I am trying to think of an algorithm to implement this for a given n bit binary number. I tried out many examples, but am unable to find out any pattern. So how shall I proceed?
How about this:
Convert the number to base 4 (this is trivial by simply combining pairs of bits). 5 in base 4 is 11. The values base 4 that are divisible by 11 are somewhat familiar: 11, 22, 33, 110, 121, 132, 203, ...
The rule for divisibility by 11 is that you add all the odd digits and all the even digits and subtract one from the other. If the result is divisible by 11 (which remember is 5), then it's divisible by 11 (which remember is 5).
For example:
123456d = 1 1110 0010 0100 0000b = 132021000_4
The even digits are 1 2 2 0 0 : sum = 5d
The odd digits are 3 0 1 0 : sum = 4d
Difference is 1, which is not divisble by 5
Or another one:
123455d = 1 1110 0010 0011 1111b = 132020333_4
The even digits are 1 2 2 3 3 : sum = 11d
The odd digits are 3 0 0 3 : sum = 6d
Difference is 5, which is a 5 or a 0
This should have a fairly efficient HW implementation because it's mostly bit-slicing, followed by N/2 adders, where N is the number of bits in the number you're interested in.
Note that after adding the digits and subtracting, the maximum value is 3/4 * N, so if you have 16-bit numbers max, you can get at most 12 as a result, so you only need to check for 0, ±5 and ±10 explicitly. If you're using 32-bit numbers then you can get at most 24 as a result, so you need to also check if the result is ±15 or ±20.
Make a Deterministic Finite Automaton (DFA) to implement the divisibility check and implement the DFA in hardware.
Creating a DFA for divisibility by 5 is easy. You just need to notice the remainders and check what 2r (mod 5) and 2r + 1(mod 5) map to. There are many websites that discuss this. For example this one.
There are well-known examples to convert DFA to a hardware representation as well.
Well , I just figured out ...
number mod 5 = a0 * 2^0 mod 5 + a1 * 2^1 mod 5 +a2* 2^2 mod 5 + a3 * 2^3 mod 5 + a4 * 2^4 mod 5 + ....
= a0 (1) + a1(2) +a2 (-1) +a3 (-2) +a4 (1) repeats ...
Hence difference of odd digits + 2 times difference of even digits = divisible by 5
for example ... consider 110010
odd digits differnce = 0-0+1 = 1 or 01
even digits difference = 1-0+1 = 2 or 10
difference of odd digits + 2 times difference of even digits = 01 + 2*(10)=01 + 100 = 101 is divisible by 5 .
The contribution of each bit toward being divisible by five is a four bit pattern 3421.
You could shift through any binary number 4 bits at a time adding the corresponding value for positive bits.
Example:
100011
take 0011
apply the pattern 0021
sum 3
next four bits 0010
apply the pattern 0020
sum = 5
We can design a Deterministic Finite Automaton (DFA) for the same. The DFA, then can be implemented in Hardware. This is similar to this answer.
We will simulate a Deterministic Finite Automaton (DFA) that accepts Binary Representation of Integers which are divisible by 5
Now, by accept, we mean that when we are done with scanning string, we should be in one of the multiple possible Final States.
Approach to Design DFA : Essentially, we need to divide the Binary Representation of Integer by 5, and track the remainder. If after consuming/scanning [From Left to Right] the entire string, remainder is Zero, then we should end up in Final State, and if remainder isn't zero we should be in Non-Final States.
Now, DFA is defined by Quintuple/5-Tuple (Q,q₀,F,Σ,δ). We will obtain these five components step-by-step.
Q : Finite Set of States
We need to track remainder. On dividing any integer by 5, we can get remainder as 0,1, 2, 3 or 4. Hence, we will have Five States Z, O, T, Th and F for each possible remainder.
Q={Z, O, T, Th, F}
If after scanning certain part of Binary String, we are in state Z, this means that integer defined from Left to this part will give remainder Zero when divided by 5. Similarly, O for remainder One, and so on.
Now, we can write these three states by Euclidean Division Algorithm as
Z : 5m
O : 5m+1
T : 5m+2
Th : 5m+3
F : 5m+4
where m is Integer.
q₀ : an initial/start state from set Q
Now, start state can be thought in terms of empty string (ɛ). An ɛ directly gets into q₀.
What remainder does ɛ gives when divided by 5?
We can append as many 0s in left hand side of a Binary Number. In the similar fashion, we can append ɛ in left hand side of a Binary String. Thus, ɛ in left can be thought of as 0. And 0 when divided by 5 gives remainder 0. Hence, ɛ should end in State Z. But ɛ ends up in q₀.
Thus, q₀=Z
F : a set of accept states
Now we want all strings which are divisible by 5, or which gives remainder 0 when divided by 5, or which after complete scanning should end up in state Z, and gets accepted.
Hence,
F={Z}
Σ : Alphabet (a finite set of input symbols)
Since we are scanning/reading a Binary String. Hence,
Σ={0,1}
δ : Transition Function (δ : Q × Σ → Q)
Now this δ tells us that if we are in state x (in Q) and next input to be scanned is y (in Σ), then at which state z (in Q) should we go.
If the string upto this point gives remainder 3/Th when divided by 5, and if we append 1 to string, then what remainder will resultant string give.
Now, this can be analyzed by observing how magnitude of a binary string changes on appending 0 and 1.
a.
In Decimal (Base-10), if we add/append 0, then magnitude gets multiplied by 10 . 53, on appending 0 it becomes 530
Also, if we append 8 to decimal, then Magnitude gets multiplied by 10, and then we add 8 to multiplied magnitude.
b.
In Binary (Base-2), if we add/append 0, then magnitude gets multiplied by 2 (The Positional Weight of each Bit get multiplied by 2)
Example : (1010)2 [which is (10)10], on appending 0 it becomes (10100)2 [which is (20)10]
Similarly, In Binary, if we append 1, then Magnitude gets multiplied by 2, and then we add 1.
Example : (10)2 [which is (2)10], on appending 1 it becomes (101)2 [which is (5)10]
Thus, we can say that for Binary String x,
x0=2|x|
x1=2|x|+1
We will use these relation to analyze Five States
Any string in Z can be written as 5m
- On 0, it becomes 2(5m), which is 5(2m), nothing but state Z.
- On 1, it becomes 2(5m)+1, which is 5(2m)+1, that is O. [This can be read as if a Binary String is presently divisible by 5, and we append 1, then resultant string will give remainder as 1]
Any string in O can be written as 5m+1
- On 0, it becomes 2(5m+1) = 10m+2, which is 5(2m)+2, state T.
- On 1, it becomes 2(5m+1)+1 = 10m+3, which is 5(2m)+3, that is state Th.
Any string in T can be written as 5m+2
- On 0, it becomes 2(5m+2) = 10m+4, which is 5(2m)+4, state F.
- On 1, it becomes 2(5m+2)+1 = 10m+5, which is 5(2m+1), state Z. [If m is integer, so is (2m+1)]
Any string in Th can be written as 5m+3
- On 0, it becomes 2(5m+3) = 10m+6, which is 5(2m+1)+1, state V.
- On 1, it becomes 2(5m+3)+1 = 10m+7, which is 5(2m+1)+2, that is state T.
Any string in F can be written as 5m+4
- On 0, it becomes 2(5m+4) = 10m+8, which is 5(2m+1)+3, state Th.
- On 1, it becomes 2(5m+4)+1 = 10m+9, which is 5(2m+1)+4, that is state F.
Hence, the final DFA combining Everything (creating using Tool)
We can even write code [in High Level Language] for the same. But it would go beyond main aim of this question. If readers wish to see the same, they can check here.
As any assignment this would have been an answer for is bound to be way overdue a year later:
in the binary representation of a natural divisible by five the parities of bits 4n and 4n+2 equal, as well as those for bits 4n+1 and 4n+3.
(This is entirely equivalent to the answers of JoshG79, notsogeek, or james: 4≡-1(mod 5), 3≡-2(mod 5) (with reduced hand-waving about recursion in argumentation, and no dispensable handling of carries in circuitry))
Related
I'm currently studying for an advanced algorithms and datastructures exam, and I simply can't seem to solve one of the practice-problems which is the following:
1.14) "Nice Triangle"
A "nice" triangle is defined in the following way:
There are three different numbers which the triangle consists of, namely the first three prime numbers (2, 3 and 5).
Every number depends on the two numbers below it in the following way.
Numbers are the same, resulting number is also the same. (2, 2 => 2)
Numbers are different, resulting number is the remaining number. (2, 3 => 5)
Given an integer N with length L, corresponding to the base of the triangle, determine the last element at the top
For example:
Given N = 25555 (and thus L = 5), the triangle looks like this:
2
3 5
2 5 5
3 5 5 5
2 5 5 5 5
=> 2 is the result of this example
What does the fact that every number is prime have to do with the problem?
By using a naive approach (simply calculating every single row), one obtains a time-complexity of O(L^2).
However, the professor said, it's possible with O(L), but I simply can't find any pattern!!!
I'm not sure why this problem would be used in an advanced algorithms course, but yes, you can do this in O(l) = O(log n) time.
There are a couple ways you can do it, but they both rely on recognizing that:
For the problem statement, it doesn't matter what digits you use. Lets use 0, 1, and 2 instead of 2, 3, and 5. Then
If a and b are the input numbers and c is the output, then c = -(a+b) mod 3
You can build the whole triangle using c = a+b mod 3 instead, and then just negate every second row.
Now the two ways you can do this in O(log n) time are:
For each digit d in the input, calculate the number of times (call it k) that it gets added into the final sum, add up all the kd mod 3, and then negate the result if you started with an even number of digits. That takes constant time per digit. Alternatively:
recognize that you can do arithmetic on n-sized values in constant time. Make a value that is a bit mask of all the digits in n. That takes 2 bits each. Then by using bitwise operations you can calculate each row from the previous one in constant time, for O(log n) time altogether.
Here's an implementation of the 2nd way in python:
def niceTriangle(n):
# a vector of 3-bit integers mod 3
rowvec = 0
# a vector of 1 for each number in the row
onevec = 0
# number of rows remaining
rows = 0
# mapping for digits 0-9
digitmap = [0, 0, 0, 1, 1, 2, 2, 2, 2, 2]
# first convert n into the first row
while n > 0:
digit = digitmap[n % 10]
n = n//10
rows += 1
onevec = (onevec << 3) + 1
rowvec = (rowvec << 3) + digit
if rows%2 == 0:
# we have an even number of rows -- negate everything
rowvec = ((rowvec&onevec)<<1) | ((rowvec>>1)&onevec)
while rows > 1:
# add each number to its neighbor
rowvec += (rowvec >> 3)
# isolate the entries >= 3, by adding 1 to each number and
# getting the 2^2 bit
gt3 = ((rowvec + onevec) >> 2) & onevec
# subtract 3 from all the greater entries
rowvec -= gt3*3
rows -= 1
return [2,3,5][rowvec%4]
This the GFG Link
In this link, I am not able to get anything intuition that how we are calculating the number of 2 as a digit in,
My doubt is if we are counting the 6000 digits in the range as explained in the below description then why we are simply dividing the number by 10 and returning it, If anyone can help me, please do post your answer with examples
Case digits < 2
Consider the value x = 61523 and digit at index d = 3 (here indexes are considered from right and rightmost index is 0). We observe that x[d] = 1. There are 2s at the 3rd digit in the ranges 2000 – 2999, 12000 – 12999, 22000 – 22999, 32000 32999, 42000 – 42999, and 52000 – 52999. So there are 6000 2’s total in the 3rd digit. This is the same amount as if we were just counting all the 2s in the 3rd digit between 1 and 60000.
In other words, we can round down to the nearest 10d+1, and then divide by 10, to compute the number of 2s in the d-th digit.
if x[d) < 2: count2sinRangeAtDigit(x, d) =
Compute y = round down to nearest 10d+1
return y/10
Case digit > 2
Now, let’s look at the case where d-th digit (from right) of x is greater than 2 (x[d] > 2). We can apply almost the exact same logic to see that there are the same number of 2s in the 3rd digit in the range 0 – 63525 as there as in the range 0 – 70000. So, rather than rounding down, we round up.
if x[d) > 2: count2sinRangeAtDigit(x, d) =
Compute y = round down to nearest 10d+1
return y / 10
Case digit = 2
The final case may be the trickiest, but it follows from the earlier logic. Consider x = 62523 and d = 3. We know that there are the same ranges of 2s from before (that is, the ranges 2000 – 2999, 12000 – 12999, … , 52000 – 52999). How many appear in the 3rd digit in the final, partial range from 62000 – 62523? Well, that should be pretty easy. It’s just 524 (62000, 62001, … , 62523).
if x[d] = 2: count2sinRangeAtDigit(x, d) =
Compute y = round down to nearest 10d+1
Compute z = right side of x (i.e., x% 10d)
return y/10 + z + 1**// here why we are doing it ,what is the logic behind this approach**
There is not complete clarity in the explantion given above that's why I am asking here Thank you
For me that explanation is strange too. Also note that true complexity is O(log(n)) because it depends on nummber length (digit count).
Consider the next example: we have number 6125.
At the first round we need to calculate how many 2's are met as the rightmost digit in all numbers from 0 to 6125. We round number down to 6120 and up to 6130. Last digit is 5>2, so we have 613 intervals, every interval contains one digit 2 as the last digit - here we count last 2's in numbers like 2,12,22,..1352,..,6122.
At the second round we need to calculate how many 2's are met as the second (from right) digit in all numbers from 0 to 6125. We round number down to 6100 and up to 6200. Also we have right=5. Digit is 2, so we have 61 intervals, every interval contains ten digits 2 at the second place (20..29, 120..129... 6020..6029). We add 61*10. Also we have to add 5+1 2's for values 6120..6125
At the third round we need to calculate how many 2's are met as the third (from right) digit in all numbers from 0 to 6125. We round number down to 6000 and up to 7000. Digit is 1, so we have 6 intervals, every interval contains one hundred of digit 2 at the third place (200.299.. 5200..5299). So add 6*100.
I think it is clear now that we add 1 interval with thousand of 2's (2000.2999) as the leftmost digit (6>2)
I need a formula for counting the number of combinations within a given limit of numbers. There must only be 2 numbers given, we have to find the third.
For example, for 2(number of repetitions) and 3(limit number), the result would be 3, because there are 3 combinations for the digits: 1 and 2, 1 and 3, 2 and 3.
For 2 and 4 the result is 6,
For 3 and 5 the result is 10,
For 6 and 7 the result is 7, etc.
The first number has to be smaller than the second.
A formula is needed for figuring out the result, if the first number is A, the second is B, what would C is going to be?
You're describing combination. The formula is going to be C = B! / (A!*(B-A)!) (where ! is the factorial operation). It's also worth noting that the first number can be equal to the second -- there should only be one repetition in that case. By convention 0! == 1 and it is OK where both numbers are equal because C(n, n) = 1 and this means n!/(n! * 0!).
Unfortunately, since factorial grows very quickly (21! is too large for a 64-bit unsigned integer), you probably can't compute this directly. Wikipedia has a few algorithms you can use here.
We are required to compute the bit wise AND amongst all natural numbers lying between A and B, both inclusive.I came across this problem on a website and here is the approach they used but i couldn't understand the method.Can anyone explain this more clearly with an example ?
In order to solve this problem, we just need to focus on the occurrences of each power 2, which turn out to be cyclic. Now for each 2^i(the length of the cycle will be 2^(i+1) having 2^i zeros followed by same number of ones) we just need to compute if 1 remains constant in the given interval, which is done by simple arithmetic. If so, that power of 2 will be present in the answer, otherwise it won't.
Let's count (unsigned) with 3 bits to visualize some numbers first:
000 = 0
001 = 1
010 = 2
011 = 3
100 = 4
101 = 5
110 = 6
111 = 7
If you look at the columns, you can see that the lowest bit is alternating with a cycle of 1, the next with a cycle of 2, then 4, and the nth lowest bit is alternating with a cycle of 2^(n-1).
As soon as a bit was 0 once it is always 0 (because 0 and whatever is 0).
You could also say the nth bit is only 1 if the nth bit of A and B is 1 and d < 2^(n-1). In other words a bit will only be 1 if it is 1 at the beginning and the end and didn't had time to change to 0 in between because its cycle is too large.
I need an algorithm for A mod B with
A is a very big integer and it contains digit 1 only (ex: 1111, 1111111111111111)
B is a very big integer (ex: 1231, 1231231823127312918923)
Big, I mean 1000 digits.
To compute a number mod n, given a function to get quotient and remainder when dividing by (n+1), start by adding one to the number. Then, as long as the number is bigger than 'n', iterate:number = (number div (n+1)) + (number mod (n+1))Finally at the end, subtract one. An alternative to adding one at the beginning and subtracting one at the end is checking whether the result equals n and returning zero if so.
For example, given a function to divide by ten, one can compute 12345678 mod 9 thusly:
12345679 -> 1234567 + 9
1234576 -> 123457 + 6
123463 -> 12346 + 3
12349 -> 1234 + 9
1243 -> 124 + 3
127 -> 12 + 7
19 -> 1 + 9
10 -> 1
Subtract 1, and the result is zero.
1000 digits isn't really big, use any big integer library to get rather fast results.
If you really worry about performance, A can be written as 1111...1=(10n-1)/9 for some n, so computing A mod B can be reduced to computing ((10^n-1) mod (9*B)) / 9, and you can do that faster.
Try Montgomery reduction on how to find modulo on large numbers - http://en.wikipedia.org/wiki/Montgomery_reduction
1) Just find a language or package that does arbitrary precision arithmetic - in my case I'd try java.math.BigDecimal.
2) If you are doing this yourself, you can avoid having to do division by using doubling and subtraction. E.g. 10 mod 3 = 10 - 3 - 3 - 3 = 1 (repeatedly subtracting 3 until you can't any more) - which is incredibly slow, so double 3 until it is just smaller than 10 (e.g. to 6), subtract to leave 4, and repeat.