We Keep Coding

'Steal minimum number of items as a thief' problem

One of the professors of mine asked this question;
Imagine a thief entering a house. In the house, there are infinitely many items
that can have only one of three different weights: 1 kg, 3 kgs, and 5 kgs. All of the items are
discrete. The thief has a bag capacity of n kgs and strangely, he wants to steal the “smallest
number of items”.
He wants us to: Show that the greedy choice of taking the largest weight items into the bag first fails to lead to an optimal solution. But I claim that greedy is not failing. In any case taking as much as 5kg item is resulting in minimum number of items which is optimal. Is he wrong? I think greedy is optimal. Is there any case that greedy fails?
By the way, my solution:
public int stealRecursive(int bagCapacity) {
return stealRecursive(bagCapacity, 0);
}
private int stealRecursive(int bagCapacity, int numberOfItemsStolen) {
boolean canSteal5kg = bagCapacity - 5 >= 0;
boolean canSteal3kg = bagCapacity - 3 >= 0;
boolean canSteal1kg = bagCapacity - 1 >= 0;
if (canSteal5kg) {
return stealRecursive(bagCapacity - 5, numberOfItemsStolen + 1);
}
if (canSteal3kg) {
return stealRecursive(bagCapacity - 3, numberOfItemsStolen + 1);
}
if (canSteal1kg) {
return stealRecursive(bagCapacity - 1, numberOfItemsStolen + 1);
}
return numberOfItemsStolen;
}
Some of you stated that putting the code is not pointing anywhere, you are right I just put it to show both my effort and way of thinking. Because whenever I ask a problem without putting my code, I've been warned to show my effort first, due this is not a homework site. That's why I put my code. Sorry for confusing.

First, let's suppose that you have "taken" as many 5k items as possible, so you end up having
m = capacity mod 5
items to be stolen and you have already stolen 5n kilograms.
Cases
m == 0
5n
In this case you have n items and if you have stolen 1k or 3k items, then it would be worse (except for n = 0, in which case it does not make a difference whether you steal 0 items of 5 kilograms, 0 items of 3 kilograms or 0 items of 1 kilogram)
m == 1
5n + 1
In this case you have stolen n items of 5 kilograms and you steal an item of 1 kilogram additionally.
In the case of capacity = 6, you can steal 5 + 1 kilograms or 3 + 3 kilograms, leading to the same result, but the greater n is, the greater is the advantage of the greedy approach.
m == 2
We have 5n + 1 + 1
in the case of capacity = 7, we have 5 + 1 + 1 vs 3 + 3 + 1, but in general, greedy is better here as well.
m == 3
5n + 3
This is much better than 5n + 1 + 1 + 1
m == 4
5n + 3 + 1
In the case of 9, we have 5 + 3 + 1 vs 3 + 3 + 3, but in general, greedy is better
Conclusion
In general, greedy is better, but in some cases there is a tie. The reason is that there is an infinity of items that can be stolen. If there would be finite items of 5, 3, and 1 kilograms, respectively, then we can imagine scenarios like
5k items: 1
3k items: 3
1k items: 0
capacity: 9
Now, if you take the 5k item, then you will end up with a loot of 8, instead of a loot of 9. But we have infinite 5k, 3k and 1k items, so this is not a real scenario.

append strings with increasing frequency

You are given two strings S and T. An infinitely long string is formed in the following manner:
Take an empty string,
Append S one time,
Append T two times,
Append S three times,
Append T four times,
and so on, appending the strings alternately and increasing the number of repetitions by 1 each time.
You will also be given an integer K.
You need to tell the Kth Character of this infinitely long string.
Sample Input (S, T, K):
a
bc
4
Sample Output:
b
Sample Explanation:
The string formed will be "abcbcaaabcbcbcbcaaaaa...". So the 4th character is "b".
My attempt:
public class FindKthCharacter {
public char find(String S, String T, int K) {
// lengths of S and T
int s = S.length();
int t = T.length();
// Counters for S and T
int sCounter = 1;
int tCounter = 2;
// To store final chunks of string
StringBuilder sb = new StringBuilder();
// Loop until K is greater than zero
while (K > 0) {
if (K > sCounter * s) {
K -= sCounter * s;
sCounter += 2;
if (K > tCounter * t) {
K -= tCounter * t;
tCounter += 2;
} else {
return sb.append(T.repeat(tCounter)).charAt(K - 1);
}
} else {
return sb.append(S.repeat(sCounter)).charAt(K - 1);
}
}
return '\u0000';
}
}
But is there any better way to reduce its time complexity?

I've tried to give a guide here, rather than just give the solution.
If s and t are the lengths of the strings S and T, then you need to find the largest odd n such that
(1+3+5+...+n)s + (2+4+6+...+(n+1))t < K.
You can simplify these expressions to get a quadratic equation in n.
Let N be (1+3+..+n)s + (2+4+6+...+(n+1))t. You know that K will lie either in the next (n+2) copies of S, or the (n+3) copies of T that follow. Compare K to N+(n+2)s, and take the appropriate letter of either S or T using a modulo.
The only difficult step here is solving the large quadratic, but you can do it in O(log K) arithmetic operations easily enough by doubling n until it's too large, and then using a binary search on the remaining range. (If K is not too large so that floating point is viable, you can do it in O(1) time using the well-known quadratic formula).

Here my quick attempt, there probably is a better solution. Runtime is still O(sqrt n), but memory is O(1).
public static char find(String a, String b, int k) {
int lenA = a.length();
int lenB = b.length();
int rep = 0;
boolean isA = false;
while (k >= 0) {
++rep;
isA = !isA;
k -= (isA ? lenA : lenB) * rep;
}
int len = (isA ? lenA : lenB);
int idx = (len * rep + k) % len;
return (isA ? a : b).charAt(idx);
}

Here's a O(1) solution that took me some time to come up with (read I would have failed an interview on time). Hopefully the process is clear and you can implement it in code.
Our Goal is to return the char that maps to the kth index.
But How? Just 4 easy steps, actually.
Step 1: Find out how many iterations of our two patterns it would take to represent at least k characters.
Step 2: Using this above number of iterations i, return how many characters are present in the previous i-1 iterations.
Step 3: Get the number of characters n into iteration i that our kth character is. (k - result of step 2)
Step 4: Mod n by the length of the pattern to get index into pattern for the specific char. If i is odd, look into s, else look into t.
For step 1, we need to find a formula to give us the iteration i that character k is in. To derive this formula, it may be easier to first derive the formula needed for step 2.
Step 2's formula is basically given an iteration i, return how many characters are present in that iteration. We are solving for 'k' in this equation and are given i, while it's the opposite for step 1 where were are solving for i given k. If we can derive the equation of find k given i, then we can surely reverse it to find i given k.
Now, let's try to derive the formula for step 2 and find k given i. Here it's best to start with the most basic example to see the pattern.
s = "a", t = "b"
i=1 a
i=2 abb
i=3 abbaaa
i=4 abbaaabbbb
i=5 abbaaabbbbaaaaa
i=6 abbaaabbbbaaaaabbbbbb
Counting the total number of combined chars for each pattern during its next iteration gives us:
#iterations of pattern: 1 2 3 4 5 6 7 8 9 10
every new s iteration: 1, 4, 9, 16, 25, 36, 49, 64, 81, 100
every new t iteration: 2, 6, 12, 20, 30, 42, 56, 72, 90, 110
You might notice some nice patterns here. For example, s has a really nice formula to find out how many combined characters it has at any given iteration. It's simply (# of s iterations^2)*s.length. t also has a simple formula. It is (# of t iterations * (# of t iterations + 1))*t.length. You may have noticed that these formulas are the formulas for sum of odd and even numbers (if you did you get a kudos). This makes sense because each pattern's sum for an iteration i is the sum of all of its previous iterations.
Using s,t as length of their respective patterns, we now have the following formula to find the total number of chars at a given iteration.
#chars = s*(# of s iterations)^2 + t * (# of t iterations * (# of t iterations + 1))
Now we just need to do some math to get the number of iterations for each pattern given i.
# of s iterations given i = ceil(i/2.0)
# of t iterations given i = floor(i/2) which / operation gives us by default
Plugging these back into our formula we get:
total # of chars = s*(ceil(i/2.0)^2) + t*((i/2)*((i/2)+1))
We have just completed step 2, and we now know at any given iteration how many total chars there are. We could stop here and start picking random iterations and adjusting accordingly until we get near k, but we can do better than that. Let's use the above formula now to complete step 1 which we skipped. We just need to reorganize our equation to solve for i now.
Doing some simplyfying we get:
// 2
// i i i
// s (-) + t - ( - + 1 ) = k
// 2 2 2
// ----------------------------
// 2
// i t i
// s - + - ( - + 1 )i = k
// 4 2 2
// ----------------------------
// 2 2
// si ti ti
// ---- + ---- + ---- - k = 0
// 4 4 2
// ----------------------------
//
// 2 2
// si + ti + 2ti - 4k = 0
// ----------------------------
// 2
// (s + t)i + 2ti - 4k = 0
// ----------------------------
This looks like a polynomial. Wow! You're right! That means we can solve it using the quadratic formula.
A=(s+t), B=2t, C=-4k
quadratic formula = (-2t + sqrt(2t^2 + 16(s+t)k)) / 2(s+t)
This is our formula for step 1, and it will give us the iteration that the kth character is on. We just need to ceil it. I'm actually not smart enough to know why this works. It just does. Here is a desmos graph that graphs our two polynomials from step 2: s(Siterations)^2 and t(Titerations (Titerations + 1)).
The area under both curves is our total number of chars at an iteration (the vertical line). The formula from step 1 is also graphed, and we can see that for any s, t, k that the x intercept (which represents our xth iteration) is always: previous iteration < x <= current iteration, which is why the ceil works.
We have now completed steps 1 and 2. We have a formula to get the ith iteration that the kth character is on and a formula that gives us how many characters are in an ith iteration. Steps 3 and 4 should follow and we get our answer. This is constant time.

How would I go about analyzing this algorithm using "Big Oh" notation and how could I improve the run time of this algorithm?

The algorithm is explained by:
if n is even: return 1 + g(n/2).
if n is odd: return 1 + g(n-1).
if n = 1: return 1.
Code:
public static int g(int n)
{
if (n==1)
return 1;
else if (n%2==0)
return 1 + g(n/2);
else
return 1 + g(n-1);
}

When a number is even the right most bit in its binary representation is 0. Dividing a number by 2 removes this zero.
N = 16 => 8 => 4 => 2 => 1
(10000)2 => (1000)2 => (100)2 => (10)2 => 1
When a number is odd the right most bit in its binary representation is 1. The algorithm decerements the number when it receives an odd number. Decrementing an odd number will result in changing the rightmost bit from 1 to 0. So the number becomes even and the algorithm then divides this number by 2 so the right most bit will be removed.
So the worst case of the algorithm happens when binary representation of the number is consist of all 1s:
1111111111111
When this happens what the algorithm does is remove each 1 in two steps
1111111111111 decrement it because it is odd
1111111111110 divide it by two because it even
111111111111
So in the worst case it takes 2* number of 1s to reach to 1. The number of 1s is proportionate to log2N. So the algorithm belongs to O(logN).

complexity : log (n)
Explanation:
If you look at the binary notation of n and g(n).
*****0 => ***** (left shift)
*****1 => ****0 (change last 1 to 0) => **** (left shift)
So, it reduces one bit per 2 iterations in the worst case running time.
So total number of operations: 2*log2(n) = O( log2(n) ) .

O(lg(n)): If the input is a power of 2, every call divides by 2, which is obviously lg(n). For any other input, at least every second operation divides by 2 (if one operation subtracts one, the input was off before and is now even). So there are at most 2*log(n) operations, which is O(lg(n)).

Effiecient Algorithm for Finding if a Very Big Number is Divisible by 7

So this was a question on one of the challenges I came across in an online competition, a few days ago.
Question:
Accept two inputs.
A big number of N digits,
The number of questions Q to be asked.
In each of the question, you have to find if the number formed by the string between indices Li and Ri is divisible by 7 or not.
Input:
First line contains the number consisting on N digits. Next line contains Q, denoting the number of questions. Each of the next Q lines contains 2 integers Li and Ri.
Output:
For each question, print "YES" or "NO", if the number formed by the string between indices Li and Ri is divisible by 7.
Constraints:
1 ≤ N ≤ 105
1 ≤ Q ≤ 105
1 ≤ Li, Ri ≤ N
Sample Input:
357753
3
1 2
2 3
4 4
Sample Output:
YES
NO
YES
Explanation:
For the first query, number will be 35 which is clearly divisible by 7.
Time Limit: 1.0 sec for each input file.
Memory Limit: 256 MB
Source Limit: 1024 KB
My Approach:
Now according to the constraints, the maximum length of the number i.e. N can be upto 105. This big a number cannot be fitted into a numeric data structure and I am pretty sure thats not the efficient way to go about it.
First Try:
I thought of this algorithm to apply the generic rules of division to each individual digit of the number. This would work to check divisibility amongst any two numbers, in linear time, i.e. O(N).
static String isDivisibleBy(String theIndexedNumber, int divisiblityNo){
int moduloValue = 0;
for(int i = 0; i < theIndexedNumber.length(); i++){
moduloValue = moduloValue * 10;
moduloValue += Character.getNumericValue(theIndexedNumber.charAt(i));
moduloValue %= divisiblityNo;
}
if(moduloValue == 0){
return "YES";
} else{
return "NO";
}
}
But in this case, the algorithm has to also loop through all the values of Q, which can also be upto 105.
Therefore, the time taken to solve the problem becomes O(Q.N) which can also be considered as Quadratic time. Hence, this crossed the given time limit and was not efficient.
Second Try:
After that didn't work, I tried searching for a divisibility rule of 7. All the ones I found, involved calculations based on each individual digit of the number. Hence, that would again result in a Linear time algorithm. And hence, combined with the number of Questions, it would amount to Quadratic Time, i.e. O(Q.N)
I did find one algorithm named Pohlman–Mass method of divisibility by 7, which suggested
Using quick alternating additions and subtractions: 42,341,530
-> 530 − 341 = 189 + 42 = 231 -> 23 − (1×2) = 21 YES
But all that did was, make the time 1/3rd Q.N, which didn't help much.
Am I missing something here? Can anyone help me find a way to solve this efficiently?
Also, is there a chance this is a Dynamic Programming problem?

There are two ways to go through this problem.
1: Dynamic Programming Approach
Let the input be array of digits A[N].
Let N[L,R] be number formed by digits L to R.
Let another array be M[N] where M[i] = N[1,i] mod 7.
So M[i+1] = ((M[i] * 10) mod 7 + A[i+1] mod 7) mod 7
Pre-calculate array M.
Now consider the expression.
N[1,R] = N[1,L-1] * 10R-L+1 + N[L,R]
implies (N[1,R] mod 7) = (N[1,L-1] mod 7 * (10R-L+1mod 7)) + (N[L,R] mod 7)
implies N[L,R] mod 7 = (M[R] - M[L-1] * (10R-L+1 mod 7)) mod 7
N[L,R] mod 7 gives your answer and can be calculated in O(1) as all values on right of expression are already there.
For 10R-L+1 mod 7, you can pre-calculate modulo 7 for all powers of 10.
Time Complexity :
Precalculation O(N)
Overall O(Q) + O(N)
2: Divide and Conquer Approach
Its a segment tree solution.
On each tree node you store the mod 7 for the number formed by digits in that node.
And the expression given in first approach can be used to find the mod 7 of parent by combining the mod 7 values of two children.
The time complexity of this solution will be O(Q log N) + O(N log N)

Basically you want to be able to to calculate the mod 7 of any digits given the mod of the number at any point.
What you can do is to;
record the modulo at each point O(N) for time and space. Uses up to 100 KB of memory.
take the modulo at the two points and determine how much subtracting the digits before the start would make e.g. O(N) time and space (once not per loop)
e.g. between 2 and 3 inclusive
357 % 7 = 0
3 % 7 = 3 and 300 % 7 = 6 (the distance between the start and end)
and 0 != 6 so the number is not a multiple of 7.
between 4 and 4 inclusive
3577 % 7 == 0
357 % 7 = 0 and 0 * 10 % 7 = 0
as 0 == 0 it is a multiple of 7.

You first build a list of digits modulo 7 for each number starting with 0 offset (like in your case, 0%7, 3%7, 35%7, 357%7...) then for each case of (a,b) grab digits[a-1] and digits[b], then multiply digits[b] by 1-3-2-6-4-5 sequence of 10^X modulo 7 defined by (1+b-a)%6 and compare. If these are equal, return YES, otherwise return NO. A pseudocode:
readString(big);
Array a=[0]; // initial value
Array tens=[1,3,2,6,4,5]; // quick multiplier lookup table
int d=0;
int l=big.length;
for (int i=0;i<l;i++) {
int c=((int)big[i])-48; // '0' -> 0, and "big" has characters
d=(3*d+c)%7;
a.push(d); // add to tail
}
readInt(q);
for (i=0;i<q;i++) {
readInt(li);
readInt(ri); // get question
int left=(a[li-1]*tens[(1+ri-li)%6])%7;
if (left==a[ri]) print("YES"); else print("NO");
}
A test example:
247761901
1
5 9
61901 % 7=0. Calculating:
a = [0 2 3 2 6 3 3 4 5 2]
li = 5
ri = 9
left=(a[5-1]*tens[(1+9-5)%6])%7 = (6*5)%7 = 30%7 = 2
a[ri]=2
Answer: YES

Tennis tournament algorithm

After a tennis tournament each player was asked how many matches he had.
An athlete can't play more than one match with another athlete.
As an input the only thing you have is the number of athletes and the matches each athlete had. As an output you will have 1 if the tournament was possible to be done according to the athletes answers or 0 if not. For example:
Input: 4 3 3 3 3 Output: 1
Input: 6 2 4 5 5 2 1 Output: 0
Input: 2 1 1 Output: 1
Input: 1 0 Output: 0
Input: 3 1 1 1 Output: 0
Input: 3 2 2 0 Output: 0
Input: 3 4 3 2 Output: 0
the first number of the input is not part of the athletes answer it's the number of athletes that took part in the tournament for example in 6 2 4 5 5 2 1 we have 6 athletes that took part and their answers were 2 4 5 5 2 1.
So far this is what we wrote but didn't work that great:
import java.util.Scanner;
import java.util.Arrays;
public class Tennis {
public static void main(String[] args) {
Scanner input = new Scanner(System.in);
String N;
int count;
int sum = 0;
int max;
int activeAthletes;
int flag;
System.out.printf("Give: ");
N = input.nextLine();
String[] arr = N.split(" ");
int[] array = new int[arr.length];
for (count = 0; count < arr.length; count++) {
array[count] = Integer.parseInt(arr[count]);
//System.out.print(arr[count] + " ");
}
for (count = 1; count < arr.length; count++) {
sum += array[count];
}
//System.out.println("\n" + sum);
activeAthletes = array[0];
for (count = 1; count < array.length; count++) {
if (array[count] == 0) {
activeAthletes--;
}
}
max = array[1];
for (count = 2; count < array.length; count++) {
if (array[count] > max) {
max = array[count];
}
}
// System.out.println(max);
if ((sum % 2 == 0) && (max < activeAthletes)) {
flag = 1;
} else{
flag = 0;
}
System.out.println(flag);
}
}
I do not want a straight solution just maybe some tips and hints because we really have no idea what else to do and I repeat even though I'll tag it as a homework (because I feel the moderators will close it again) it is not, it's just something my brother found and we are trying to solve.
Well many of you have answered and I'm really grateful but as I have work tomorrow I need to go to sleep, so I'll probably read the rest of the answers tomorrow and see what works

Not sure if it works 100%, i would go like:
Sort input
for each element going from right to left in array (bigger to smaller)
based on value n of element at index i decrease n left elements by 1
return fail if cant decrease because you reached end of list or value 0
return success.
This logic (if correct) can lead whit some modifications to O(N*log(N)) solution, but I currently think that that would be just too much for novice programmer.
EDIT:
This does not work correct on input
2 2 1 1
All steps are then (whitout sorting):
while any element in list L not 0:
find largest element N in list L
decrease N other values in list L by 1 if value >= 1 (do not decrease this largest element)
return fail if failure at this step
set this element N on 0
return OK

Here's a hint. Answer these questions
Given N athletes, what is the maximum number of matches?
Given athlete X, what is the maximum number of matches he could do?
Is this sufficient to check just these? If you're not sure, try writing a program to generate every possible matching of players and check if at least one satisfies the input. This will only work for small #s of atheletes, but it's a good exercise. Or just do it by hand
Another way of asking this question, can we create a symmetric matrix of 1s and 0s whose rows are equal the values. This would be the table of 'who played who'. Think of this like an N by N grid where grid[i][j] = grid[j][i] (if you play someone they play you) and grid[i][i] = 0 (no one plays themselves)
For example
Input: 4 3 3 3 3 Output: 1
Looks like
0 1 1 1
1 0 1 1
1 1 0 1
1 1 1 0
We can't do this with this one, though:
Input: 3 2 2 0 Output: 0
EDIT
This is equivalent to this (from Degree (graph theory))
Hakimi (1962) proved that (d1, d2, ..., dn) is a degree sequence of a
simple graph if and only if (d2 − 1, d3 − 1, ..., dd1+1 − 1, dd1+2,
dd1+3, ..., dn) is. This fact leads to a simple algorithm for finding
a simple graph that has a given realizable degree sequence:
Begin with a graph with no edges.
Maintain a list of vertices whose degree requirement has not yet been met in non-increasing order of residual degree requirement.
Connect the first vertex to the next d1 vertices in this list, and then remove it from the list. Re-sort the list and repeat until all
degree requirements are met.
The problem of finding or estimating the number of graphs with a given
degree sequence is a problem from the field of graph enumeration.

Maybe you can take the array of athletes' match qties, and determine the largest number in there.
Then see if you can split that number into 1's and subtract those 1's from a few other members of the array.
Zero out that largest number array member, and remove it from the array, and update the other members with reduced values.
Now, repeat the process - determine the new largest number, and subtract it from other members of the array.
If at any point there are not enough array members to subtract the 1's from, then have the app return 0. otherwise continue doing it until there are no more members in the array, at which point you can have the app return 1.
Also, remember to remove array members that were reduced down to zero.

Your examples can all trivially be solved by counting the matches and looking whether they divide by 2.
A problem not covered by your examples would be a player, who has more games than the sum of the other players:
Input: 4 5 1 1 1 Output: 0
This can be complicated if we add more players:
Input: 6 5 5 5 1 1 1 Output: 0
How to solve this question? Well, remove one game pairwise from the maximum and the minimum player, and see what happens:
Input: 6 5 5 5 1 1 1
Input: 5 5 5 4 1 1 -
Input: 4 5 4 4 1 - -
Input: 3 4 4 4 - - -
It violates the rule:
An athlete can't play more than one match with another athlete.
If 3 players are left, they can't have had more than 2 games each.

Edit: Below solution passes some invalid inputs as valid. It's a fast way to check for definite negatives, but it allows false positives.
Here's what a mathematician would suggest:
The sum of the number of matches must be even. 3 3 4 2 1 sums to 13, which would imply someone played a match against themselves.
For n players, if every match eliminates one player at least n-1 distinct matches must be played. (A knockout tournament.) To see this, draw a tree of matches for 2, 4, 8, 16, 32... players, requiring 1, 3, 7, 31... matches to decide a winner.
For n players, the maximum number of matches if everyone plays everyone once, assuming no repeat matches, is n choose 2, or (n!)/(2!)(n-2)! (Round robin tournament). n! is the factorial function, n! = n * n-1 * n-2 * ... * 3 * 2 * 1..
So the criteria are:
Sum of the number of matches must be even.
Sum of the number of matches must be at least 2n-2. (Note the multiplication by 2 - each match results in both players increasing their count by one.)
Sum of the number of matches must be at most 2 * n choose 2.
[Edit] Each player must participate in at least one match.
If your tournament is a cross between a knockout tournament and a round robin tournament, you could have somewhere between n-1 and n choose 2 matches.
Edit:
If any player plays more than n-1 matches, they played someone at least twice.
If your tournament is a knockout tournament ordered so that each player participates in as few matches as possible, then each player can participate in at most log_2(n) matches or so (Take log base 2 and round up.) In a tournament with 16 players, at most 4 matches. In a tournament of 1024 players, at most 10 matches.