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I see there are a few other questions around this exercise but none specifically are asking what is meant within the hint... "define the state transition in such a way that the product abn is unchanged from state to state".
They also mention that this idea of using an "invariant quantity" is a powerful idea with respect to "iterative algorithms". By the way, this problem calls for the design of a "logarithmic" exponent algorithm that has a space complexity of O(1).
Mainly I just have no idea what is meant by this hint and am pretty confused. Can anyone give me a nudge in what is meant by this? The only thing I can really find about "invariant quantities" are described using examples in physics which only makes this concept more opaque.
Exercise description in full:
Exercise 1.16: Design a procedure that evolves an iterative exponentiation process that uses successive squaring and uses a logarithmic number of steps, as does fast-expt. (Hint: Using the observation that (bn/2)2 = (b2)n/2, keep, along with the exponent n and the base b, an additional state variable a, and define the state transformation in such a way that the product abn is unchanged from state to state.
At the beginning of the process a is taken to be 1, and the answer is given by the value of a at the end of the process. In general, the technique of defining an invariant quantity that remains unchanged from state to state is a powerful way to think about the design of iterative algorithms.)
Thanks in advance.
This is not about any "logarithmic exponentiation", which is a very vague and confusing terminology.
As the quote you provided says, it is about exponentiation function that takes logarithmic number of steps to get its final result.
So we want to develop a function, exp(b,n), which would take O(log n) time to finish.
The numbers involved are all integers.
So how do we calculate bn? We notice that
b^n = b * b * b * b *... * b
`------n times------/ O(n) time process
and, when n is even,
b^n = b * b * b * b * ... * b *
`------n/2 times-----/
b * b * b * b * ... * b
`------n/2 times-----/
= (b^(n/2))^2 ; viewed from the side
= (b^2)^(n/2) ; viewed from above
and when n is odd,
b^n = b * b * b * b * ... * b *
`------n/2 times-----/
b * b * b * b * ... * b *
`------n/2 times-----/
b
`--1 time--/
= (b^(n/2))^2 * b
= (b^2)^(n/2) * b
Note that both expression fall into same category if we write the first one as (b^2)^(n/2) * 1.
Also note that the equation
b^n = (b )^(n ) * { a where a = 1 }
= (b ^2)^(n/2) * { a where a = ... }
= (b' )^(n' ) * { a' }
means that to calculate b^n * a is the same as to calculate b'^n' * a' with the changed values of { b' = b^2 ; n' = n/2 ; a' = {if {n is even} then {a} else {a * b}} }.
So we don't actually compute either of the sides of that equation. Instead we keep the triples { b, n, a } at each step and transform them according to that rule
{ b, n, a } --> { b', n', a' } --> ...
with the initial values of b and n as given to us, and the first a equal to 1; and know that the final result calculated from any of the triples would be the same if we'd actually calculated it somehow. We still don't know how exactly we'd do that; just that it would be the same. That's the "invariant" part.
So what all this is good for? Well, since the chain { n --> n/2 --> ... } will certainly reach a point where n == 1, we know that at that point we can break the chain since
c^1 * d == c * d
and this one simple multiplication of these two numbers will produce the same result as the initial formula.
Which is the final result of the function.
And that's the meaning of the hint: maintain the state of the computation as a (here) triple of numbers (or just three variables named b, n, a), and implement your computation as a chain of state-transforming steps. When the chain is broken according to some test (here, n == 1), we've reached our destination and can calculate the final result according to some simple rule (here, c * d).
This gives us a nice and powerful methodology for problem solving.
Oh, and we also know that the length of that chain of changing states is O(log n), since we halve that n at each step.
when fast-exp execute, the trace looks something like this
b^14 = (b^2)^7 ; where: b' = b^2; a = 1, n = 7
b^14 = (b^2)^6 * b^2 ; where: b' = b^2; a = b^2, n = 6
b^14 = ((b^2)^2)^3 *b^2 ; where: b' = b^2^2 ; a = b^2, n = 3
b^14 = ((b^2)^2)^2 *b^2 *(b^2)^2 ; where: b' = b^2^2; a = b^2*(b^2)^2, n = 2
notice that all these statements have the general form of b^n = b'^n' * a', and this does not change
I am trying to do this programming task:
Write a program that will calculate the number of trailing zeros in a
factorial of a given number.
N! = 1 * 2 * 3 * ... * N
Be careful 1000! has 2568 digits.
For more info, see: http://mathworld.wolfram.com/Factorial.html
Examples:
zeros(6) = 1 ->
6! = 1 * 2 * 3 * 4 * 5 * 6 = 720 --> 1 trailing zero
zeros(12) = 2 ->
12! = 479001600 --> 2 trailing zeros
I'm confused as one of the sample tests I have is showing this: expect_equal(zeros(30), 7)
I could be misunderstanding the task, but where do the trailing 7 zeros come from when the input is 30?
with scientific notation turned on I get this:
2.6525286e+32
and with it turned off I get this:
265252859812191032282026086406022
What you are experiencing is a result of this: Why are these numbers not equal?
But in this case, calculating factorials to find the numbers of trailing zeros is not that efficient.
We can count number of 5-factors in a number (since there will be always enough 2-factors to pair with them and create 10-factors). This function gives you trailing zeros for a factorial by counting 5-factors in a given number.
tailingzeros_factorial <- function(N){
mcount = 0L
mdiv = 5L
N = as.integer(N)
while (as.integer((N/mdiv)) > 0L) {
mcount = mcount + as.integer(N/mdiv)
mdiv = as.integer(mdiv * 5L)
}
return(mcount)
}
tailingzeros_factorial(6)
#> 1
tailingzeros_factorial(25)
#> 6
tailingzeros_factorial(30)
#> 7
Combinations without repetitions look like this, when the number of elements to choose from (n) is 5 and elements chosen (r) is 3:
0 1 2
0 1 3
0 1 4
0 2 3
0 2 4
0 3 4
1 2 3
1 2 4
1 3 4
2 3 4
As n and r grows the amount of combinations gets large pretty quickly. For (n,r) = (200,4) the number of combinations is 64684950.
It is easy to iterate the list with r nested for-loops, where the initial iterating value of each for loop is greater than the current iterating value of the for loop in which it is nested, as in this jsfiddle example:
https://dotnetfiddle.net/wHWK5o
What I would like is a function that calculates only one combination based on its index. Something like this:
tuple combination(i,n,r) {
return [combination with index i, when the number of elements to choose from is n and elements chosen is r]
Does anyone know if this is doable?
You would first need to impose some sort of ordering on the set of all combinations available for a given n and r, such that a linear index makes sense. I suggest we agree to keep our combinations in increasing order (or, at least, the indices of the individual elements), as in your example. How then can we go from a linear index to a combination?
Let us first build some intuition for the problem. Suppose we have n = 5 (e.g. the set {0, 1, 2, 3, 4}) and r = 3. How many unique combinations are there in this case? The answer is of course 5-choose-3, which evaluates to 10. Since we will sort our combinations in increasing order, consider for a minute how many combinations remain once we have exhausted all those starting with 0. This must be 4-choose-3, or 4 in total. In such a case, if we are looking for the combination at index 7 initially, this implies we must subtract 10 - 4 = 6 and search for the combination at index 1 in the set {1, 2, 3, 4}. This process continues until we find a new index that is smaller than this offset.
Once this process concludes, we know the first digit. Then we only need to determine the remaining r - 1 digits! The algorithm thus takes shape as follows (in Python, but this should not be too difficult to translate),
from math import factorial
def choose(n, k):
return factorial(n) // (factorial(k) * factorial(n - k))
def combination_at_idx(idx, elems, r):
if len(elems) == r:
# We are looking for r elements in a list of size r - thus, we need
# each element.
return elems
if len(elems) == 0 or len(elems) < r:
return []
combinations = choose(len(elems), r) # total number of combinations
remains = choose(len(elems) - 1, r) # combinations after selection
offset = combinations - remains
if idx >= offset: # combination does not start with first element
return combination_at_idx(idx - offset, elems[1:], r)
# We now know the first element of the combination, but *not* yet the next
# r - 1 elements. These need to be computed as well, again recursively.
return [elems[0]] + combination_at_idx(idx, elems[1:], r - 1)
Test-driving this with your initial input,
N = 5
R = 3
for idx in range(choose(N, R)):
print(idx, combination_at_idx(idx, list(range(N)), R))
I find,
0 [0, 1, 2]
1 [0, 1, 3]
2 [0, 1, 4]
3 [0, 2, 3]
4 [0, 2, 4]
5 [0, 3, 4]
6 [1, 2, 3]
7 [1, 2, 4]
8 [1, 3, 4]
9 [2, 3, 4]
Where the linear index is zero-based.
Start with the first element of the result. The value of that element depends on the number of combinations you can get with smaller elements. For each such smaller first element, the number of combinations with first element k is n − k − 1 choose r − 1, with potentially some of-by-one corrections. So you would sum over a bunch of binomial coefficients. Wolfram Alpha can help you compute such a sum, but the result still has a binomial coefficient in it. Solving for the largest k such that the sum doesn't exceed your given index i is a computation you can't do with something as simple as e.g. a square root. You need a loop to test possible values, e.g. like this:
def first_naive(i, n, r):
"""Find first element and index of first combination with that first element.
Returns a tuple of value and index.
Example: first_naive(8, 5, 3) returns (1, 6) because the combination with
index 8 is [1, 3, 4] so it starts with 1, and because the first combination
that starts with 1 is [1, 2, 3] which has index 6.
"""
s1 = 0
for k in range(n):
s2 = s1 + choose(n - k - 1, r - 1)
if i < s2:
return k, s1
s1 = s2
You can reduce the O(n) loop iterations to O(log n) steps using bisection, which is particularly relevant for large n. In that case I find it easier to think about numbering items from the end of your list. In the case of n = 5 and r = 3 you get choose(2, 2)=1 combinations starting with 2, choose(3,2)=3 combinations starting with 1 and choose(4,2)=6 combinations starting with 0. So in the general choose(n,r) binomial coefficient you increase the n with each step, and keep the r. Taking into account that sum(choose(k,r) for k in range(r,n+1)) can be simplified to choose(n+1,r+1), you can eventually come up with bisection conditions like the following:
def first_bisect(i, n, r):
nCr = choose(n, r)
k1 = r - 1
s1 = nCr
k2 = n
s2 = 0
while k2 - k1 > 1:
k3 = (k1 + k2) // 2
s3 = nCr - choose(k3, r)
if s3 <= i:
k2, s2 = k3, s3
else:
k1, s1 = k3, s3
return n - k2, s2
Once you know the first element to be k, you also know the index of the first combination with that same first element (also returned from my function above). You can use the difference between that first index and your actual index as input to a recursive call. The recursive call would be for r − 1 elements chosen from n − k − 1. And you'd add k + 1 to each element from the recursive call, since the top level returns values starting at 0 while the next element has to be greater than k in order to avoid duplication.
def combination(i, n, r):
"""Compute combination with a given index.
Equivalent to list(itertools.combinations(range(n), r))[i].
Each combination is represented as a tuple of ascending elements, and
combinations are ordered lexicograplically.
Args:
i: zero-based index of the combination
n: number of possible values, will be taken from range(n)
r: number of elements in result list
"""
if r == 0:
return []
k, ik = first_bisect(i, n, r)
return tuple([k] + [j + k + 1 for j in combination(i - ik, n - k - 1, r - 1)])
I've got a complete working example, including an implementation of choose, more detailed doc strings and tests for some basic assumptions.
Given a pyramid like:
0
1 2
3 4 5
6 7 8 9
...
and given the index of the pyramid i where i represents the ith number of the pyramid, is there a way to find the index of the row to which the ith element belongs? (e.g. if i = 6,7,8,9, it is in the 3rd row, starting from row 0)
There's a connection between the row numbers and the triangular numbers. The nth triangular number, denoted Tn, is given by Tn = n(n-1)/2. The first couple triangular numbers are 0, 1, 3, 6, 10, 15, etc., and if you'll notice, the starts of each row are given by the nth triangular number (the fact that they come from this triangle is where this name comes from.)
So really, the goal here is to determine the largest n such that Tn ≤ i. Without doing any clever math, you could solve this in time O(√n) by just computing T0, T1, T2, etc. until you find something bigger than i. Even better, you could binary search for it in time O(log n) by computing T1, T2, T4, T8, etc. until you overshoot, then binary searching on the range you found.
Alternatively, we could try to solve for this directly. Suppose we want to find the choice of n such that
n(n + 1) / 2 = i
Expanding, we get
n2 / 2 + n / 2 = i.
Equivalently,
n2 / 2 + n / 2 - i = 0,
or, more easily:
n2 + n - 2i = 0.
Now we use the quadratic formula:
n = (-1 ± √(1 + 8i)) / 2
The negative root we can ignore, so the value of n we want is
n = (-1 + √(1 + 8i)) / 2.
This number won't necessarily be an integer, so to find the row you want, we just round down:
row = ⌊(-1 + √(1 + 8i)) / 2⌋.
In code:
int row = int((-1 + sqrt(1 + 8 * i)) / 2);
Let's confirm that this works by testing it out a bit. Where does 9 go? Well, we have
(-1 + √(1 + 72)) / 2 = (-1 + √73) / 2 = 3.77
Rounding down, we see it goes in row 3 - which is correct!
Trying another one, where does 55 go? Well,
(-1 + √(1 + 440)) / 2 = (√441 - 1) / 2 = 10
So it should go in row 10. The tenth triangular number is T10 = 55, so in fact, 55 starts off that row. Looks like it works!
I get row = math.floor (√(2i + 0.25) - 0.5) where i is your number
Essentially the same as the guy above but I reduced n2 + n to (n + 0.5)2 - 0.25
I think ith element belongs nth row where n is number of n(n+1)/2 <= i < (n+1)(n+2)/2
For example, if i = 6, then n = 3 because n(n+1)/2 <= 6
and if i = 8, then n = 3 because n(n+1)/2 <= 8
Suppose there is number s=12 , now i want to make sequence with the element a1+a2+.....+an=12.
The criteria is as follows-
n must be minimum.
a1 and an must be 1;
ai can differs a(i-1) by only 1,0 and -1.
for s=12 the result is 6.
So how to find the minimum value of n.
Algorithm for finding n from given s:
1.Find q = FLOOR( SQRT(s-1) )
2.Find r = q^2 + q
3.If s <= r then n = 2q, else n = 2q + 1
Example: s = 12
q = FLOOR( SQRT(12-1) ) = FLOOR(SQRT(11) = 3
r = 3^2 + 3 = 12
12 <= 12, therefore n = 2*3 = 6
Example: s = 160
q = FLOOR( SQRT(160-1) ) = FLOOR(SQRT(159) = 12
r = 12^2 + 12 = 156
159 > 156, therefore n = 2*12 + 1 = 25
and the 25-numbers sequence for
159: 1,2,3,4,5,6,7,8,9,10,10,10,9,10,10,10,9,8,7,6,5,4,3,2,1
Here's a way to visualize the solution.
First, draw the smallest triangle (rows containing successful odd numbers of stars) that has a greater or equal number of stars to n. In this case, we draw a 16-star triangle.
*
***
*****
*******
Then we have to remove 16 - 12 = 4 more stars. We do this diagonally starting from the top.
1
**2
****3
******4
The result is:
**
****
******
Finally, add up the column heights to get the final answer:
1, 2, 3, 3, 2, 1.
There are two cases: s odd and s even. When s is odd, you have the sequence:
1, 2, 3, ..., (s-1)/2, (s-1)/2, (s-1)/2-1, (s-1)/2-2, ..., 1
when n is even you have:
1, 2, 3, ..., s/2, s/2-1, s/2-2, ..., 1
The maximum possible for any given series of length n is:
n is even => (n^2+2n)/4
n is odd => (n+1)^2/4
These two results are arrived at easily enough by looking at the simple arithmetic sum of series where in the case of n even it is twice the sum of the series 1...n/2. In the case of n odd it is twice the sum of the series 1...(n-1)/2 and add on n+1/2 (the middle element).
Clearly you can generate any positive number that is less than this max as long as n>3.
So the problem then becomes finding the smallest n with a max greater than your target.
Algorithmically I'd go for:
Find (sqrt(4*s)-1) and round up to the next odd number. Call this M. This is an easy to work out value and will represent the lowest odd n that will work.
Check M-1 to see if its max sum is greater than s. If so then that your n is M-1. Otherwise your n is M.
Thank all you answer me. I derived a simpler solution. The algorithm looks like-
First find what is the maximum sum that can be made using n element-
if n=1 -> 1 sum=1;
if n=2 -> 1,1 sum=2;
if n=3 -> 1,2,1 sum=4;
if n=4 -> 1,2,2,1 sum=6;
if n=5 -> 1,2,3,2,1 sum=9;
if n=6 -> 1,2,3,3,2,1 sum=12;
So from observation it is clear that form any number,n 9<n<=12 can be
made using 6 element, similarly number
6<n<=9 can be made at using 5 element.
So it require only a binary search to find the number of
element that make a particular number.