Can anyone help with this?
while (x > level)
x = x – 1;
x = 0
Cyclomatic complexity can be computed using the formula provided here.
Cyclomatic complexity = E - N + P
where,
E = number of edges in the flow graph.
N = number of nodes in the flow graph.
P = number of nodes that have exit points
For your case, the graph should look like this:
--------------- ----------
| x > level |----- NO ------>| x = x-1|
|-------------| ----|-----
| |---------------------
|
Yes
|
-------|----------
| End while (if) |
-------|----------
|
|
---------
| x = 0 |
----------
(not an ASCII art person)
So, the cyclomatic complexity should be:
E = 4, N = 4, P = 2 => Complexity = 4 - 4 + 2 = 2
[edit]
Ira Baxter points out very well how to simplify this computation for languages like Java, C#, C++ etc. However, identifying the conditionals must be carefully performed, as shown here:
- Start with a count of one for the method.
- Add one for each of the following flow-related elements that are found in the method.
Returns - Each return that isn't the last statement of a method.
Selection - if, else, case, default.
Loops - for, while, do-while, break, and continue.
Operators - &&, ||, ?, and :
Exceptions - catch, finally, throw, or throws clause.
Related
The outer loop executes n times while the inner loop executes ? So the total time is n*something.
Do i need to learn summation,if yes then any book to refer?
for(int i=1;i<=n;i++)
for(int j=1;j<=n;j+=i)
printf("*");
This question can be approached by inspection:
n = 16
i | j values | # terms
1 | 1, 2, ..., 16 | n
2 | 1, 3, 5, ..., 16 | n / 2
.. | .. | n / 3
16 | 16 | n / n
In the above table, i is the outer loop value, and j values show the iterations of the inner loop. By inspection, we can see that the loops will take n * (1 + 1/2 + 1/3 + ... + 1/n) steps. This is a bounded harmonic series. As this Math Stack Exchange article shows, there is no closed form for the above expression in terms of n. However, as this SO article shows, there is an upper bound of O(n*ln(n)).
So, the running time for your two loops is O(n*ln(n)).
I believe the time complexity of that is O(n*log(n)). Here is why:
Let us pick some arbitrary natural number i and see how many steps the inner loop takes for this given i. Well for this i, you are going from j=1 to j<=n with a jump of i in between. So basically you are doing this summation many steps:
summation = 1 + (1+i) + (1+2i) + ... (1+ki)
where k is the largest integer such that 1+ki <= n. That is, k is the number of steps and this is what we want to solve for. Well we can solve for k in the equality resulting in k <= (n-1)/i and thus k = ⌊(n-1)/i⌋. That is, k is the floor function/integer division of (n-1)/i. Since we are dealing with time complexities, this floor function doesn't matter so we will just say k = n/i for simplicity. This is the number of steps that the inner loop will take for a given i. So we basically need to add all these for i = 1 to i <= n.
So numsteps will be this addition:
numsteps = n/1 + n/2 + n/3 + ... n/n
= n(1 + 1/2 + 1/3 + ... 1+n)
So we need to find the sum of 1 + 1/2 + ... 1/n to finish this. There is actually no good closed form for this sum but it is on the order of ln(n). You can read more about this here. You can also guess this since the integral from 1 to n of 1/x is ln(n). Again, since we are dealing with time complexity, we can just use ln(n) to represent its complexity. Thus we have:
numsteps = n(ln(n))
And so the time complexity is O(n*log(n)).
Edit: My bad, i was calculating the sum :P
**{a^i b^j c^k d^m | i+j=k+m | i<m}**
The grammar should allow the language in order abbccd not cbbcda. First should be the a's then b's and so on.
I know that you must "count" the number of a's and b's you are adding to make sure there are an equivalent number of c's and d's. I just can't seem to figure out how to make sure there are more c's than a's in the language. I appreciate any help anyone can give. I've been working on this for many hours now.
Edit:
The grammar should be Context Free
I have only got these two currently because all others turned out to be very wrong:
S -> C A D
| B
B -> C B D
|
C -> a
| b
D -> c
| d
and
S -> a S d
| A
A -> b A c
|
(which is close but doesn't satisfy the i < k part)
EDIT: This is for when i < k, not i < m. OP changed the problem, but I figure this answer may still be useful.
This is not a context free grammar, and this can be proven with the pumping lemma which states that if the grammar is context free, there exists an integer p > 0, such that all strings in the language of length >= p can be split into a substring uvwxy, where len(vx) >= 1, len(vwx) <= p, and uvnwxny is a member of the language for all n >= 0.
Suppose that a value of p exists. We can create a string such that:
k = i + 1
j = m + 1
j > p
k > p
v and x cannot contain more than one type of character or be both on the left side or both on the right side, because then raising them to powers would break the grammar immediately. They cannot be the same character as each other, because then multiplying them would break the rule that i + j = k + m. v cannot be a if x is d, because then w contains the bs and cs, which makes len(vwx) > p. By the same reasoning, v cannot be as if x is cs, and v cannot be bs if x is ds. The only remaining option is bs and cs, but setting n to 0 would make i >= k and j >= m, breaking the grammar.
Therefore, it is not a context free grammar.
There has to be at least one d because i < m, so there has to be a b somewhere to offset it. T and V guarantee this criterion before moving to S, the accepted state.
T ::= bd | bTd
U ::= bc | bUc
V ::= bUd | bVd
S ::= T | V | aSd
I'm working on some Hoare logic and I am wondering whether my approach is the right one.
I have the following program P:
s = 0
i = 1
while (i <= n) {
s = s + i
i = i + 1
}
It should satisfy the hoare triple {n >= 0}P{s = n*(n+1)/2} (so it just takes the sum). Now, initially I had |s = i*(i-1)/2| as my invariant, which works fine. However, I had a problem from going to the end of my loop, to my desired postcondition. Because for the impliciation
|s = i*(i-1)/2 & i > n|
=>
| s = n * (n+1) / 2 |
to hold, I need to prove that i is n+1, and not just any i bigger than n. So what I thought of is to add a (i <= n + 1) to the invariant, so that it becomes :
|s = i * (i-1)/2 & i <= n+1|
Then I can prove the program so I think it should be correct.
Nonetheless, I find the invariant to be a bit, less "invariantly" :). And not like anything I've seen in the course or in the exercises so far, so I was wondering if there was a more elegant solution here?
So what I thought of is to add a (i <= n + 1) to the invariant, so that it becomes :
|s = i * (i-1)/2 & i <= n+1|
Nonetheless, I find the invariant to be a bit, less "invariantly" :). And not like anything I've seen in the course or in the exercises so far, so I was wondering if there was a more elegant solution here?
Nope, given the way the code is written, that's exactly the way to go. (I can tell from experience since I've been teaching Hoare logic during several semesters in two different courses and since it's part of my graduate studies.)
Using i <= n is common practice when programming. In your particular program, you could just as well have written i != n+1 instead, in which case your first invariant (which indeed looks cleaner) would have sufficed since you get
| s=i*(i-1)/2 & i=n+1 |
=>
| s=n*(n+1)/2 |
which evidently holds.
There is another way to reason,given a more appropriate invariant (and other code)...searh n for final value of i...
I : s = i*(i+1)/2 and 0 <= i <=n
B : i < n
Now,evidently you have for post condition:
I and i >= n => s = i*(i+1)/2 and i=n => s = n*(n+1)/2
The code now becomes
s = 0
i = 0
while (i < n) {
s = s + (i+1)
i = i + 1
}
The invariant holds at init and keeps after each loop,since rewriting I as 2s=i*(i+1) we have to proof
I and i<n => 2(s + (i+1)) = (i+1)*(i+2)
2(s + (i+1) )=
2s + 2(i+1) =
i*(i+1) + 2(i+1)= (since I holds)
(i+1)(i+2)
Qed.
I have been reading about bit operators in Objective-C in Kochan's book, "Programming in Objective-C".
I am VERY confused about this part, although I have really understood most everything else presented to me thus far.
Here is a quote from the book:
The Bitwise AND Operator
Bitwise ANDing is frequently used for masking operations. That is, this operator can be used easily to set specific bits of a data item to 0. For example, the statement
w3 = w1 & 3;
assigns to w3 the value of w1 bitwise ANDed with the constant 3. This has the same ffect of setting all the bits in w, other than the rightmost two bits to 0 and preserving the rightmost two bits from w1.
As with all binary arithmetic operators in C, the binary bit operators can also be used as assignment operators by adding an equal sign. The statement
word &= 15;
therefore performs the same function as the following:
word = word & 15;
Additionally, it has the effect of setting all but the rightmost four bits of word to 0. When using constants in performing bitwise operations, it is usually more convenient to express the constants in either octal or hexadecimal notation.
OK, so that is what I'm trying to understand. Now, I'm extremely confused with pretty much this entire concept and I am just looking for a little clarification if anyone is willing to help me out on that.
When the book references "setting all the bits" now, all of the bits.. What exactly is a bit. Isn't that just a 0 or 1 in 2nd base, in other words, binary?
If so, why, in the first example, are all of the bits except the "rightmost 2" to 0? Is it 2 because it's 3 - 1, taking 3 from our constant?
Thanks!
Numbers can be expressed in binary like this:
3 = 000011
5 = 000101
10 = 001010
...etc. I'm going to assume you're familiar with binary.
Bitwise AND means to take two numbers, line them up on top of each other, and create a new number that has a 1 where both numbers have a 1 (everything else is 0).
For example:
3 => 00011
& 5 => 00101
------ -------
1 00001
Bitwise OR means to take two numbers, line them up on top of each other, and create a new number that has a 1 where either number has a 1 (everything else is 0).
For example:
3 => 00011
| 5 => 00101
------ -------
7 00111
Bitwise XOR (exclusive OR) means to take two numbers, line them up on top of each other, and create a new number that has a 1 where either number has a 1 AND the other number has a 0 (everything else is 0).
For example:
3 => 00011
^ 5 => 00101
------ -------
6 00110
Bitwise NOR (Not OR) means to take the Bitwise OR of two numbers, and then reverse everything (where there was a 0, there's now a 1, where there was a 1, there's now a 0).
Bitwise NAND (Not AND) means to take the Bitwise AND of two numbers, and then reverse everything (where there was a 0, there's now a 1, where there was a 1, there's now a 0).
Continuing: why does word &= 15 set all but the 4 rightmost bits to 0? You should be able to figure it out now...
n => abcdefghjikl
& 15 => 000000001111
------ --------------
? 00000000jikl
(0 AND a = 0, 0 AND b = 0, ... j AND 1 = j, i AND 1 = i, ...)
How is this useful? In many languages, we use things called "bitmasks". A bitmask is essentially a number that represents a whole bunch of smaller numbers combined together. We can combine numbers together using OR, and pull them apart using AND. For example:
int MagicMap = 1;
int MagicWand = 2;
int MagicHat = 4;
If I only have the map and the hat, I can express that as myInventoryBitmask = (MagicMap | MagicHat) and the result is my bitmask. If I don't have anything, then my bitmask is 0. If I want to see if I have my wand, then I can do:
int hasWand = (myInventoryBitmask & MagicWand);
if (hasWand > 0) {
printf("I have a wand\n");
} else {
printf("I don't have a wand\n");
}
Get it?
EDIT: more stuff
You'll also come across the "bitshift" operator: << and >>. This just means "shift everything left n bits" or "shift everything right n bits".
In other words:
1 << 3 = 0001 << 3 = 0001000 = 8
And:
8 >> 2 = 01000 >> 2 = 010 = 2
"Bit" is short for "binary digit". And yes, it's a 0 or 1. There are almost always 8 in a byte, and they're written kinda like decimal numbers are -- with the most significant digit on the left, and the least significant on the right.
In your example, w1 & 3 masks everything but the two least significant (rightmost) digits because 3, in binary, is 00000011. (2 + 1) The AND operation returns 0 if either bit being ANDed is 0, so everything but the last two bits are automatically 0.
w1 = ????...??ab
3 = 0000...0011
--------------------
& = 0000...00ab
0 & any bit N = 0
1 & any bit N = N
So, anything bitwise anded with 3 has all their bits except the last two set to 0. The last two bits, a and b in this case, are preserved.
#cHao & all: No! Bits are not numbers. They’re not zero or one!
Well, 0 and 1 are possible and valid interpretations. Zero and one is the typical interpretation.
But a bit is only a thing, representing a simple alternative. It says “it is” or “it is not”. It doesn’t say anything about the thing, the „it“, itself. It doesn’t tell, what thing it is.
In most cases this won’t bother you. You can take them for numbers (or parts, digits, of numbers) as you (or the combination of programming languages, cpu and other hardware, you know as being “typical”) usaly do – and maybe you’ll never have trouble with them.
But there is no principal problem if you switch the meaning of “0“ and “1”. Ok, if doing this while programming assembler, you’ll find it a bit problematic as some mnemonics will do other logic then they tell you with their names, numbers will be negated and such things.
Have a look at http://webdocs.cs.ualberta.ca/~amaral/courses/329/webslides/Topic2-DeMorganLaws/sld017.htm if you want.
Greetings
I understand Big-O notation, but I don't know how to calculate it for many functions. In particular, I've been trying to figure out the computational complexity of the naive version of the Fibonacci sequence:
int Fibonacci(int n)
{
if (n <= 1)
return n;
else
return Fibonacci(n - 1) + Fibonacci(n - 2);
}
What is the computational complexity of the Fibonacci sequence and how is it calculated?
You model the time function to calculate Fib(n) as sum of time to calculate Fib(n-1) plus the time to calculate Fib(n-2) plus the time to add them together (O(1)). This is assuming that repeated evaluations of the same Fib(n) take the same time - i.e. no memoization is used.
T(n<=1) = O(1)
T(n) = T(n-1) + T(n-2) + O(1)
You solve this recurrence relation (using generating functions, for instance) and you'll end up with the answer.
Alternatively, you can draw the recursion tree, which will have depth n and intuitively figure out that this function is asymptotically O(2n). You can then prove your conjecture by induction.
Base: n = 1 is obvious
Assume T(n-1) = O(2n-1), therefore
T(n) = T(n-1) + T(n-2) + O(1) which is equal to
T(n) = O(2n-1) + O(2n-2) + O(1) = O(2n)
However, as noted in a comment, this is not the tight bound. An interesting fact about this function is that the T(n) is asymptotically the same as the value of Fib(n) since both are defined as
f(n) = f(n-1) + f(n-2).
The leaves of the recursion tree will always return 1. The value of Fib(n) is sum of all values returned by the leaves in the recursion tree which is equal to the count of leaves. Since each leaf will take O(1) to compute, T(n) is equal to Fib(n) x O(1). Consequently, the tight bound for this function is the Fibonacci sequence itself (~θ(1.6n)). You can find out this tight bound by using generating functions as I'd mentioned above.
Just ask yourself how many statements need to execute for F(n) to complete.
For F(1), the answer is 1 (the first part of the conditional).
For F(n), the answer is F(n-1) + F(n-2).
So what function satisfies these rules? Try an (a > 1):
an == a(n-1) + a(n-2)
Divide through by a(n-2):
a2 == a + 1
Solve for a and you get (1+sqrt(5))/2 = 1.6180339887, otherwise known as the golden ratio.
So it takes exponential time.
I agree with pgaur and rickerbh, recursive-fibonacci's complexity is O(2^n).
I came to the same conclusion by a rather simplistic but I believe still valid reasoning.
First, it's all about figuring out how many times recursive fibonacci function ( F() from now on ) gets called when calculating the Nth fibonacci number. If it gets called once per number in the sequence 0 to n, then we have O(n), if it gets called n times for each number, then we get O(n*n), or O(n^2), and so on.
So, when F() is called for a number n, the number of times F() is called for a given number between 0 and n-1 grows as we approach 0.
As a first impression, it seems to me that if we put it in a visual way, drawing a unit per time F() is called for a given number, wet get a sort of pyramid shape (that is, if we center units horizontally). Something like this:
n *
n-1 **
n-2 ****
...
2 ***********
1 ******************
0 ***************************
Now, the question is, how fast is the base of this pyramid enlarging as n grows?
Let's take a real case, for instance F(6)
F(6) * <-- only once
F(5) * <-- only once too
F(4) **
F(3) ****
F(2) ********
F(1) **************** <-- 16
F(0) ******************************** <-- 32
We see F(0) gets called 32 times, which is 2^5, which for this sample case is 2^(n-1).
Now, we want to know how many times F(x) gets called at all, and we can see the number of times F(0) is called is only a part of that.
If we mentally move all the *'s from F(6) to F(2) lines into F(1) line, we see that F(1) and F(0) lines are now equal in length. Which means, total times F() gets called when n=6 is 2x32=64=2^6.
Now, in terms of complexity:
O( F(6) ) = O(2^6)
O( F(n) ) = O(2^n)
There's a very nice discussion of this specific problem over at MIT. On page 5, they make the point that, if you assume that an addition takes one computational unit, the time required to compute Fib(N) is very closely related to the result of Fib(N).
As a result, you can skip directly to the very close approximation of the Fibonacci series:
Fib(N) = (1/sqrt(5)) * 1.618^(N+1) (approximately)
and say, therefore, that the worst case performance of the naive algorithm is
O((1/sqrt(5)) * 1.618^(N+1)) = O(1.618^(N+1))
PS: There is a discussion of the closed form expression of the Nth Fibonacci number over at Wikipedia if you'd like more information.
You can expand it and have a visulization
T(n) = T(n-1) + T(n-2) <
T(n-1) + T(n-1)
= 2*T(n-1)
= 2*2*T(n-2)
= 2*2*2*T(n-3)
....
= 2^i*T(n-i)
...
==> O(2^n)
Recursive algorithm's time complexity can be better estimated by drawing recursion tree, In this case the recurrence relation for drawing recursion tree would be T(n)=T(n-1)+T(n-2)+O(1)
note that each step takes O(1) meaning constant time,since it does only one comparison to check value of n in if block.Recursion tree would look like
n
(n-1) (n-2)
(n-2)(n-3) (n-3)(n-4) ...so on
Here lets say each level of above tree is denoted by i
hence,
i
0 n
1 (n-1) (n-2)
2 (n-2) (n-3) (n-3) (n-4)
3 (n-3)(n-4) (n-4)(n-5) (n-4)(n-5) (n-5)(n-6)
lets say at particular value of i, the tree ends, that case would be when n-i=1, hence i=n-1, meaning that the height of the tree is n-1.
Now lets see how much work is done for each of n layers in tree.Note that each step takes O(1) time as stated in recurrence relation.
2^0=1 n
2^1=2 (n-1) (n-2)
2^2=4 (n-2) (n-3) (n-3) (n-4)
2^3=8 (n-3)(n-4) (n-4)(n-5) (n-4)(n-5) (n-5)(n-6) ..so on
2^i for ith level
since i=n-1 is height of the tree work done at each level will be
i work
1 2^1
2 2^2
3 2^3..so on
Hence total work done will sum of work done at each level, hence it will be 2^0+2^1+2^2+2^3...+2^(n-1) since i=n-1.
By geometric series this sum is 2^n, Hence total time complexity here is O(2^n)
The proof answers are good, but I always have to do a few iterations by hand to really convince myself. So I drew out a small calling tree on my whiteboard, and started counting the nodes. I split my counts out into total nodes, leaf nodes, and interior nodes. Here's what I got:
IN | OUT | TOT | LEAF | INT
1 | 1 | 1 | 1 | 0
2 | 1 | 1 | 1 | 0
3 | 2 | 3 | 2 | 1
4 | 3 | 5 | 3 | 2
5 | 5 | 9 | 5 | 4
6 | 8 | 15 | 8 | 7
7 | 13 | 25 | 13 | 12
8 | 21 | 41 | 21 | 20
9 | 34 | 67 | 34 | 33
10 | 55 | 109 | 55 | 54
What immediately leaps out is that the number of leaf nodes is fib(n). What took a few more iterations to notice is that the number of interior nodes is fib(n) - 1. Therefore the total number of nodes is 2 * fib(n) - 1.
Since you drop the coefficients when classifying computational complexity, the final answer is θ(fib(n)).
It is bounded on the lower end by 2^(n/2) and on the upper end by 2^n (as noted in other comments). And an interesting fact of that recursive implementation is that it has a tight asymptotic bound of Fib(n) itself. These facts can be summarized:
T(n) = Ω(2^(n/2)) (lower bound)
T(n) = O(2^n) (upper bound)
T(n) = Θ(Fib(n)) (tight bound)
The tight bound can be reduced further using its closed form if you like.
It is simple to calculate by diagramming function calls. Simply add the function calls for each value of n and look at how the number grows.
The Big O is O(Z^n) where Z is the golden ratio or about 1.62.
Both the Leonardo numbers and the Fibonacci numbers approach this ratio as we increase n.
Unlike other Big O questions there is no variability in the input and both the algorithm and implementation of the algorithm are clearly defined.
There is no need for a bunch of complex math. Simply diagram out the function calls below and fit a function to the numbers.
Or if you are familiar with the golden ratio you will recognize it as such.
This answer is more correct than the accepted answer which claims that it will approach f(n) = 2^n. It never will. It will approach f(n) = golden_ratio^n.
2 (2 -> 1, 0)
4 (3 -> 2, 1) (2 -> 1, 0)
8 (4 -> 3, 2) (3 -> 2, 1) (2 -> 1, 0)
(2 -> 1, 0)
14 (5 -> 4, 3) (4 -> 3, 2) (3 -> 2, 1) (2 -> 1, 0)
(2 -> 1, 0)
(3 -> 2, 1) (2 -> 1, 0)
22 (6 -> 5, 4)
(5 -> 4, 3) (4 -> 3, 2) (3 -> 2, 1) (2 -> 1, 0)
(2 -> 1, 0)
(3 -> 2, 1) (2 -> 1, 0)
(4 -> 3, 2) (3 -> 2, 1) (2 -> 1, 0)
(2 -> 1, 0)
The naive recursion version of Fibonacci is exponential by design due to repetition in the computation:
At the root you are computing:
F(n) depends on F(n-1) and F(n-2)
F(n-1) depends on F(n-2) again and F(n-3)
F(n-2) depends on F(n-3) again and F(n-4)
then you are having at each level 2 recursive calls that are wasting a lot of data in the calculation, the time function will look like this:
T(n) = T(n-1) + T(n-2) + C, with C constant
T(n-1) = T(n-2) + T(n-3) > T(n-2) then
T(n) > 2*T(n-2)
...
T(n) > 2^(n/2) * T(1) = O(2^(n/2))
This is just a lower bound that for the purpose of your analysis should be enough but the real time function is a factor of a constant by the same Fibonacci formula and the closed form is known to be exponential of the golden ratio.
In addition, you can find optimized versions of Fibonacci using dynamic programming like this:
static int fib(int n)
{
/* memory */
int f[] = new int[n+1];
int i;
/* Init */
f[0] = 0;
f[1] = 1;
/* Fill */
for (i = 2; i <= n; i++)
{
f[i] = f[i-1] + f[i-2];
}
return f[n];
}
That is optimized and do only n steps but is also exponential.
Cost functions are defined from Input size to the number of steps to solve the problem. When you see the dynamic version of Fibonacci (n steps to compute the table) or the easiest algorithm to know if a number is prime (sqrt(n) to analyze the valid divisors of the number). you may think that these algorithms are O(n) or O(sqrt(n)) but this is simply not true for the following reason:
The input to your algorithm is a number: n, using the binary notation the input size for an integer n is log2(n) then doing a variable change of
m = log2(n) // your real input size
let find out the number of steps as a function of the input size
m = log2(n)
2^m = 2^log2(n) = n
then the cost of your algorithm as a function of the input size is:
T(m) = n steps = 2^m steps
and this is why the cost is an exponential.
Well, according to me to it is O(2^n) as in this function only recursion is taking the considerable time (divide and conquer). We see that, the above function will continue in a tree until the leaves are approaches when we reach to the level F(n-(n-1)) i.e. F(1). So, here when we jot down the time complexity encountered at each depth of tree, the summation series is:
1+2+4+.......(n-1)
= 1((2^n)-1)/(2-1)
=2^n -1
that is order of 2^n [ O(2^n) ].
No answer emphasizes probably the fastest and most memory efficient way to calculate the sequence. There is a closed form exact expression for the Fibonacci sequence. It can be found by using generating functions or by using linear algebra as I will now do.
Let f_1,f_2, ... be the Fibonacci sequence with f_1 = f_2 = 1. Now consider a sequence of two dimensional vectors
f_1 , f_2 , f_3 , ...
f_2 , f_3 , f_4 , ...
Observe that the next element v_{n+1} in the vector sequence is M.v_{n} where M is a 2x2 matrix given by
M = [0 1]
[1 1]
due to f_{n+1} = f_{n+1} and f_{n+2} = f_{n} + f_{n+1}
M is diagonalizable over complex numbers (in fact diagonalizable over the reals as well, but this is not usually the case). There are two distinct eigenvectors of M given by
1 1
x_1 x_2
where x_1 = (1+sqrt(5))/2 and x_2 = (1-sqrt(5))/2 are the distinct solutions to the polynomial equation x*x-x-1 = 0. The corresponding eigenvalues are x_1 and x_2. Think of M as a linear transformation and change your basis to see that it is equivalent to
D = [x_1 0]
[0 x_2]
In order to find f_n find v_n and look at the first coordinate. To find v_n apply M n-1 times to v_1. But applying M n-1 times is easy, just think of it as D. Then using linearity one can find
f_n = 1/sqrt(5)*(x_1^n-x_2^n)
Since the norm of x_2 is smaller than 1, the corresponding term vanishes as n tends to infinity; therefore, obtaining the greatest integer smaller than (x_1^n)/sqrt(5) is enough to find the answer exactly. By making use of the trick of repeatedly squaring, this can be done using only O(log_2(n)) multiplication (and addition) operations. Memory complexity is even more impressive because it can be implemented in a way that you always need to hold at most 1 number in memory whose value is smaller than the answer. However, since this number is not a natural number, memory complexity here changes depending on whether if you use fixed bits to represent each number (hence do calculations with error)(O(1) memory complexity this case) or use a better model like Turing machines, in which case some more analysis is needed.