if (i !=j){
prev_i=i;
prev_j=j;
}
int max=Math.max(i,j);
if (max<next2){
next2=max;
}
}
}
}
n=Math.min(next1,next2);
count1++;
}
return count1;
}
This is part is coded for us:
public class Solution {
public static void main(String[] args) throws IOException {
BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));
BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(System.getenv("OUTPUT_PATH")));
int q = Integer.parseInt(bufferedReader.readLine().trim());
for (int qItr = 0; qItr < q; qItr++) {
int n = Integer.parseInt(bufferedReader.readLine().trim());
int result = Result.downToZero(n);
bufferedWriter.write(String.valueOf(result));
bufferedWriter.newLine();
}
bufferedReader.close();
bufferedWriter.close();
}
}
Ex: it is not working for number 7176 ....

To explore all solution tree and find globally optimal solution, we must choose the best result both from all possible divisor pairs and from solution(n-1)
My weird translation to Java (ideone) uses bottom-up dynamic programming to make execution faster.
We calculate solutions for values i from 1 to n, they are written into table[i].
At first we set result into 1 + best result for previous value (table[i-1]).
Then we factor N into all pairs of divisors and check whether using already calculated result for larger divisor table[d] gives better result.
Finally we write result into the table.
Note that we can calculate table once and use it for all Q queries.
class Ideone
{
public static int makezeroDP(int n){
int[] table = new int[n+1];
table[1] = 1; table[2] = 2; table[3] = 3;
int res;
for (int i = 4; i <= n; i++) {
res = 1 + table[i-1];
int a = 2;
while (a * a <= i) {
if (i % a == 0)
res = Math.min(res, 1 + table[i / a]);
a += 1;
}
table[i] = res;
}
return table[n];
}
public static void main (String[] args) throws java.lang.Exception
{
int n = 145;//999999;
System.out.println(makezeroDP(n));
}
}
Old part
Simple implementation (sorry, in Python) gives answer 7 for 7176
def makezero(n):
if n <= 3:
return n
result = 1 + makezero(n - 1)
t = 2
while t * t <= n:
if n % t == 0:
result = min(result, 1 + makezero(n // t))
t += 1
return result
In Python it's needed to set recursion limit or change algorithm. Now use memoization, as I wrote in comments).
t = [-i for i in range(1000001)]
def makezeroMemo(n):
if t[n] > 0:
return t[n]
if t[n-1] < 0:
res = 1 + makezeroMemo(n-1)
else:
res = 1 + t[n-1]
a = 2
while a * a <= n:
if n % a == 0:
res = min(res, 1 + makezeroMemo(n // a))
a += 1
t[n] = res
return res
Bottom-up table dynamic programming. No recursion.
def makezeroDP(n):
table = [0,1,2,3] + [0]*(n-3)
for i in range(4, n+1):
res = 1 + table[i-1]
a = 2
while a * a <= i:
if i % a == 0:
res = min(res, 1 + table[i // a])
a += 1
table[i] = res
return table[n]

We can construct the directed acyclic graph quickly with a sieve and
then compute shortest paths. No trial division needed.
Time and space usage is Θ(N log N).
n_max = 1000000
successors = [[n - 1] for n in range(n_max + 1)]
for a in range(2, n_max + 1):
for b in range(a, n_max // a + 1):
successors[a * b].append(b)
table = [0]
for n in range(1, n_max + 1):
table.append(min(table[s] for s in successors[n]) + 1)
print(table[7176])
Results:
7

EDIT:
The algorithm uses Greedy approach and doesn't return optimal results, it just simplifies OP's approach. For 7176 given as example, below algorithm returns 10, I can see a shorter chain of 7176 -> 104 -> 52 -> 13 -> 12 -> 4 -> 2 -> 1 -> 0 with 8 steps, and expected answer is 7.
Let's review your problem in simple terms.
If we take 2 integers a and b where N=a*b (a ,b cannot be equal to 1), then we can change N=max(a,b)
and
Determine the minimum number of moves required to reduce the value of to .
You're looking for 2 factors of N, a and b and, if you want the minimum number of moves, this means that your maximum at each step should be minimum. We know for a fact that this minimum is reached when factors are closest to N. Let me give you an example:
36 = 1 * 36 = 2 * 18 = 3 * 12 = 4 * 9 = 6 * 6
We know that sqrt(36) = 6 and you can see that the minimum of 2 factors you can get at this step is max(6, 6) = 6. Sure, 36 is 6 squared, let me take a number without special properties, 96, with its square root rounded down to nearest integer 9.
96 = 2 * 48 = 3 * 32 = 4 * 24 = 6 * 16 = 8 * 12
You can see that your minimum value for max(a, b) is max(8, 12) = 12, which is, again, attained when factors are closest to square root.
Now let's look at the code:
for (int i=1;i<=n;i++){
for (int j=1;j<=n;j++){
if (i*j==n){
You can do this in one loop, knowing that n / i returns an integer, therefore you need to check if i * (n / i) == n. With the previous observation, we need to start at the square root, and go down, until we get to 1. If we got i and n / i as factors, we know that this pair is also the minimum you can get at this step. If no factors are found and you reach 1, which obviously is a factor of n, you have a prime number and you need to use the second instruction:
Decrease the value of N by 1 .
Note that if you go from sqrt(n) down to 1, looking for factors, if you find one, max(i, n / i) will be n / i.
Additionally, if n = 1, you take 1 step. If n = 2, you take 2 steps (2 -> 1). If n = 3, you take 3 steps (3 -> 2 -> 1). Therefore if n is 1, 2 or 3, you take n steps to go to 0. OK, less talking, more coding:
static int downToZero(int n) {
if (n == 1 || n == 2 || n == 3) return n;
int sqrt = (int) Math.sqrt(n);
for (int i = sqrt; i > 1; i--) {
if (n / i * i == n) {
return 1 + downToZero(n / i);
}
}
return 1 + downToZero(n - 1);
}
Notice that I'm stopping when i equals 2, I know that if I reach 1, it's a prime number and I need to go a step forward and look at n - 1.
However, I have tried to see the steps your algorithm and mine takes, so I've added a print statement each time n changes, and we both have the same succession: 7176, 92, 23, 22, 11, 10, 5, 4, 2, 1, which returns 10. Isn't that correct?

So, I found a solution which is working for all the test cases -
static final int LIMIT = 1_000_000;
static int[] solutions = buildSolutions();
public static int downToZero(int n) {
// Write your code here
return solutions[n];
}
static int[] buildSolutions() {
int[] solutions = new int[LIMIT + 1];
for (int i = 1; i < solutions.length; i++) {
solutions[i] = solutions[i - 1] + 1;
for (int j = 2; j * j <= i; j++) {
if (i % j == 0) {
solutions[i] = Math.min(solutions[i], solutions[i / j] + 1);
}
}
}
return solutions;
}
}

Maximum height of the staircase

Given an integer A representing the square blocks. The height of each square block is 1. The task is to create a staircase of max height using these blocks. The first stair would require only one block, the second stair would require two blocks and so on. Find and return the maximum height of the staircase.
Your submission failed for the following input: A : 92761
Your function returned the following : 65536
The expected returned value : 430
Approach:
We are interested in the number of steps and we know that each step Si uses exactly Bi number of bricks. We can represent this problem as an equation:
n * (n + 1) / 2 = T (For Natural number series starting from 1, 2, 3, 4, 5 …)
n * (n + 1) = 2 * T
n-1 will represent our final solution because our series in problem starts from 2, 3, 4, 5…
Now, we just have to solve this equation and for that we can exploit binary search to find the solution to this equation. Lower and Higher bounds of binary search are 1 and T.
CODE
public int solve(int A) {
int l=1,h=A,T=2*A;
while(l<=h)
{
int mid=l+(h-l)/2;
if((mid*(mid+1))==T)
return mid;
if((mid*(mid+1))>T && (mid!=0 && (mid*(mid-1))<=T) )
return mid-1;
if((mid*(mid+1))>T)
h=mid-1;
else
l=mid+1;
}
return 0;
}

To expand on the comment by Matt Timmermans:
You know that for n steps, you need (n * (n + 1))/2 blocks. You want know, if given B blocks, how many steps you can create.
So you have:
(n * (n + 1))/2 = B
(n^2 + n)/2 = B
n^2 + n = 2B
n^2 + n - 2B = 0
That looks suspiciously like something for which you'd use the quadratic formula.
In this case, a=1, b=1, and c=(-2B). Plugging the numbers into the formula:
n = ((-b) + sqrt(b^2 - 4*a*c))/(2*a)
= (-1 + sqrt(1 - 4*1*(-2B)))/(2*a)
= (-1 + sqrt(1 + 8B))/2
= (sqrt(1 + 8B) - 1)/2
So if you have 5050 blocks, you get:
n = (sqrt(1 + 40400) - 1)/2
= (sqrt(40401) - 1)/2
= (201 - 1)/2
= 100
Try it with the quadratic formula calculator. Use 1 for the value of a and b, and replace c with negative two times the number of blocks you're given. So in the example above, c would be -10100.
In your program, since you can't have a partial step, you'd want to truncate the result.

Why are you using all these formulas? A simple while() loop should do the trick, eventually, it's just a simple Gaussian Sum ..
public static int calculateStairs(int blocks) {
int lastHeight = 0;
int sum = 0;
int currentHeight = 0; //number of bricks / level
while (sum <= blocks) {
lastHeight = currentHeight;
currentHeight++;
sum += currentHeight;
}
return lastHeight;
}

So this should do the job as it also returns the expected value. Correct me if im wrong.
public int solve(int blocks) {
int current; //Create Variables
for (int x = 0; x < Integer.MAX_VALUE; x++) { //Increment until return
current = 0; //Set current to 0
//Implementation of the Gauss sum
for (int i = 1; i <= x; i++) { //Sum up [1,*current height*]
current += i;
} //Now we have the amount of blocks required for the current height
//Now we check if the amount of blocks is bigger than
// the wanted amount, and if so we return the last one
if (current > blocks) {
return x - 1;
}
}
return current;
}

Is there any better algorithm to do this? (Unit fractions creation)

I have been given a problem where fractions between 1/2 - 1/1000 have to be added to create the longest sequence of all unique unit fractions.
The rules on constructing these fractions:
Let create a set: D , D is only to hold unique unit fractions , sub-fractions can add up to the same fraction for example:
1/10
/ \
1/110 + 1/11 1/35 + 1/14
All sub-fractions can be held within the set as long as they themselves are not the same fractions once we are adding them together it is ok for them to total up to the same root.
The goal:
The fractions have to be added in a way to sum up to exactly 1. Any sub-fractions are not allowed to be over 1000 it is explicitly between 2 and 1000 hence the fractions which make up 1/1000 would not be applicable ( 1/1004 + 1/251000 ).
What currently I found to be the most effective:
Find the two lowest multiples which make-up the current fraction that I am looking at so for e.g 1/6 = A = 3 , B = 2. And now we complete the following equation: C = (A+B)*A , D = (A+B)*B. Now C & D are the sub-fractions which add up to my initial fraction
1/6
/ \
1/15 1/10
In code:
public static int[] provideFactorsSmallest(int n) {
int k[] = new int[2];
for(int i = 2; i <= n - 1; i++) {
if(n % i == 0) {
k[0] = i;
break;
}
}
for(int i = k[0] + 1; i <= n - 1 && k[0] != 0; i++) {
//System.out.println("I HAVE BEEN ENTERED");
if(k[0] * i == n) {
k[1] = i;
int firstTerm = k[0];
int secondTerm = k[1];
k[0] = (firstTerm + secondTerm) * firstTerm;
k[1] = (firstTerm + secondTerm) * secondTerm;
return k;
}
}
return null;
}
My question:
What would be the most effective way to pair and group the numbers to achieve possible longest fraction sequence?
Thank you in advance!

Collatz Sequence - printing every sequence from 1 until N

I have this HW assignment that I'm stuck in:
I need to write the sequence from 1 until N given seed.
for example:
if user inputs 4 v then I need to write every line from the first sequence until the 4th and then write down how many have reached 1 in the end and count the number of numbers.
example:
1 4 2 1 (4)
2 1 (2)
3 10 5 16 8 4 2 1 (8)
4 2 1 (3)
s.o.p :The first 4 hailstone sequences reached 1.
if user inputs 7 c then I only need to write the sentence The first 4 hailstone sequences reached 1.
so far I've written the code for the v part,
the part that works:
public class Collatz {
public static void main(String[] args){
int n = Integer.parseInt(args[0]);
String str = String.valueOf(args[1]);
int counter = 1;
if (str.equals("v")) {
while (n != 1)
{
System.out.print(n + " ");
// If n is odd
if ((n & 1) == 1) {
n = 3 * n + 1;
}
// If even
else{
n = n / 2;
}
counter++;
}
// Print 1 at the end
System.out.print(n + " (" + counter + ")");
}
}
}
I have tried putting a for loop to print from 1 to n in order to print like my example but it doesn't, my attempt:
` for (int i = 1; i < n; i = i+1){
while (i!= 1) {
System.out.print(i + " ");
// If n is odd
if ((i & 1) == 1)
i = 3 * i + 1;
// If even
else
i = i / 2;
}
// Print 1 at the end
System.out.print(i); `
no go. please help me debug this.

Solved it:
add: before the while
for (i = 1; i <= n; i = i+1){
hail = i; // we need to make sure i isn't run over
int counter = 1;