I have gone through Google and Stack Overflow search, but nowhere I was able to find a clear and straightforward explanation for how to calculate time complexity.
What do I know already?
Say for code as simple as the one below:
char h = 'y'; // This will be executed 1 time
int abc = 0; // This will be executed 1 time
Say for a loop like the one below:
for (int i = 0; i < N; i++) {
Console.Write('Hello, World!!');
}
int i=0; This will be executed only once.
The time is actually calculated to i=0 and not the declaration.
i < N; This will be executed N+1 times
i++ This will be executed N times
So the number of operations required by this loop are {1+(N+1)+N} = 2N+2. (But this still may be wrong, as I am not confident about my understanding.)
OK, so these small basic calculations I think I know, but in most cases I have seen the time complexity as O(N), O(n^2), O(log n), O(n!), and many others.
How to find time complexity of an algorithm
You add up how many machine instructions it will execute as a function of the size of its input, and then simplify the expression to the largest (when N is very large) term and can include any simplifying constant factor.
For example, lets see how we simplify 2N + 2 machine instructions to describe this as just O(N).
Why do we remove the two 2s ?
We are interested in the performance of the algorithm as N becomes large.
Consider the two terms 2N and 2.
What is the relative influence of these two terms as N becomes large? Suppose N is a million.
Then the first term is 2 million and the second term is only 2.
For this reason, we drop all but the largest terms for large N.
So, now we have gone from 2N + 2 to 2N.
Traditionally, we are only interested in performance up to constant factors.
This means that we don't really care if there is some constant multiple of difference in performance when N is large. The unit of 2N is not well-defined in the first place anyway. So we can multiply or divide by a constant factor to get to the simplest expression.
So 2N becomes just N.
This is an excellent article: Time complexity of algorithm
The below answer is copied from above (in case the excellent link goes bust)
The most common metric for calculating time complexity is Big O notation. This removes all constant factors so that the running time can be estimated in relation to N as N approaches infinity. In general you can think of it like this:
statement;
Is constant. The running time of the statement will not change in relation to N.
for ( i = 0; i < N; i++ )
statement;
Is linear. The running time of the loop is directly proportional to N. When N doubles, so does the running time.
for ( i = 0; i < N; i++ ) {
for ( j = 0; j < N; j++ )
statement;
}
Is quadratic. The running time of the two loops is proportional to the square of N. When N doubles, the running time increases by N * N.
while ( low <= high ) {
mid = ( low + high ) / 2;
if ( target < list[mid] )
high = mid - 1;
else if ( target > list[mid] )
low = mid + 1;
else break;
}
Is logarithmic. The running time of the algorithm is proportional to the number of times N can be divided by 2. This is because the algorithm divides the working area in half with each iteration.
void quicksort (int list[], int left, int right)
{
int pivot = partition (list, left, right);
quicksort(list, left, pivot - 1);
quicksort(list, pivot + 1, right);
}
Is N * log (N). The running time consists of N loops (iterative or recursive) that are logarithmic, thus the algorithm is a combination of linear and logarithmic.
In general, doing something with every item in one dimension is linear, doing something with every item in two dimensions is quadratic, and dividing the working area in half is logarithmic. There are other Big O measures such as cubic, exponential, and square root, but they're not nearly as common. Big O notation is described as O ( <type> ) where <type> is the measure. The quicksort algorithm would be described as O (N * log(N )).
Note that none of this has taken into account best, average, and worst case measures. Each would have its own Big O notation. Also note that this is a VERY simplistic explanation. Big O is the most common, but it's also more complex that I've shown. There are also other notations such as big omega, little o, and big theta. You probably won't encounter them outside of an algorithm analysis course. ;)
Taken from here - Introduction to Time Complexity of an Algorithm
1. Introduction
In computer science, the time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the string representing the input.
2. Big O notation
The time complexity of an algorithm is commonly expressed using big O notation, which excludes coefficients and lower order terms. When expressed this way, the time complexity is said to be described asymptotically, i.e., as the input size goes to infinity.
For example, if the time required by an algorithm on all inputs of size n is at most 5n3 + 3n, the asymptotic time complexity is O(n3). More on that later.
A few more examples:
1 = O(n)
n = O(n2)
log(n) = O(n)
2 n + 1 = O(n)
3. O(1) constant time:
An algorithm is said to run in constant time if it requires the same amount of time regardless of the input size.
Examples:
array: accessing any element
fixed-size stack: push and pop methods
fixed-size queue: enqueue and dequeue methods
4. O(n) linear time
An algorithm is said to run in linear time if its time execution is directly proportional to the input size, i.e. time grows linearly as input size increases.
Consider the following examples. Below I am linearly searching for an element, and this has a time complexity of O(n).
int find = 66;
var numbers = new int[] { 33, 435, 36, 37, 43, 45, 66, 656, 2232 };
for (int i = 0; i < numbers.Length - 1; i++)
{
if(find == numbers[i])
{
return;
}
}
More Examples:
Array: Linear Search, Traversing, Find minimum etc
ArrayList: contains method
Queue: contains method
5. O(log n) logarithmic time:
An algorithm is said to run in logarithmic time if its time execution is proportional to the logarithm of the input size.
Example: Binary Search
Recall the "twenty questions" game - the task is to guess the value of a hidden number in an interval. Each time you make a guess, you are told whether your guess is too high or too low. Twenty questions game implies a strategy that uses your guess number to halve the interval size. This is an example of the general problem-solving method known as binary search.
6. O(n2) quadratic time
An algorithm is said to run in quadratic time if its time execution is proportional to the square of the input size.
Examples:
Bubble Sort
Selection Sort
Insertion Sort
7. Some useful links
Big-O Misconceptions
Determining The Complexity Of Algorithm
Big O Cheat Sheet
Several examples of loop.
O(n) time complexity of a loop is considered as O(n) if the loop variables is incremented / decremented by a constant amount. For example following functions have O(n) time complexity.
// Here c is a positive integer constant
for (int i = 1; i <= n; i += c) {
// some O(1) expressions
}
for (int i = n; i > 0; i -= c) {
// some O(1) expressions
}
O(nc) time complexity of nested loops is equal to the number of times the innermost statement is executed. For example, the following sample loops have O(n2) time complexity
for (int i = 1; i <=n; i += c) {
for (int j = 1; j <=n; j += c) {
// some O(1) expressions
}
}
for (int i = n; i > 0; i += c) {
for (int j = i+1; j <=n; j += c) {
// some O(1) expressions
}
For example, selection sort and insertion sort have O(n2) time complexity.
O(log n) time complexity of a loop is considered as O(log n) if the loop variables is divided / multiplied by a constant amount.
for (int i = 1; i <=n; i *= c) {
// some O(1) expressions
}
for (int i = n; i > 0; i /= c) {
// some O(1) expressions
}
For example, [binary search][3] has _O(log n)_ time complexity.
O(log log n) time complexity of a loop is considered as O(log log n) if the loop variables is reduced / increased exponentially by a constant amount.
// Here c is a constant greater than 1
for (int i = 2; i <=n; i = pow(i, c)) {
// some O(1) expressions
}
//Here fun is sqrt or cuberoot or any other constant root
for (int i = n; i > 0; i = fun(i)) {
// some O(1) expressions
}
One example of time complexity analysis
int fun(int n)
{
for (int i = 1; i <= n; i++)
{
for (int j = 1; j < n; j += i)
{
// Some O(1) task
}
}
}
Analysis:
For i = 1, the inner loop is executed n times.
For i = 2, the inner loop is executed approximately n/2 times.
For i = 3, the inner loop is executed approximately n/3 times.
For i = 4, the inner loop is executed approximately n/4 times.
…………………………………………………….
For i = n, the inner loop is executed approximately n/n times.
So the total time complexity of the above algorithm is (n + n/2 + n/3 + … + n/n), which becomes n * (1/1 + 1/2 + 1/3 + … + 1/n)
The important thing about series (1/1 + 1/2 + 1/3 + … + 1/n) is around to O(log n). So the time complexity of the above code is O(n·log n).
References:
1
2
3
Time complexity with examples
1 - Basic operations (arithmetic, comparisons, accessing array’s elements, assignment): The running time is always constant O(1)
Example:
read(x) // O(1)
a = 10; // O(1)
a = 1,000,000,000,000,000,000 // O(1)
2 - If then else statement: Only taking the maximum running time from two or more possible statements.
Example:
age = read(x) // (1+1) = 2
if age < 17 then begin // 1
status = "Not allowed!"; // 1
end else begin
status = "Welcome! Please come in"; // 1
visitors = visitors + 1; // 1+1 = 2
end;
So, the complexity of the above pseudo code is T(n) = 2 + 1 + max(1, 1+2) = 6. Thus, its big oh is still constant T(n) = O(1).
3 - Looping (for, while, repeat): Running time for this statement is the number of loops multiplied by the number of operations inside that looping.
Example:
total = 0; // 1
for i = 1 to n do begin // (1+1)*n = 2n
total = total + i; // (1+1)*n = 2n
end;
writeln(total); // 1
So, its complexity is T(n) = 1+4n+1 = 4n + 2. Thus, T(n) = O(n).
4 - Nested loop (looping inside looping): Since there is at least one looping inside the main looping, running time of this statement used O(n^2) or O(n^3).
Example:
for i = 1 to n do begin // (1+1)*n = 2n
for j = 1 to n do begin // (1+1)n*n = 2n^2
x = x + 1; // (1+1)n*n = 2n^2
print(x); // (n*n) = n^2
end;
end;
Common running time
There are some common running times when analyzing an algorithm:
O(1) – Constant time
Constant time means the running time is constant, it’s not affected by the input size.
O(n) – Linear time
When an algorithm accepts n input size, it would perform n operations as well.
O(log n) – Logarithmic time
Algorithm that has running time O(log n) is slight faster than O(n). Commonly, algorithm divides the problem into sub problems with the same size. Example: binary search algorithm, binary conversion algorithm.
O(n log n) – Linearithmic time
This running time is often found in "divide & conquer algorithms" which divide the problem into sub problems recursively and then merge them in n time. Example: Merge Sort algorithm.
O(n2) – Quadratic time
Look Bubble Sort algorithm!
O(n3) – Cubic time
It has the same principle with O(n2).
O(2n) – Exponential time
It is very slow as input get larger, if n = 1,000,000, T(n) would be 21,000,000. Brute Force algorithm has this running time.
O(n!) – Factorial time
The slowest!!! Example: Travelling salesman problem (TSP)
It is taken from this article. It is very well explained and you should give it a read.
When you're analyzing code, you have to analyse it line by line, counting every operation/recognizing time complexity. In the end, you have to sum it to get whole picture.
For example, you can have one simple loop with linear complexity, but later in that same program you can have a triple loop that has cubic complexity, so your program will have cubic complexity. Function order of growth comes into play right here.
Let's look at what are possibilities for time complexity of an algorithm, you can see order of growth I mentioned above:
Constant time has an order of growth 1, for example: a = b + c.
Logarithmic time has an order of growth log N. It usually occurs when you're dividing something in half (binary search, trees, and even loops), or multiplying something in same way.
Linear. The order of growth is N, for example
int p = 0;
for (int i = 1; i < N; i++)
p = p + 2;
Linearithmic. The order of growth is n·log N. It usually occurs in divide-and-conquer algorithms.
Cubic. The order of growth is N3. A classic example is a triple loop where you check all triplets:
int x = 0;
for (int i = 0; i < N; i++)
for (int j = 0; j < N; j++)
for (int k = 0; k < N; k++)
x = x + 2
Exponential. The order of growth is 2N. It usually occurs when you do exhaustive search, for example, check subsets of some set.
Loosely speaking, time complexity is a way of summarising how the number of operations or run-time of an algorithm grows as the input size increases.
Like most things in life, a cocktail party can help us understand.
O(N)
When you arrive at the party, you have to shake everyone's hand (do an operation on every item). As the number of attendees N increases, the time/work it will take you to shake everyone's hand increases as O(N).
Why O(N) and not cN?
There's variation in the amount of time it takes to shake hands with people. You could average this out and capture it in a constant c. But the fundamental operation here --- shaking hands with everyone --- would always be proportional to O(N), no matter what c was. When debating whether we should go to a cocktail party, we're often more interested in the fact that we'll have to meet everyone than in the minute details of what those meetings look like.
O(N^2)
The host of the cocktail party wants you to play a silly game where everyone meets everyone else. Therefore, you must meet N-1 other people and, because the next person has already met you, they must meet N-2 people, and so on. The sum of this series is x^2/2+x/2. As the number of attendees grows, the x^2 term gets big fast, so we just drop everything else.
O(N^3)
You have to meet everyone else and, during each meeting, you must talk about everyone else in the room.
O(1)
The host wants to announce something. They ding a wineglass and speak loudly. Everyone hears them. It turns out it doesn't matter how many attendees there are, this operation always takes the same amount of time.
O(log N)
The host has laid everyone out at the table in alphabetical order. Where is Dan? You reason that he must be somewhere between Adam and Mandy (certainly not between Mandy and Zach!). Given that, is he between George and Mandy? No. He must be between Adam and Fred, and between Cindy and Fred. And so on... we can efficiently locate Dan by looking at half the set and then half of that set. Ultimately, we look at O(log_2 N) individuals.
O(N log N)
You could find where to sit down at the table using the algorithm above. If a large number of people came to the table, one at a time, and all did this, that would take O(N log N) time. This turns out to be how long it takes to sort any collection of items when they must be compared.
Best/Worst Case
You arrive at the party and need to find Inigo - how long will it take? It depends on when you arrive. If everyone is milling around you've hit the worst-case: it will take O(N) time. However, if everyone is sitting down at the table, it will take only O(log N) time. Or maybe you can leverage the host's wineglass-shouting power and it will take only O(1) time.
Assuming the host is unavailable, we can say that the Inigo-finding algorithm has a lower-bound of O(log N) and an upper-bound of O(N), depending on the state of the party when you arrive.
Space & Communication
The same ideas can be applied to understanding how algorithms use space or communication.
Knuth has written a nice paper about the former entitled "The Complexity of Songs".
Theorem 2: There exist arbitrarily long songs of complexity O(1).
PROOF: (due to Casey and the Sunshine Band). Consider the songs Sk defined by (15), but with
V_k = 'That's the way,' U 'I like it, ' U
U = 'uh huh,' 'uh huh'
for all k.
For the mathematically-minded people: The master theorem is another useful thing to know when studying complexity.
O(n) is big O notation used for writing time complexity of an algorithm. When you add up the number of executions in an algorithm, you'll get an expression in result like 2N+2. In this expression, N is the dominating term (the term having largest effect on expression if its value increases or decreases). Now O(N) is the time complexity while N is dominating term.
Example
For i = 1 to n;
j = 0;
while(j <= n);
j = j + 1;
Here the total number of executions for the inner loop are n+1 and the total number of executions for the outer loop are n(n+1)/2, so the total number of executions for the whole algorithm are n + 1 + n(n+1/2) = (n2 + 3n)/2.
Here n^2 is the dominating term so the time complexity for this algorithm is O(n2).
Other answers concentrate on the big-O-notation and practical examples. I want to answer the question by emphasizing the theoretical view. The explanation below is necessarily lacking in details; an excellent source to learn computational complexity theory is Introduction to the Theory of Computation by Michael Sipser.
Turing Machines
The most widespread model to investigate any question about computation is a Turing machine. A Turing machine has a one dimensional tape consisting of symbols which is used as a memory device. It has a tapehead which is used to write and read from the tape. It has a transition table determining the machine's behaviour, which is a fixed hardware component that is decided when the machine is created. A Turing machine works at discrete time steps doing the following:
It reads the symbol under the tapehead.
Depending on the symbol and its internal state, which can only take finitely many values, it reads three values s, σ, and X from its transition table, where s is an internal state, σ is a symbol, and X is either Right or Left.
It changes its internal state to s.
It changes the symbol it has read to σ.
It moves the tapehead one step according to the direction in X.
Turing machines are powerful models of computation. They can do everything that your digital computer can do. They were introduced before the advent of digital modern computers by the father of theoretical computer science and mathematician: Alan Turing.
Time Complexity
It is hard to define the time complexity of a single problem like "Does white have a winning strategy in chess?" because there is a machine which runs for a single step giving the correct answer: Either the machine which says directly 'No' or directly 'Yes'. To make it work we instead define the time complexity of a family of problems L each of which has a size, usually the length of the problem description. Then we take a Turing machine M which correctly solves every problem in that family. When M is given a problem of this family of size n, it solves it in finitely many steps. Let us call f(n) the longest possible time it takes M to solve problems of size n. Then we say that the time complexity of L is O(f(n)), which means that there is a Turing machine which will solve an instance of it of size n in at most C.f(n) time where C is a constant independent of n.
Isn't it dependent on the machines? Can digital computers do it faster?
Yes! Some problems can be solved faster by other models of computation, for example two tape Turing machines solve some problems faster than those with a single tape. This is why theoreticians prefer to use robust complexity classes such as NL, P, NP, PSPACE, EXPTIME, etc. For example, P is the class of decision problems whose time complexity is O(p(n)) where p is a polynomial. The class P do not change even if you add ten thousand tapes to your Turing machine, or use other types of theoretical models such as random access machines.
A Difference in Theory and Practice
It is usually assumed that the time complexity of integer addition is O(1). This assumption makes sense in practice because computers use a fixed number of bits to store numbers for many applications. There is no reason to assume such a thing in theory, so time complexity of addition is O(k) where k is the number of bits needed to express the integer.
Finding The Time Complexity of a Class of Problems
The straightforward way to show the time complexity of a problem is O(f(n)) is to construct a Turing machine which solves it in O(f(n)) time. Creating Turing machines for complex problems is not trivial; one needs some familiarity with them. A transition table for a Turing machine is rarely given, and it is described in high level. It becomes easier to see how long it will take a machine to halt as one gets themselves familiar with them.
Showing that a problem is not O(f(n)) time complexity is another story... Even though there are some results like the time hierarchy theorem, there are many open problems here. For example whether problems in NP are in P, i.e. solvable in polynomial time, is one of the seven millennium prize problems in mathematics, whose solver will be awarded 1 million dollars.
I need to calculate nCr(n,m) % k for large n (n <= 10^7) efficiently.
Here is my try:
int choose(int n, int m, int k) {
if (n==m || m==0)
return 1 % k;
return (choose(n-1, m-1, k) + choose(n-1, m , k)) % k;
}
It calculates some amount of combinations mod k: nCr(n,m) % k by exploiting pascals identity.
This is too inefficient for large n (try choose(100, 12, 223092870)), I'm not sure if this can be speeded up by memoization or if some totally different number theoretic approach is necessary.
I need this to be executed efficiently for large numbers instantly which is why I'm not sure if memoization is the solution.
Note: k doesn't have to be a prime!
Since nPr has an explicit formula nPr(n, m) = n!/((n-m)!) you should definitely try to use that instead. My tip would be:
Remember that n! = n*(n-1)*...*2*1
Notice that a while loop (yes, loop, not recursion ^^) could greatly optimize the calculation (the division cancels out lots of factors, leaving you with a multiplication nPr(n, m) = n*(n-1)*...*(n-m+2)*(n-m+1))
Finally, you should calculate the modulo after calculating nPr(n, m), to avoid redundant modulo operations.
If it helps, you could try formulating a loop invariant, which is pretty much a statement that should be true for all valid values of n and m.
Hope this helped :)
EDIT
I realized you said nCr after I wrote my answer. For nCr, you could add another while-loop after calculating nPr, that simply calculates m!, divide nPr by m!, and then modulo THAT answer instead. All in all, this would yield an O(n) algorithm, which is pretty scalable. It uses very little memory as well.
This comes up now and then in programming competitions, one common way of solving this is using Lucas' and the Chinese Remainder Theorem.
#DAle posted a useful resource with the details: http://fishi.devtail.io/weblog/2015/06/25/computing-large-binomial-coefficients-modulo-prime-non-prime/
Per mine understanding , To find the majority element Boyer–Moore majority vote algorithm is O(1) i.e. it constant and not proportional to size of input.
Then why thi wiki link mentions logarithmic space {\displaystyle O(\log n)} O(\log n)
Here is the program for reference
public class MajorityElement {
/* Function to print Majority Element */
void printMajority(int a[], int size) {
/* Find the candidate for Majority */
int cand = findCandidate(a, size);
/* Print the candidate if it is Majority */
if (isMajority(a, size, cand))
System.out.println(" " + cand + " ");
else
System.out.println("No Majority Element");
}
/* Function to find the candidate for Majority */
int findCandidate(int a[], int size) {
int maj_index = 0, count = 1;
int i;
for (i = 1; i < size; i++) {
if (a[maj_index] == a[i])
count++;
else
count--;
if (count == 0) {
maj_index = i;
count = 1;
}
}
return a[maj_index];
}
/*
* Function to check if the candidate occurs more than n/2 times
*/
boolean isMajority(int a[], int size, int cand) {
int i, count = 0;
for (i = 0; i < size; i++) {
if (a[i] == cand)
count++;
}
if (count > size / 2)
return true;
else
return false;
}
This is why Wikipedia cannot always be relied upon, at least not without some critical thinking on the part of the reader. (Which should not be considered a reason to not use Wikipedia; it's an immensely valuable resource thanks to a vast and committed team of volunteer contributors.)
There are two common models used to measure space and time complexity: the uniform cost model and the logarithmic cost model. The uniform cost model assumes that the storage cost of a single value is Θ(1) (regardless of the magnitude of that value) and that the time complexity of a single simple arithmetic computation is also Θ(1). If the values are very large, then these simplifications are not correct, so one might want to use the logarithmic model. In the logarithmic model, we measure the size of the problem not by the count of values, but rather by the total size in bits of the values. (A different Wikipedia article provides a discussion of these models. Also see the references.)
This has little impact on simple arithmetic. The cost of adding two N-bit numbers is Θ(N) and the cost of adding a vector of numbers whose total size is N bits is Θ(N), just as it would be with the simplifying assumption that the size of the problem is measured in values and the cost of adding two values is Θ(1). But if multiplication and division are involved, the complexity computations get much more complicated, and it is really not worth going down that road unless the numbers are really very very big, as in, for example, various encryption algorithms which include operations on values whose size is thousands of bits.
While there are algorithms which involve arithmetic on numbers sufficiently large that it needs to be taken into account for an accurate analysis, there really are no practical algorithms which involve so many inputs that the size of the address of a value (in the random access machine) needs to be taken into account. There are not 2256 subatomic particles in the entire universe, so it is completely reasonable to assume that a limited-bit-width register is sufficient for any addressing purpose, which includes counting the number of participating objects.
Consequently, categorizing an algorithm which needs to maintain a count of inputs as Θ(log N) (or O(log N)) just because the counter might have an arbitrary number of bits in some alternative universe is, at best, pedantry, and (in my opinion) contributes nothing to the understanding of the complexity of given algorithms.
Nonetheless, pedants have as much right as anyone to contribute to Wikipedia; indeed, it might be theorized that the Wikipedia culture invites pedantry. That still needs to be balanced against the Wikipedia insistence that authors not include "original research", which would include (again, in my opinion) reinterpreting the storage complexity of an algorithm in a way which contradicts commonly published results. (And that might explain the "citation-needed" marker in the Wikipedia article in question.)
This is because the variable count needs O(log(n)) bits to store the number of occurences of the candidate. Of course in your daily tests it is unlikely that you will try with arrays of more than 2^32 (or something similar) cells.
I want to efficiently calculate ((X+Y)!/(X!Y!))% P (P is like 10^9+7)
This discussion gives some insights on distributing modulo over division.
My concern is it's not necessary that a modular inverse always exists for a number.
Basically, I am looking for a code implementation of solving the problem.
For multiplication it is very straightforward:
public static int mod_mul(int Z,int X,int Y,int P)
{
// Z=(X+Y) the factorial we need to calculate, P is the prime
long result = 1;
while(Z>1)
{
result = (result*Z)%P
Z--;
}
return result;
}
I also realize that many factors can get cancelled in the division (before taking modulus), but if the number of divisors increase, then I'm finding it difficult to efficiently come up with an algorithm to divide. ( Looping over List(factors(X)+factors(Y)...) to see which divides current multiplying factor of numerator).
Edit: I don't want to use BigInt solutions.
Is there any java/python based solution or any standard algorithm/library for cancellation of factors( if inverse option is not full-proof) or approaching this type of problem.
((X+Y)!/(X!Y!)) is a low-level way of spelling a binomial coefficient ((X+Y)-choose-X). And while you didn't say so in your question, a comment in your code implies that P is prime. Put those two together, and Lucas's theorem applies directly: http://en.wikipedia.org/wiki/Lucas%27_theorem.
That gives a very simple algorithm based on the base-P representations of X+Y and X. Whether BigInts are required is impossible to guess because you didn't give any bounds on your arguments, beyond that they're ints. Note that your sample mod_mul code may not work at all if, e.g., P is greater than the square root of the maximum int (because result * Z may overflow then).
It's binomial coefficients - C(x+y,x).
You can calculate it differently C(n,m)=C(n-1,m)+C(n-1,m-1).
If you are OK with time complexity O(x*y), the code will be much simpler.
http://en.wikipedia.org/wiki/Combination
for what you need here is a way to do it efficiently : -
C(n,k) = C(n-1,k) + C(n-1,k-1)
Use dynamic programming to calculate efficient in bottom up approach
C(n,k)%P = ((C(n-1,k))%P + (C(n-1,k-1))%P)%P
Therefore F(n,k) = (F(n-1,k)+F(n-1,k-1))%P
Another faster approach : -
C(n,k) = C(n-1,k-1)*n/k
F(n,k) = ((F(n-1,k-1)*n)%P*inv(k)%P)%P
inv(k)%P means modular inverse of k.
Note:- Try to evaluate C(n,n-k) if (n-k<k) because nC(n-k) = nCk
I am trying to write a program to find the largest prime factor of a very large number, and have tried several methods with varying success. All of the ones I have found so far have been unbelievably slow. I had a thought, and am wondering if this is a valid approach:
long number = input;
while(notPrime(number))
{
number = number / getLowestDivisiblePrimeNumber();
}
return number;
This approach would take an input, and would do the following:
200 -> 100 -> 50 -> 25 -> 5 (return)
90 -> 45 -> 15 -> 5 (return)
It divides currentNum repeatedly by the smallest divisible number (most often 2, or 3) until currentNum itself is prime (there is no divisible prime number less than the squareroot of currentNum), and assumes this is the largest prime factor of the original input.
Will this always work? If not, can someone give me a counterexample?
-
EDIT: By very large, I mean about 2^40, or 10^11.
The method will work, but will be slow. "How big are your numbers?" determines the method to use:
Less than 2^16 or so: Lookup table.
Less than 2^70 or so: Sieve of Atkin. This is an optimized version of the more well known Sieve of Eratosthenes. Edit: Richard Brent's modification of Pollard's rho algorithm may be better in this case.
Less than 10^50: Lenstra elliptic curve factorization
Less than 10^100: Quadratic Sieve
More than 10^100: General Number Field Sieve
This will always work because of the Unique Prime Factorization Theorem.
Certainly it will work (see Mark Byers' answer), but for "very large" inputs it may take far too long. You should note that your call to getLowestDivisiblePrimeNumber() conceals another loop, so this runs at O(N^2), and that depending on what you mean by "very large" it may have to work on BigNums which will be slow.
You could speed it up a little, by noting that your algorithm need never check factors smaller than the last one found.
You are trying to find the prime factors of a number. What you are proposing will work, but will still be slow for large numbers.... you should be thankful for this, since most modern security is predicated on this being a difficult problem.
From a quick search I just did, the fastest known way to factor a number is by using the Elliptic Curve Method.
You could try throwing your number at this demo: http://www.alpertron.com.ar/ECM.HTM .
If that convinces you, you could try either stealing the code (that's no fun, they provide a link to it!) or reading up on the theory of it elsewhere. There's a Wikipedia article about it here: http://en.wikipedia.org/wiki/Lenstra_elliptic_curve_factorization but I'm too stupid to understand it. Thankfully, it's your problem, not mine! :)
The thing with Project Euler is that there is usually an obvious brute-force method to do the problem, which will take just about forever. As the questions become more difficult, you will need to implement clever solutions.
One way you can solve this problem is to use a loop that always finds the smallest (positive integer) factor of a number. When the smallest factor of a number is that number, then you've found the greatest prime factor!
Detailed Algorithm description:
You can do this by keeping three variables:
The number you are trying to factor (A)
A current divisor store (B)
A largest divisor store (C)
Initially, let (A) be the number you are interested in - in this case, it is 600851475143. Then let (B) be 2. Have a conditional that checks if (A) is divisible by (B). If it is divisible, divide (A) by (B), reset (B) to 2, and go back to checking if (A) is divisible by (B). Else, if (A) is not divisible by (B), increment (B) by +1 and then check if (A) is divisible by (B). Run the loop until (A) is 1. The (3) you return will be the largest prime divisor of 600851475143.
There are numerous ways you could make this more effective - instead of incrementing to the next integer, you could increment to the next necessarily prime integer, and instead of keeping a largest divisor store, you could just return the current number when its only divisor is itself. However, the algorithm I described above will run in seconds regardless.
The implementation in python is as follows:-
def lpf(x):
lpf = 2;
while (x > lpf):
if (x%lpf==0):
x = x/lpf
lpf = 2
else:
lpf+=1;
print("Largest Prime Factor: %d" % (lpf));
def main():
x = long(raw_input("Input long int:"))
lpf(x);
return 0;
if __name__ == '__main__':
main()
Example: Let's find the largest prime factor of 105 using the method described above.
Let (A) = 105. (B) = 2 (we always start with 2), and we don't have a value for (C) yet.
Is (A) divisible by (B)? No. Increment (B) by +1: (B) = 3. Is Is (A) divisible by (B)? Yes. (105/3 = 35). The largest divisor found so far is 3. Let (C) = 3. Update (A) = 35. Reset (B) = 2.
Now, is (A) divisible by (B)? No. Increment (B) by +1: (B) = 3. Is (A) divisible by (B)? No. Increment (B) by +1: (B) = 4. Is (A) divisible by (B)? No. Increment (B) by +1: (B) = 5. Is (A) divisible by (B)? Yes. (35/5 = 7). The largest divisor we found previously is stored in (C). (C) is currently 3. 5 is larger than 3, so we update (C) = 5. We update (A)=7. We reset (B)=2.
Then we repeat the process for (A), but we will just keep incrementing (B) until (B)=(A), because 7 is prime and has no divisors other than itself and 1. (We could already stop when (B)>((A)/2), as you cannot have integer divisors greater than half of a number - the smallest possible divisor (other than 1) of any number is 2!)
So at that point we return (A) = 7.
Try doing a few of these by hand, and you'll get the hang of the idea