I have an integer, N.
I denote f[i] = number of appearances of the digit i in N.
Now, I have the following algorithm.
FOR i = 0 TO 9
FOR j = 1 TO f[i]
k = k*10 + i;
My teacher said this is O(N). It seems to me more like a O(logN) algorithm.
Am I missing something?
I think that you and your teacher are saying the same thing but it gets confused because the integer you are using is named N but it is also common to refer to an algorithm that is linear in the size of its input as O(N). N is getting overloaded as the specific name and the generic figure of speech.
Suppose we say instead that your number is Z and its digits are counted in the array d and then their frequencies are in f. For example, we could have:
Z = 12321
d = [1,2,3,2,1]
f = [0,2,2,1,0,0,0,0,0,0]
Then the cost of going through all the digits in d and computing the count for each will be O( size(d) ) = O( log (Z) ). This is basically what your second loop is doing in reverse, it's executing one time for each occurence of each digits. So you are right that there is something logarithmic going on here -- the number of digits of Z is logarithmic in the size of Z. But your teacher is also right that there is something linear going on here -- counting those digits is linear in the number of digits.
The time complexity of an algorithm is generally measured as a function of the input size. Your algorithm doesn't take N as an input; the input seems to be the array f. There is another variable named k which your code doesn't declare, but I assume that's an oversight and you meant to initialise e.g. k = 0 before the first loop, so that k is not an input to the algorithm.
The outer loop runs 10 times, and the inner loop runs f[i] times for each i. Therefore the total number of iterations of the inner loop equals the sum of the numbers in the array f. So the complexity could be written as O(sum(f)) or O(Σf) where Σ is the mathematical symbol for summation.
Since you defined that N is an integer which f counts the digits of, it is in fact possible to prove that O(Σf) is the same thing as O(log N), so long as N must be a positive integer. This is because Σf equals how many digits the number N has, which is approximately (log N) / (log 10). So by your definition of N, you are correct.
My guess is that your teacher disagrees with you because they think N means something else. If your teacher defines N = Σf then the complexity would be O(N). Or perhaps your teacher made a genuine mistake; that is not impossible. But the first thing to do is make sure you agree on the meaning of N.
I find your explanation a bit confusing, but lets assume N = 9075936782959 is an integer. Then O(N) doesn't really make sense. O(length of N) makes more sense. I'll use n for the length of N.
Then f(i) = iterate over each number in N and sum to find how many times i is in N, that makes O(f(i)) = n (it's linear). I'm assuming f(i) is a function, not an array.
Your algorithm loops at most:
10 times (first loop)
0 to n times, but the total is n (the sum of f(i) for all digits must be n)
It's tempting to say that algorithm is then O(algo) = 10 + n*f(i) = n^2 (removing the constant), but f(i) is only calculated 10 times, each time the second loops is entered, so O(algo) = 10 + n + 10*f(i) = 10 + 11n = n. If f(i) is an array, it's constant time.
I'm sure I didn't see the problem the same way as you. I'm still a little confused about the definition in your question. How did you come up with log(n)?
I have a list of elements to be searched in a dataset of variable lengths. I have tried binary search and I found it is not always efficient when the objective is to search a list of elements.
I did the following study and conclude that if the number of elements to be searched is less than 5% of the data, binary search is efficient, other wise the Linear search is better.
Below are the details
Number of elements : 100000
Number of elements to be searched: 5000
Number of Iterations (Binary Search) =
log2 (N) x SearchCount=log2 (100000) x 5000=83048
Further increase in the number of search elements lead to more iterations than the linear search.
Any thoughts on this?
I am calling the below function only if the number elements to be searched is less than 5%.
private int SearchIndex(ref List<long> entitylist, ref long[] DataList, int i, int len, ref int listcount)
{
int Start = i;
int End = len-1;
int mid;
while (Start <= End)
{
mid = (Start + End) / 2;
long target = DataList[mid];
if (target == entitylist[listcount])
{
i = mid;
listcount++;
return i;
}
else
{
if (target < entitylist[listcount])
{
Start = mid + 1;
}
if (target > entitylist[listcount])
{
End = mid - 1;
}
}
}
listcount++;
return -1; //if the element in the list is not in the dataset
}
In the code I retun the index rather than the value because, I need to work with Index in the calling function. If i=-1, the calling function resets the value to the previous i and calls the function again with a new element to search.
In your problem you are looking for M values in an N long array, N > M, but M can be quite large.
Usually this can be approached as M independent binary searches (or even with the slight optimization of using the previous result as a starting point): you are going to O(M*log(N)).
However, using the fact that also the M values are sorted, you can find all of them in one pass, with linear search. In this case you are going to have your problem O(N). In fact this is better than O(M*log(N)) for M large.
But you have a third option: since M values are sorted, binary split M too, and every time you find it, you can limit the subsequent searches in the ranges on the left and on the right of the found index.
The first look-up is on all the N values, the second two on (average) N/2, than 4 on N/4 data,.... I think that this scale as O(log(M)*log(N)). Not sure of it, comments welcome!
However here is a test code - I have slightly modified your code, but without altering its functionality.
In case you have M=100000 and N=1000000, the "M binary search approach" takes about 1.8M iterations, that's more that the 1M needed to scan linearly the N values. But with what I suggest it takes just 272K iterations.
Even in case the M values are very "collapsed" (eg, they are consecutive), and the linear search is in the best condition (100K iterations would be enough to get all of them, see the comments in the code), the algorithm performs very well.
Let's say we have a set : {1, 2, ..., n}.
How many subsets of order R exist S = {a_i1, a_i2, ...a_iR} that sum up to a certain number S?. What is the recursion for this problem?
Just define method to solve original problem. Parameters it receives are:
max number to use (n),
subset size (R),
subset sum (S),
and returns number of combinations.
To implement this method, first we have to check is it possible to make this request. It is not possible to fulfill task if:
subset size is larger than number of possible elements (R > n)
maximal possible sum is smaller than S. n + (n-1) + ... + (n-R+1) < S => R*((n-R) + (R+1)/2) < S
After that it is enough to try all possibilities for larger element that will go in subset. In python style it should be implemented like:
def combinations(n, R, S):
if R > n or R*((n-R) + (R+1)/2) < S:
return 0
c = 0
for i in xrange(R, n+1): # try i as maximal element in subset. It can go from R to n
# recursion n is i-1, since i is already used
# recursion R is R-1, since we put i in a set
# recursion S is S-i, since i is added to a set and we are looking for sum without it
c += combinations(i-1, R-1, S-i)
return c
I am trying to find the loop invariant in the following code:
Find Closest Pair Iter(A) :
# Precondition: A is a non-empty list of 2D points and len(A) > 1.
# Postcondition: Returns a pair of points which are the two closest points in A.
min = infinity
p = -1
q = -1
for i = 0,...,len(A) - 1:`=
for j = i + 1,...,len(A) - 1:
if Distance(A[i],A[j]) < min:
min = Distance(A[i],A[j])
p = i
q = j
return (A[p],A[q])
I think the loop invariant is min = Distance(A[i],A[j]) so closest point in A is A[p] and a[q] .
I'm trying to show program correctness. Here I want to prove the inner loop by letting i be some constant, then once I've proven the inner loop, replace it by it's loop invariant and prove the outer loop. By the way this is homework. Any help will be much appreciated.
I'm not sure I fully understand what you mean by replacing the inner loop by its loop invariant. A loop invariant is a condition that holds before the loop and after every iteration of the loop (including the last one).
That being said, I wouldn't like to spoil your homework, so I'll try my best to help you without giving too much of the answer away. Let me try:
There are three variables in your algorithm that hold very important values (min, p and q). You should ask yourself what is true about these values as the algorithm goes through each pair of points (A[i], A[j])?
In a simpler example: if you were designing an algorithm to sum values in a list, you would create a variable called sum before the loop and assign 0 to it. You would then sum the elements one by one through a loop, and then return the variable sum.
Since it is true that this variable holds the sum of every single element "seen" in the loop, and since after the main loop the algorithm will have "seen" every element in the list, the sum variable necessarily holds the sum of all values in the list. In this case the loop invariant would be: The sum variable holds the sum of every element "seen" so far.
Good luck with your homework!
I know the modulus (%) operator calculates the remainder of a division. How can I identify a situation where I would need to use the modulus operator?
I know I can use the modulus operator to see whether a number is even or odd and prime or composite, but that's about it. I don't often think in terms of remainders. I'm sure the modulus operator is useful, and I would like to learn to take advantage of it.
I just have problems identifying where the modulus operator is applicable. In various programming situations, it is difficult for me to see a problem and realize "Hey! The remainder of division would work here!".
Imagine that you have an elapsed time in seconds and you want to convert this to hours, minutes, and seconds:
h = s / 3600;
m = (s / 60) % 60;
s = s % 60;
0 % 3 = 0;
1 % 3 = 1;
2 % 3 = 2;
3 % 3 = 0;
Did you see what it did? At the last step it went back to zero. This could be used in situations like:
To check if N is divisible by M (for example, odd or even)
or
N is a multiple of M.
To put a cap of a particular value. In this case 3.
To get the last M digits of a number -> N % (10^M).
I use it for progress bars and the like that mark progress through a big loop. The progress is only reported every nth time through the loop, or when count%n == 0.
I've used it when restricting a number to a certain multiple:
temp = x - (x % 10); //Restrict x to being a multiple of 10
Wrapping values (like a clock).
Provide finite fields to symmetric key algorithms.
Bitwise operations.
And so on.
One use case I saw recently was when you need to reverse a number. So that 123456 becomes 654321 for example.
int number = 123456;
int reversed = 0;
while ( number > 0 ) {
# The modulus here retrieves the last digit in the specified number
# In the first iteration of this loop it's going to be 6, then 5, ...
# We are multiplying reversed by 10 first, to move the number one decimal place to the left.
# For example, if we are at the second iteration of this loop,
# reversed gonna be 6, so 6 * 10 + 12345 % 10 => 60 + 5
reversed = reversed * 10 + number % 10;
number = number / 10;
}
Example. You have message of X bytes, but in your protocol maximum size is Y and Y < X. Try to write small app that splits message into packets and you will run into mod :)
There are many instances where it is useful.
If you need to restrict a number to be within a certain range you can use mod. For example, to generate a random number between 0 and 99 you might say:
num = MyRandFunction() % 100;
Any time you have division and want to express the remainder other than in decimal, the mod operator is appropriate. Things that come to mind are generally when you want to do something human-readable with the remainder. Listing how many items you could put into buckets and saying "5 left over" is good.
Also, if you're ever in a situation where you may be accruing rounding errors, modulo division is good. If you're dividing by 3 quite often, for example, you don't want to be passing .33333 around as the remainder. Passing the remainder and divisor (i.e. the fraction) is appropriate.
As #jweyrich says, wrapping values. I've found mod very handy when I have a finite list and I want to iterate over it in a loop - like a fixed list of colors for some UI elements, like chart series, where I want all the series to be different, to the extent possible, but when I've run out of colors, just to start over at the beginning. This can also be used with, say, patterns, so that the second time red comes around, it's dashed; the third time, dotted, etc. - but mod is just used to get red, green, blue, red, green, blue, forever.
Calculation of prime numbers
The modulo can be useful to convert and split total minutes to "hours and minutes":
hours = minutes / 60
minutes_left = minutes % 60
In the hours bit we need to strip the decimal portion and that will depend on the language you are using.
We can then rearrange the output accordingly.
Converting linear data structure to matrix structure:
where a is index of linear data, and b is number of items per row:
row = a/b
column = a mod b
Note above is simplified logic: a must be offset -1 before dividing & the result must be normalized +1.
Example: (3 rows of 4)
1 2 3 4
5 6 7 8
9 10 11 12
(7 - 1)/4 + 1 = 2
7 is in row 2
(7 - 1) mod 4 + 1 = 3
7 is in column 3
Another common use of modulus: hashing a number by place. Suppose you wanted to store year & month in a six digit number 195810. month = 195810 mod 100 all digits 3rd from right are divisible by 100 so the remainder is the 2 rightmost digits in this case the month is 10. To extract the year 195810 / 100 yields 1958.
Modulus is also very useful if for some crazy reason you need to do integer division and get a decimal out, and you can't convert the integer into a number that supports decimal division, or if you need to return a fraction instead of a decimal.
I'll be using % as the modulus operator
For example
2/4 = 0
where doing this
2/4 = 0 and 2 % 4 = 2
So you can be really crazy and let's say that you want to allow the user to input a numerator and a divisor, and then show them the result as a whole number, and then a fractional number.
whole Number = numerator/divisor
fractionNumerator = numerator % divisor
fractionDenominator = divisor
Another case where modulus division is useful is if you are increasing or decreasing a number and you want to contain the number to a certain range of number, but when you get to the top or bottom you don't want to just stop. You want to loop up to the bottom or top of the list respectively.
Imagine a function where you are looping through an array.
Function increase Or Decrease(variable As Integer) As Void
n = (n + variable) % (listString.maxIndex + 1)
Print listString[n]
End Function
The reason that it is n = (n + variable) % (listString.maxIndex + 1) is to allow for the max index to be accounted.
Those are just a few of the things that I have had to use modulus for in my programming of not just desktop applications, but in robotics and simulation environments.
Computing the greatest common divisor
Determining if a number is a palindrome
Determining if a number consists of only ...
Determining how many ... a number consists of...
My favorite use is for iteration.
Say you have a counter you are incrementing and want to then grab from a known list a corresponding items, but you only have n items to choose from and you want to repeat a cycle.
var indexFromB = (counter-1)%n+1;
Results (counter=indexFromB) given n=3:
`1=1`
`2=2`
`3=3`
`4=1`
`5=2`
`6=3`
...
Best use of modulus operator I have seen so for is to check if the Array we have is a rotated version of original array.
A = [1,2,3,4,5,6]
B = [5,6,1,2,3,4]
Now how to check if B is rotated version of A ?
Step 1: If A's length is not same as B's length then for sure its not a rotated version.
Step 2: Check the index of first element of A in B. Here first element of A is 1. And its index in B is 2(assuming your programming language has zero based index).
lets store that index in variable "Key"
Step 3: Now how to check that if B is rotated version of A how ??
This is where modulus function rocks :
for (int i = 0; i< A.length; i++)
{
// here modulus function would check the proper order. Key here is 2 which we recieved from Step 2
int j = [Key+i]%A.length;
if (A[i] != B[j])
{
return false;
}
}
return true;
It's an easy way to tell if a number is even or odd. Just do # mod 2, if it is 0 it is even, 1 it is odd.
Often, in a loop, you want to do something every k'th iteration, where k is 0 < k < n, assuming 0 is the start index and n is the length of the loop.
So, you'd do something like:
int k = 5;
int n = 50;
for(int i = 0;i < n;++i)
{
if(i % k == 0) // true at 0, 5, 10, 15..
{
// do something
}
}
Or, you want to keep something whitin a certain bound. Remember, when you take an arbitrary number mod something, it must produce a value between 0 and that number - 1.