Well, after solving this problem by naive STL set,I was reading the forum entries,there I find this entry :
#include <iostream>
#include <cmath>
#define MAX 100
using namespace std;
int main(){
int res=(MAX-1)*(MAX-1);
for(int i=2;i<MAX;i++)
for(int j=i*i;j<=MAX;j=j*i)
res = res-int(MAX*(log(i)/log(j)))+1;
cout<<res<<endl;
return 0;
}
The author's explanation :
Maximum will be 99*99. I subtracted occurrences of those numbers which are powers of some lower numbers (2-100): -
For example: -
4^2,4^3,4^4 (i.e. 3 should be subtracted) as they will be duplicates from lower number powers as in 2^4,2^6,2^8
This program is giving correct answer check here but I am unable to get the implemented logic,to be precise I am not getting how the duplicates are determined. Could somebody help ?
I may be missing something, but it seems to me this program gives the wrong answer. It's off by one. If I set MAX to 10, it's off by two.
I have read that some players like to produce approximate answers and then dictionary-attack the Project Euler servers to brute-force the problem. Other players consider that rather against the spirit of the thing.
Anyway—an algorithm like this (starting with N*M and eliminating duplicates) is the right way to tackle the problem, but as written this code doesn't make much sense to me. Note that in any case int(MAX*(log(i)/log(j))) is very sensitive to rounding error; but even if you eliminate that source of error by using integer arithmetic, the program still gives the wrong answer.
EDIT: How can we (correctly) count the duplicates?
First you must understand that two numbers are only the same if they have the same prime factorization. So there are only going to be duplicates a1b1 = a2b2 when a1 and a2 are distinct integer powers of the same integer, which I'll call x. For example,
97 = 314; this is possible because 9 and 3 are both powers of 3.
86 = 49; this is possible because 8 and 4 are both powers of 2.
So we have established that for all duplicates, a1 = xe1 and a2 = xe2 for some integers x, e1, and e1.
Then with a little algebra,
a1b1 = a2b2
xe1b1 = xe2b2
e1b1 = e2b2
Going back to the earlier examples,
97 = 314 because 2×7 = 1×14.
86 = 49 because 3×6 = 2×9.
So to find all duplicates for any given x, you only need to find duplicates for the simpler expression eb where 2 ≤ xe ≤ 100 and 2 ≤ b ≤ 100.
Here is a picture of that simpler problem, for x=3 and b ranging only from 2 to 10. I've marked two places where there are duplicates.
e=1 a=3 *********
e=2 a=9 * * * * * * * * *
e=3 a=27 * * * * * * * * *
e=4 a=81 * * * * * * * * *
| |
1*8 = 2*4 = 4*2 3*8 = 4*6
3^8 = 9^4 = 81^2 27^8 = 81^6
And here are the duplicates:
e=1 a=3 *********
e=2 a=9 x x x x * * * * *
e=3 a=27 x x x * x * * * *
e=4 a=81 x x x x x * * * *
The C++ program you found is trying to count them by visiting each pair of overlapping rows i and j, and calculating how much of row i overlaps row j. But again, unless I'm missing something, the program seems hopelessly imprecise. And it misses some pairs of rows entirely (you never have i=9 and j=27, or i=27 and j=81).
first, it sets res to 99*99 at line 6, because MAX was defined as 100. Then it enters a loop, with the condition that i is smaller than MAX. then it enters this pseudocode loop
int i;
int j;
int x=2;
for( j = i2; j <= MAX , j = ix)
{
res = res- (MAX* ( jlog(i) )+1;
x++;
}
sorry 'bout the not using <pre><code> above; but if I did I could not use <sup>
Please note log(a)/log(x) is the same as xlog(a)
comments on question because <sup> does not work there:
2log(2) = 1 because 21 = 2
2log(4) = 2 because 22 = 2
log(x) == 10log(x)
log(10) = 1
glog(x) = y => gy = x
Well, the question involves ways to combine two numbers chosen from a range. There are 99 possible numbers, so the number of combinations is 99 * 99, with possible duplicates. His basic algorithm here is to figure out how many duplicates are present, and subtract that value from the maximum.
As for counting duplicates, it might help intuitively to think of the numbers in terms of their prime factors. Raising a number to an integer power means multiplying it by itself; so, represented as a list of primes, this is equivalent to simply concatenating the lists. For instance, 6 is {2, 3}, so 6^3 would be {2, 2, 2, 3, 3, 3}. Note that if you count how many times each prime appears in the list, x^n will always have the same proportions as x, for instance 6^n will have an equal quantity of 2's and 3's. So, any two numbers in the range with the same proportion between primes must both be powers of some number.
So, in the full list, each distinct proportion of prime factors will appear repeatedly as x^2, x^3, x^4..., (x^3)^2, (x^3)^4..., (x^4)^2..., etc., where x is the smallest number with that proportion; more precisely, (x^m)^n where (x^m) <= 100 and 2 <= n <= 100. Since (x^m)^n is equal to x^(mn), counting duplicates amounts to counting the ways that x^(mn) can also be <= 100.
There are (at least) two ways to approach this problem. One is to start your count of distinct values at 0, and add one for each calculated value that hasn't been seen before. The other way is to calculate the maximum number of values, and then subtract one for each duplicate.
The poster is attempting the second methed. a can range from 2 to 100 for 99 values, as can b, so there are 99 * 99 produced values. The poster then attempts to subtract the duplicate values to get the correct answer.
Edit: However, the poster has written an incorrect algorithm.
For instance, setting MAX = 8 or 9. For 8 it should give 44 but it gives 45. For 9 it should give 54 but gives 56. Either they lucked out and happened across an algorithm that gives the correct answer for some inputs, or they reverse-engineered an algorithm that worked when MAX = 100 but not for all other values.
Related
I need to find the sum of all multiples of 7 and 11 below 926. Here is what I have so far:
#include <iostream>
int main() {
int sum = 0;
for (for i = 1; sum<926; i++)
sum = sum + 7*i + 11*i;
std::cout << "The sum is "<<sum;
return 0;
}
You are calculating
(7+11)* (1+2+3+4+5+6+7+8+9+10) == 18*55 == 990
Your program stops at 990 because it is the first number > 926.
This is calculating "the lowest cumulative multiple of (7+11) greater or equal 926".
This is of course not what you want.
In order to find "the sum of all multiples of 7 and 11 below 926" you should check all numbers below 926 for being a multiple of 7 or 11.
I am not sure whether you are supposed to only add up those which are a multiple of 7 AND a multiple of 11 (which would be only the multiples of 77) or whether you are supposed to add up all which are a multiple of 7 and those which are a multiple of 11. This is a unclear area between English phrasing and programming expressions.
Read the assignment again, it might be clearer in there.
So lets simplify to "the sum of all multiples of 7 and 11 below 78", for discussing the algorithm instead of language precision.
The answer is either:
7+11+14+21+22+28+33+35+42+44+49+55+56+63+66+70+77
or
77
After you decided you will easily find a way to detect a multiple of something. (Hint: read up on % operator).
I intentionally do not give a full solution, or even code for it, because of:
How do I ask and answer homework questions?
There is a fable about Guass quickly responding to a teacher busy-work assignment to sum the numbers 1..100. Guass quickly answered 5050; that is (100 + 1) * 100/2. For an evenly spaced sequence, this can be summarized as (a[0] + a[n-1]) * n /2; since the average value of the sequence will be (a[0] + a[n-1]) / 2; and the number of elements n.
Thus, the sequence 2,4,...200 = (202 / 2) * 100 or 1010.
And the sequence 77,154,231, ... 77 * n will be ((77 * (n+1))/2) * n.
So, you just have to figure out N.
Given an array A with size N. Value of a subset of Array A is defined as product of all numbers in that subset. We have to return the product of values of all possible non-empty subsets of array A %(10^9+7).
E.G. array A {3,5}
` Value{3} = 3,
Value{5} = 5,
Value{3,5} = 5*3 = 15
answer = 3*5*15 %(10^9+7).
Can someone explain the mathematics behind the problem. I am thinking of solving it by combination to solve it efficiently.
I have tried using brute force it gives correct answer but it is way too slow.
Next approach is using combination. Now i think that if we take all the sets and multiply all the numbers in those set then we will get the correct answer. Thus i have to find out how many times a number is coming in calculation of answer. In the example 5 and 3 both come 2 times. If we look closely, each number in a will come same number of times.
You're heading in the right direction.
Let x be an element of the given array A. In our final answer, x appears p number of times, where p is equivalent to the number of subsets of A possible that include x.
How to calculate p? Once we have decided that we will definitely include x in our subset, we have two choices for the rest N-1 elements: either include them in set or do not. So, we conclude p = 2^(N-1).
So, each element of A appears exactly 2^(N-1) times in the final product. All remains is to calculate the answer: (a1 * a2 * ... * an)^p. Since the exponent is very large, you can use binary exponentiation for fast calculation.
As Matt Timmermans suggested in comments below, we can obtain our answer without actually calculating p = 2^(N-1). We first calculate the product a1 * a2 * ... * an. Then, we simply square this product n-1 times.
The corresponding code in C++:
int func(vector<int> &a) {
int n = a.size();
int m = 1e9+7;
if(n==0) return 0;
if(n==1) return (m + a[0]%m)%m;
long long ans = 1;
//first calculate ans = (a1*a2*...*an)%m
for(int x:a){
//negative sign does not matter since we're squaring
if(x<0) x *= -1;
x %= m;
ans *= x;
ans %= m;
}
//now calculate ans = [ ans^(2^(n-1)) ]%m
//we do this by squaring ans n-1 times
for(int i=1; i<n; i++){
ans = ans*ans;
ans %= m;
}
return (int)ans;
}
Let,
A={a,b,c}
All possible subset of A is ={{},{a},{b},{c},{a,b},{b,c},{c,a},{a,b,c,d}}
Here number of occurrence of each of the element are 4 times.
So if A={a,b,c,d}, then numbers of occurrence of each of the element will be 2^3.
So if the size of A is n, number of occurrence of eachof the element will be 2^(n-1)
So final result will be = a1^p*a2^pa3^p....*an^p
where p is 2^(n-1)
We need to solve x^2^(n-1) % mod.
We can write x^2^(n-1) % mod as x^(2^(n-1) % phi(mod)) %mod . link
As mod is a prime then phi(mod)=mod-1.
So at first find p= 2^(n-1) %(mod-1).
Then find Ai^p % mod for each of the number and multiply with the final result.
I read the previous answers and I was understanding the process of making sets. So here I am trying to put it in as simple as possible for people so that they can apply it to similar problems.
Let i be an element of array A. Following the approach given in the question, i appears p number of times in final answer.
Now, how do we make different sets. We take sets containing only one element, then sets containing group of two, then group of 3 ..... group of n elements.
Now we want to know for every time when we are making set of certain numbers say group of 3 elements, how many of these sets contain i?
There are n elements so for sets of 3 elements which always contains i, combinations are (n-1)C(3-1) because from n-1 elements we can chose 3-1 elements.
if we do this for every group, p = [ (n-1)C(x-1) ] , m going from 1 to n. Thus, p= 2^(n-1).
Similarly for every element i, p will be same. Thus we get
final answer= A[0]^p *A[1]^p...... A[n]^p
Please when answering this question try to be as general as possible to help the wider community, rather than just specifically helping my issue (although helping my issue would be great too ;) )
I seem to be encountering this problem time and time again with the simple problems on Project Euler. Most commonly are the problems that require a computation of the prime numbers - these without fail always fail to terminate for numbers greater than about 60,000.
My most recent issue is with Problem 12:
The sequence of triangle numbers is generated by adding the natural numbers. So the 7th triangle number would be 1 + 2 + 3 + 4 + 5 + 6 + 7 = 28. The first ten terms would be:
1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ...
Let us list the factors of the first seven triangle numbers:
1: 1
3: 1,3
6: 1,2,3,6
10: 1,2,5,10
15: 1,3,5,15
21: 1,3,7,21
28: 1,2,4,7,14,28
We can see that 28 is the first triangle number to have over five divisors.
What is the value of the first triangle number to have over five hundred divisors?
Here is my code:
#include <iostream>
#include <vector>
#include <cmath>
using namespace std;
int main() {
int numberOfDivisors = 500;
//I begin by looping from 1, with 1 being the 1st triangular number, 2 being the second, and so on.
for (long long int i = 1;; i++) {
long long int triangularNumber = (pow(i, 2) + i)/2
//Once I have the i-th triangular, I loop from 1 to itself, and add 1 to count each time I encounter a divisor, giving the total number of divisors for each triangular.
int count = 0;
for (long long int j = 1; j <= triangularNumber; j++) {
if (triangularNumber%j == 0) {
count++;
}
}
//If the number of divisors is 500, print out the triangular and break the code.
if (count == numberOfDivisors) {
cout << triangularNumber << endl;
break;
}
}
}
This code gives the correct answers for smaller numbers, and then either fails to terminate or takes an age to do so!
So firstly, what can I do with this specific problem to make my code more efficient?
Secondly, what are some general tips both for myself and other new C++ users for making code more efficient? (I.e. applying what we learn here in the future.)
Thanks!
The key problem is that your end condition is bad. You are supposed to stop when count > 500, but you look for an exact match of count == 500, therefore you are likely to blow past the correct answer without detecting it, and keep going ... maybe forever.
If you fix that, you can post it to code review. They might say something like this:
Break it down into separate functions for finding the next triangle number, and counting the factors of some number.
When you find the next triangle number, you execute pow. I perform a single addition.
For counting the number of factors in a number, a google search might help. (e.g. http://www.cut-the-knot.org/blue/NumberOfFactors.shtml ) You can build a list of prime numbers as you go, and use that to quickly find a prime factorization, from which you can compute the number of factors without actually counting them. When the numbers get big, that loop gets big.
Tldr: 76576500.
About your Euler problem, some math:
Preliminary 1:
Let's call the n-th triangle number T(n).
T(n) = 1 + 2 + 3 + ... + n = (n^2 + n)/2 (sometimes attributed to Gauss, sometimes someone else). It's not hard to figure it out:
1+2+3+4+5+6+7+8+9+10 =
(1+10) + (2+9) + (3+8) + (4+7) + (5+6) =
11 + 11 + 11 + 11 + 11 =
55 =
110 / 2 =
(10*10 + 10)/2
Because of its definition, it's trivial that T(n) + n + 1 = T(n+1), and that with a<b, T(a)<T(b) is true too.
Preliminary 2:
Let's call the divisor count D. D(1)=1, D(4)=3 (because 1 2 4).
For a n with c non-repeating prime factors (not just any divisors, but prime factors, eg. n = 42 = 2 * 3 * 7 has c = 3), D(n) is c^2: For each factor, there are two possibilites (use it or not). The 9 possibile divisors for the examples are: 1, 2, 3, 7, 6 (2*3), 14 (2*7), 21 (3*7), 42 (2*3*7).
More generally with repeating, the solution for D(n) is multiplying (Power+1) together. Example 126 = 2^1 * 3^2 * 7^1: Because it has two 3, the question is no "use 3 or not", but "use it 1 time, 2 times or not" (if one time, the "first" or "second" 3 doesn't change the result). With the powers 1 2 1, D(126) is 2*3*2=12.
Preliminary 3:
A number n and n+1 can't have any common prime factor x other than 1 (technically, 1 isn't a prime, but whatever). Because if both n/x and (n+1)/x are natural numbers, (n+1)/x - n/x has to be too, but that is 1/x.
Back to Gauss: If we know the prime factors for a certain n and n+1 (needed to calculate D(n) and D(n+1)), calculating D(T(n)) is easy. T(N) = (n^2 + n) / 2 = n * (n+1) / 2. As n and n+1 don't have common prime factors, just throwing together all factors and removing one 2 because of the "/2" is enough. Example: n is 7, factors 7 = 7^1, and n+1 = 8 = 2^3. Together it's 2^3 * 7^1, removing one 2 is 2^2 * 7^1. Powers are 2 1, D(T(7)) = 3*2 = 6. To check, T(7) = 28 = 2^2 * 7^1, the 6 possible divisors are 1 2 4 7 14 28.
What the program could do now: Loop through all n from 1 to something, always factorize n and n+1, use this to get the divisor count of the n-th triangle number, and check if it is >500.
There's just the tiny problem that there are no efficient algorithms for prime factorization. But for somewhat small numbers, todays computers are still fast enough, and keeping all found factorizations from 1 to n helps too for finding the next one (for n+1). Potential problem 2 are too large numbers for longlong, but again, this is no problem here (as can be found out with trying).
With the described process and the program below, I got
the 12375th triangle number is 76576500 and has 576 divisors
#include <iostream>
#include <vector>
#include <cstdint>
using namespace std;
const int limit = 500;
vector<uint64_t> knownPrimes; //2 3 5 7...
//eg. [14] is 1 0 0 1 ... because 14 = 2^1 * 3^0 * 5^0 * 7^1
vector<vector<uint32_t>> knownFactorizations;
void init()
{
knownPrimes.push_back(2);
knownFactorizations.push_back(vector<uint32_t>(1, 0)); //factors for 0 (dummy)
knownFactorizations.push_back(vector<uint32_t>(1, 0)); //factors for 1 (dummy)
knownFactorizations.push_back(vector<uint32_t>(1, 1)); //factors for 2
}
void addAnotherFactorization()
{
uint64_t number = knownFactorizations.size();
size_t len = knownPrimes.size();
for(size_t i = 0; i < len; i++)
{
if(!(number % knownPrimes[i]))
{
//dividing with a prime gets a already factorized number
knownFactorizations.push_back(knownFactorizations[number / knownPrimes[i]]);
knownFactorizations[number][i]++;
return;
}
}
//if this failed, number is a newly found prime
//because a) it has no known prime factors, so it must have others
//and b) if it is not a prime itself, then it's factors should've been
//found already (because they are smaller than the number itself)
knownPrimes.push_back(number);
len = knownFactorizations.size();
for(size_t s = 0; s < len; s++)
{
knownFactorizations[s].push_back(0);
}
knownFactorizations.push_back(knownFactorizations[0]);
knownFactorizations[number][knownPrimes.size() - 1]++;
}
uint64_t calculateDivisorCountOfN(uint64_t number)
{
//factors for number must be known
uint64_t res = 1;
size_t len = knownFactorizations[number].size();
for(size_t s = 0; s < len; s++)
{
if(knownFactorizations[number][s])
{
res *= (knownFactorizations[number][s] + 1);
}
}
return res;
}
uint64_t calculateDivisorCountOfTN(uint64_t number)
{
//factors for number and number+1 must be known
uint64_t res = 1;
size_t len = knownFactorizations[number].size();
vector<uint32_t> tmp(len, 0);
size_t s;
for(s = 0; s < len; s++)
{
tmp[s] = knownFactorizations[number][s]
+ knownFactorizations[number+1][s];
}
//remove /2
tmp[0]--;
for(s = 0; s < len; s++)
{
if(tmp[s])
{
res *= (tmp[s] + 1);
}
}
return res;
}
int main()
{
init();
uint64_t number = knownFactorizations.size() - 2;
uint64_t DTn = 0;
while(DTn <= limit)
{
number++;
addAnotherFactorization();
DTn = calculateDivisorCountOfTN(number);
}
uint64_t tn;
if(number % 2) tn = ((number+1)/2)*number;
else tn = (number/2)*(number+1);
cout << "the " << number << "th triangle number is "
<< tn << " and has " << DTn << " divisors" << endl;
return 0;
}
About your general question about speed:
1) Algorithms.
How to know them? For (relatively) simple problems, either reading a book/Wikipedia/etc. or figuring it out if you can. For harder stuff, learning more basic things and gaining experience is necessary before it's even possible to understand them, eg. studying CS and/or maths ... number theory helps a lot for your Euler problem. (It will help less to understand how a MP3 file is compressed ... there are many areas, it's not possible to know everything.).
2a) Automated compiler optimizations of frequently used code parts / patterns
2b) Manual timing what program parts are the slowest, and (when not replacing it with another algorithm) changing it in a way that eg. requires less data send to slow devices (HDD, hetwork...), less RAM memory access, less CPU cycles, works better together with OS scheduler and memory management strategies, uses the CPU pipeline/caches better etc.etc. ... this is both education and experience (and a big topic).
And because long variables have a limited size, sometimes it is necessary to use custom types that use eg. a byte array to store a single digit in each byte. That way, it's possible to use the whole RAM for a single number if you want to, but the downside is you/someone has to reimplement stuff like addition and so on for this kind of number storage. (Of course, libs for that exist already, without writing everything from scratch).
Btw., pow is a floating point function and may get you inaccurate results. It's not appropriate to use it in this case.
Is it possible for a given number s to just check that Is there any possible arithmetic progression having n terms and sum of these n terms results in s.
where starting element and difference of AP must not be zero.
for eg:
s = 24 & n = 4
yes, it is possible where AP is 3 5 7 9.
Note: I just want to check whether it is possible or not . No need to find the actual array. 0 < n < 10^9 & 0 < s < 10^18.
My Attempt:
we know that sum of an AP is equal to s = n(first+last)/2;
therefore first+last = 2*s/n;
2*s/n should be an integer.
we also know that last = first+(n-1)diff;
so my expression becomes 2*first + (n-1)diff = 2*s/n;
first = (2*s/n - (n-1)diff)/2; and it should be an integer for a particular value of diff.
this is my approach to doing this but its time complexity is too large to cover 10^18.
Please help. :)
Case 1: a and d are real numbers
Using s for the sum, n for the number of terms, a for the first term and d for the difference between terms, you get the result
2 * s / n = 2 * a + (n - 1) * d
This gives you one degree of freedom. So you can see that it's always possible to pick an infinite set of a and d values that satisfies this result.
Case 2: a and d are integer numbers
You can see from my result that if a and d are constrained to be integers, then the decomposition is only possible if the left hand side of this equation is also an integer; that is 2 * s is a multiple of n. (In your case, 2 * s is 48 which is a multiple of 4. So yes, there exists an integral a and d in that case).
Let a be the initial term of the progression and d its common difference. You want to solve the linear diophantine equation
n * a + (n*(n-1)/2) * d = s
The solution will exist if and only if s is a multiple ofgcd(n, n*(n-1)/2).
If n is odd, gcd(n, n*(n-1)/2) = n * gcd(1, (n-1)/2) = n.
If n is even, gcd(n, n*(n-1)/2) = (n/2) * gcd(2, n-1) = n/2.
In any case, the solution exists if and only if 2 * s is a multiple of n.
I think this is not possible in every case but if you can provide some more data then it can.
because there is multiple possibilities of same AP sum.
so in case you will give some hint it is possible
An array of integers A[i] (i > 1) is defined in the following way: an element A[k] ( k > 1) is the smallest number greater than A[k-1] such that the sum of its digits is equal to the sum of the digits of the number 4* A[k-1] .
You need to write a program that calculates the N th number in this array based on the given first element A[1] .
INPUT:
In one line of standard input there are two numbers seperated with a single space: A[1] (1 <= A[1] <= 100) and N (1 <= N <= 10000).
OUTPUT:
The standard output should only contain a single integer A[N] , the Nth number of the defined sequence.
Input:
7 4
Output:
79
Explanation:
Elements of the array are as follows: 7, 19, 49, 79... and the 4th element is solution.
I tried solving this by coding a separate function that for a given number A[k] calculates the sum of it's digits and finds the smallest number greater than A[k-1] as it says in the problem, but with no success. The first testing failed because of a memory limit, the second testing failed because of a time limit, and now i don't have any possible idea how to solve this. One friend suggested recursion, but i don't know how to set that.
Anyone who can help me in any way please write, also suggest some ideas about using recursion/DP for solving this problem. Thanks.
This has nothing to do with recursion and almost nothing with dynamic programming. You just need to find viable optimizations to make it fast enough. Just a hint, try to understand this solution:
http://codepad.org/LkTJEILz
Here is a simple solution in python. It only uses iteration, recursion is unnecessary and inefficient even for a quick and dirty solution.
def sumDigits(x):
sum = 0;
while(x>0):
sum += x % 10
x /= 10
return sum
def homework(a0, N):
a = [a0]
while(len(a) < N):
nextNum = a[len(a)-1] + 1
while(sumDigits(nextNum) != sumDigits(4 * a[len(a)-1])):
nextNum += 1
a.append(nextNum)
return a[N-1]
PS. I know we're not really supposed to give homework answers, but it appears the OP is in an intro to C++ class so probably doesn't know python yet, hopefully it just looks like pseudo code. Also the code is missing many simple optimizations which would probably make it too slow for a solution as is.
It is rather recursive.
The kernel of the problem is:
Find the smallest number N greater than K having digitsum(N) = J.
If digitsum(K) == J then test if N = K + 9 satisfies the condition.
If digitsum(K) < J then possibly N differs from K only in the ones digit (if the digitsum can be achieved without exceeding 9).
Otherwise if digitsum(K) <= J the new ones digit is 9 and the problem recurses to "Find the smallest number N' greater than (K/10) having digitsum(N') = J-9, then N = N'*10 + 9".
If digitsum(K) > J then ???
In every case N <= 4 * K
9 -> 18 by the first rule
52 -> 55 by the second rule
99 -> 189 by the third rule, the first rule is used during recursion
25 -> 100 requires the fourth case, which I had originally not seen the need for.
Any more counterexamples?