Solving a recurrence relation:
T(n) = qT(n/2) + cn where q is a const > 2
Ive got it to T(n) <= cn * sum{0, log_2(n) - 1} (q/2)^i
I'm trying to use geometric series to calculate this: a = 1, r = q/2, n = log_2(n)
I get
1) S = (1-r^(log_2(n)) / (1 - r) and somehow this ends up as
2) S = (r^log_2(n) - 1) / (r - 1) according to a textbook
Im confused as to how they get from 1) to 2), any explanation is much appreciated.
Related
I have a Computer Science Midterm tomorrow and I need help determining the complexity of a particular recursive function as below, which is much complicated than the stuffs I've already worked on: it has two variables
T(n) = 3 + mT(n-m)
In simpler cases where m is a constant, the formula can be easily obtained by writing unpacking the relation; however, in this case, unpacking doesn't make the life easier as follows (let's say T(0) = c):
T(n) = 3 + mT(n-m)
T(n-1) = 3 + mT(n-m-1)
T(n-2) = 3 + mT(n-m-2)
...
Obviously, there's no straightforward elimination according to these inequalities. So, I'm wondering whether or not I should use another technique for such cases.
Don't worry about m - this is just a constant parameter. However you're unrolling your recursion incorrectly. Each step of unrolling involves three operations:
Taking value of T with argument value, which is m less
Multiplying it by m
Adding constant 3
So, it will look like this:
T(n) = m * T(n - m) + 3 = (Step 1)
= m * (m * T(n - 2*m) + 3) + 3 = (Step 2)
= m * (m * (m * T(n - 3*m) + 3) + 3) + 3 = ... (Step 3)
and so on. Unrolling T(n) up to step k will be given by following formula:
T(n) = m^k * T(n - k*m) + 3 * (1 + m + m^2 + m^3 + ... + m^(k-1))
Now you set n - k*m = 0 to use the initial condition T(0) and get:
k = n / m
Now you need to use a formula for the sum of geometric progression - and finally you'll get a closed formula for the T(n) (I'm leaving that final step to you).
I was wondering how I can convert this code from Matlab to R code. It seems this is the code for midpoint method. Any help would be highly appreciated.
% Usage: [y t] = midpoint(f,a,b,ya,n) or y = midpoint(f,a,b,ya,n)
% Midpoint method for initial value problems
%
% Input:
% f - Matlab inline function f(t,y)
% a,b - interval
% ya - initial condition
% n - number of subintervals (panels)
%
% Output:
% y - computed solution
% t - time steps
%
% Examples:
% [y t]=midpoint(#myfunc,0,1,1,10); here 'myfunc' is a user-defined function in M-file
% y=midpoint(inline('sin(y*t)','t','y'),0,1,1,10);
% f=inline('sin(y(1))-cos(y(2))','t','y');
% y=midpoint(f,0,1,1,10);
function [y t] = midpoint(f,a,b,ya,n)
h = (b - a) / n;
halfh = h / 2;
y(1,:) = ya;
t(1) = a;
for i = 1 : n
t(i+1) = t(i) + h;
z = y(i,:) + halfh * f(t(i),y(i,:));
y(i+1,:) = y(i,:) + h * f(t(i)+halfh,z);
end;
I have the R code for Euler method which is
euler <- function(f, h = 1e-7, x0, y0, xfinal) {
N = (xfinal - x0) / h
x = y = numeric(N + 1)
x[1] = x0; y[1] = y0
i = 1
while (i <= N) {
x[i + 1] = x[i] + h
y[i + 1] = y[i] + h * f(x[i], y[i])
i = i + 1
}
return (data.frame(X = x, Y = y))
}
so based on the matlab code, do I need to change h in euler method (R code) to (b - a) / n to modify Euler code to midpoint method?
Note
Broadly speaking, I agree with the expressed comments; however, I decided to vote up this question. (now deleted) This is due to the existence of matconv that facilitates this process.
Answer
Given your code, we could use matconv in the following manner:
pacman::p_load(matconv)
out <- mat2r(inMat = "input.m")
The created out object will attempt to translate Matlab code into R, however, the job is far from finished. If you inspect the out object you will see that it requires further work. Simple statements are usually translated correctly with Matlab comments % replaced with # and so forth but more complex statements may require a more detailed investigation. You could then inspect respective line and attempt to evaluate them to see where further work may be required, example:
eval(parse(text=out$rCode[1]))
NULL
(first line is a comment so the output is NULL)
Due to rounding error cannot get ratio between two numbers:
Ratio=exp(x)/(exp(x)+exp(y)) such that x=-1.11e4 and y=-1.12e4.
Any mathematical or computational trick to do?
You can simplify it like this:
R = exp(x) / (exp(x) + exp(y))
= exp(x) / (exp(x) * (1 + exp(y) / exp(x)))
= 1 / (1 + exp(y) / exp(x))
= 1 / (1 + exp(y - x))
(This is the same result as derived by DiltihiumMatrix, but obtained without going into the log domain and back again.)
How about some mathematical manipulation in log-space...
R = exp(x)/[exp(x)+exp(y)]
log(R) = log[exp(x)] - log[exp(x)+exp(y)]
= log[exp(x)] - log[exp(x)*(1+exp(y)/exp(x))]
= log[exp(x)] - log[exp(x)*(1+exp(y-x)]
= log[exp(x)] - log[exp(x)] - log[(1+exp(y-x))]
= - log[(1+exp(y-x))]
Now, exp(y-x) should be a reasonable number, so you can calculate that easily. Then convert back to normal space using R = exp(log(R)).
If that still doesn't work, you can actually taylor expand the last line:
log[(1+z)] ~ 1 + z^2/2 - z^3/3 ...
for small z, in this case z = exp(y-x).
In our Data Structures class we are learning how to solve recurrence relations in 1 variable. Unfortunately some things seem to come "out of the blue".
For example, some exercises already tell you how to substitute the variable n:
Compute T(n) for n = 2^k
T(n) = a for n =< 2
T(n) = 8T(n/2) + bn^2 (a and b are > 0)
But some exercises just give you the T(n) without providing a replacement for the variable n:
T(n) = 1 n =<1
T(n) = 2T(n/4) + sqrt(n)
I used the iterative method and arrived to the right answer: sqrt(n) + (1/2) * sqrt(n) * Log(n).
But when the professor explained she started by saying: "Let n = 4^k", which is what I mean by "out of the blue". Using that fact the answer is simpler to obtain.
But how is the student supposed to come up with that?
This is another example:
T(n) = 1 n =<1
T(n) = 2T( (n-1)/2 ) + n
Here I started again with the iterative method but I can't reach a definitive answer, it looks more complex that way.
After 3 iterative steps I arrived to this:
T(n) = 4T( (n-2)/4 ) + 2n - 1
T(n) = 8T( (n-3)/8 ) + 3n - 3
T(n) = 16T( (n-4)/16 ) + 4n - 6
I am inclined to say T(i) = 2^i * T( (n-i)/2^i ) + i*n - ? This last part I can't figure out, maybe I made a mistake.
However in the answer she provides she starts again with another substitution: Let n = (2^k) -1. I don’t see where this comes from - why would I do this? What is the logic behind that?
In all of these cases, these substitutions are reasonable because they rewrite the recurrence as one of the form S(k) = aS(k - 1) + f(k). These recurrences are often easier to solve than other recurrences because they define S(k) purely in terms of S(k - 1).
Let's do some examples to see how this works. Consider this recurrence:
T(n) = 1 (if n ≤ 1)
T(n) = 2T(n/4) + sqrt(n) (otherwise)
Here, the size of the problem shrinks by a factor of four on each iteration. Therefore, if the input is a perfect power of four, then the input will shrink from size 4k to 4k-1, from 4k-1 to 4k-2, etc. until the recursion bottoms out. If we make this substitution and let S(k) = T(4k), then we get hat
S(0) = 1
S(k) = 2S(k - 1) + 2k
This is now a recurrence relation where S(k) is defined in terms of S(k - 1), which can make the recurrence easier to solve.
Let's look at your original recurrence:
T(n) = a (for n ≤ 2)
T(n) = 8T(n/2) + bn2
Notice that the recursive step divides n by two. If n is a perfect power of two, then the recursive step considers the power of two that comes right before n. Letting S(k) = T(2k) gives
S(k) = a (for k ≤ 1)
S(k) = 8S(k - 1) + b22k
Notice how that S(k) is defined in terms of S(k - 1), which is a much easier recurrence to solve. The choice of powers of two was "natural" here because it made the recursive step talk purely about the previous value of S and not some arbitrarily smaller value of S.
Now, look at the last recurrence:
T(n) = 1 (n ≤ 1)
T(n) = 2T( (n-1)/2 ) + n
We'd like to make some substitution k = f(n) such that T(f(n)) = 2T(f(n) - 1) + n. The question is how to do that.
With some trial and error, we get that setting f(n) = 2n - 1 fits the bill, since
(f(n) - 1) / 2 = ((2n - 1) - 1) / 2 = (2n - 2) / 2 = 2n-1 - 1 = f(n) - 1
Therefore, letting k = 2n - 1 and setting S(k) = T(2n - 1), we get
S(n) = 1 (if n ≤ 1)
S(n) = 2S(n - 1) + 2n - 1
Hope this helps!
Can someone explain mathematical induction to prove a recursive method? I am a freshmen computer science student and I have not yet taken Calculus (I have had up through Trig). I kind of understand it but I have trouble when asked to write out an induction proof for a recursive method.
Here is a explanation by example:
Let's say you have the following formula that you want to prove:
sum(i | i <- [1, n]) = n * (n + 1) / 2
This formula provides a closed form for the sum of all integers between 1 and n.
We will start by proving the formula for the simple base case of n = 1. In this case, both sides of the formula reduce to 1. This in turn means that the formula holds for n = 1.
Next, we will prove that if the formula holds for a value n, then it holds for the next value of n (or n + 1). In other words, if the following is true:
sum(i | i <- [1, n]) = n * (n + 1) / 2
Then the following is also true:
sum(i | i <- [1, n + 1]) = (n + 1) * (n + 2) / 2
To do so, let's start with the first side of the last formula:
s1 = sum(i | i <- [1, n + 1]) = sum(i | i <- [1, n]) + (n + 1)
That is, the sum of all integers between 1 and n + 1 is equal to the sum of integers between 1 and n, plus the last term n + 1.
Since we are basing this proof on the condition that the formula holds for n, we can write:
s1 = n * (n + 1) / 2 + (n + 1) = (n + 1) * (n + 2) / 2 = s2
As you can see, we have arrived at the second side of the formula we are trying to prove, which means that the formula does indeed hold.
This finishes the inductive proof, but what does it actually mean?
The formula is correct for n = 0.
If the formula is correct for n, then it is correct for n + 1.
From 1 and 2, we can say: if the formula is correct for n = 0, then it is correct for 0 + 1 = 1. Since we proved the case of n = 0, then the case of n = 1 is indeed correct.
We can repeat this above process again. The case of n = 1 is correct, then the case of n = 2 is correct. This reasoning can go ad infinitum; the formula is correct for all integer values of n >= 1.
induction != Calc!!!
I can get N guys drunk with 10*N beers.
Base Case: 1 guy
I can get one guy drunk with 10 beers
Inductive step, given p(n) prove p(n + 1)
I can get i guys drunk with 10 * i beers, if I add another guy, I can get him drunk with 10 more beers. Therefore, I can get i + 1 guys drunk with 10 * (i + 1) beers.
p(1) -> p(i + 1) -> p(i + 2) ... p(inf)
Discrete Math is easy!
First, you need a base case. Then you need an inductive step that holds true for some step n. In your inductive step, you will need an inductive hypothesis. That hypothesis is the assumption that you needed to have made. Finally, use that assumption to prove step n+1