thanks in advance for your help in figuring this out. I'm taking an algorithms class and I'm stuck on something. According to the professor, the following holds true where C(1)=1 and n is a power of 2:
C(n) = 2 * C(n/2) + n resolves to C(n) = n * lg(n) + n
C(n) = 2 * C(n/2) + lg(n) resolves to C(n) = 3 * n - lg(n) - 2
The first one I completely grok. As I understand the form, what's stated is that C(n) resolves to two sub-problems, each of which requires n/2 work to solve, and an additional n amount of work to split and merge everything. As such, for every division of the problem, the constant 2 is increased by a factor of ^k (where k is the number of splits), the 2 in n/2 is also increased by a factor of ^k for much the same reason, and the last n is multiplied by a factor of k because each split creates a multiple of k extra work.
My confusion stems from the second relation. Given that the first and second relations are almost identical, why isn't the result of the second something like nlgn+(lgn^2)?
The general result is the Master Theorem
But in this specific case, you can work out the math for a power of 2:
C(2^k)
= 2 * C(2^(k-1)) + lg(2^k)
= 4 * C(2^(k-2)) + lg(2^k) + 2 * lg(2^(k-1))
= ... repeat ...
= 2^k * C(1) + sum (from i=1 to k) 2^(k-i) * lg 2^i
= 2^k + lg(2) * sum (from i=1 to k) 2^(i) * i
= 2^k - 2 + 2^k+1 - k
= 3 * 2^k - k - 2
= 3 * n - lg(n) - 2
Related
I am creating a rails application which is like a game. So it has points and levels. For example: to become level one the user has to get atleast 100 points and again for level two the user has to reach level 2 the user has to collect 200 points. The level difference changes after every 10 levels i.e., The difference between each level changes after 10 levels always. By that I mean the difference in points between level one and two is 100 and the difference in points in level 11 and 12 is 150 and so on. There is no upper bound for levels.
Now my question is let's say a user's total points is 3150 and just got updated to 3155. What's the optimal solution to find the current level and update it if needed?
I can get a solution using while loops and again looping inside it which will give a result in O(n^2). I need something better.
I think this code works but I'm not sure if this is the best way to go about it
def get_level(points)
diff = 100
sum = 0
level = -1
current_level = 0
while level.negative?
10.times do |i|
current_level += 1
sum += diff
if points > sum
next
elsif points <= sum
level = current_level
break
end
end
diff += 50
end
puts level
end
I wrote a get_points function (it should not be difficult). Then based on it get_level function in which it was necessary to solve the quadratic equation to find high value, and then calc low.
If you have any questions, let me know.
Check output here.
#!/usr/bin/env python3
import math
def get_points(level):
high = (level + 1) // 10
low = (level + 1) % 10
high_point = 250 * high * high + 750 * high # (3 + high) * high // 2 * 500
low_point = (100 + 50 * high) * low
return low_point + high_point
def get_level(points):
# quadratic equation
a = 250
b = 750
c = -points
d = b * b - 4 * a * c
x = (-b + math.sqrt(d)) / (2 * a)
high = int(x)
remainder = points - (250 * high * high + 750 * high)
low = remainder // (100 + 50 * high)
level = high * 10 + low
return level
def main():
for l in range(0, 40):
print(f'{l:3d} {get_points(l - 1):5d}..{get_points(l) - 1}')
for level, (l, r) in (
(1, (100, 199)),
(2, (200, 299)),
(9, (900, 999)),
(10, (1000, 1149)),
(11, (1150, 1299)),
(19, (2350, 2499)),
(20, (2500, 2699)),
):
for p in range(l, r + 1): # for in [l, r]
assert get_level(p) == level, f'{p} {l}'
if __name__ == '__main__':
main()
Why did you set the value of a=250 and b = 750? Can you explain that to me please?
Let's write out every 10 level and the difference between points:
lvl - pnt (+delta)
10 - 1000 (+1000 = +100 * 10)
20 - 2500 (+1500 = +150 * 10)
30 - 4500 (+2000 = +200 * 10)
40 - 7000 (+2500 = +250 * 10)
Divide by 500 (10 levels * 50 difference changes) and received an arithmetic progression starting at 2:
10 - 2 (+2)
20 - 5 (+3)
30 - 9 (+4)
40 - 14 (+5)
Use arithmetic progression get points formula for level = k * 10 equal to:
sum(x for x in 2..k+1) * 500 =
(2 + k + 1) * k / 2 * 500 =
(3 + k) * k * 250 =
250 * k * k + 750 * k
Now we have points and want to find the maximum high such that point >= 250 * high^2 + 750 * high, i. e. 250 * high^2 + 750 * high - points <= 0. Value a = 250 is positive and branches of the parabola are directed up. Now we find the solution of quadratic equation 250 * high^2 + 750 * high - points = 0 and discard the real part (is high = int(x) in python script).
I was following Siraj Raval's videos on logistic regression using gradient descent :
1) Link to longer video :
https://www.youtube.com/watch?v=XdM6ER7zTLk&t=2686s
2) Link to shorter video :
https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D
In the videos he talks about using gradient descent to reduce the error for a set number of iterations so that the function converges(slope becomes zero).
He also illustrates the process via code. The following are the two main functions from the code :
def step_gradient(b_current, m_current, points, learningRate):
b_gradient = 0
m_gradient = 0
N = float(len(points))
for i in range(0, len(points)):
x = points[i, 0]
y = points[i, 1]
b_gradient += -(2/N) * (y - ((m_current * x) + b_current))
m_gradient += -(2/N) * x * (y - ((m_current * x) + b_current))
new_b = b_current - (learningRate * b_gradient)
new_m = m_current - (learningRate * m_gradient)
return [new_b, new_m]
def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations):
b = starting_b
m = starting_m
for i in range(num_iterations):
b, m = step_gradient(b, m, array(points), learning_rate)
return [b, m]
#The above functions are called below:
learning_rate = 0.0001
initial_b = 0 # initial y-intercept guess
initial_m = 0 # initial slope guess
num_iterations = 1000
[b, m] = gradient_descent_runner(points, initial_b, initial_m, learning_rate, num_iterations)
# code taken from Siraj Raval's github page
Why does the value of b & m continue to update for all the iterations? After a certain number of iterations, the function will converge, when we find the values of b & m that give slope = 0.
So why do we continue iteration after that point and continue updating b & m ?
This way, aren't we losing the 'correct' b & m values? How is learning rate helping the convergence process if we continue to update values after converging? Thus, why is there no check for convergence, and so how is this actually working?
In practice, most likely you will not reach to slope 0 exactly. Thinking of your loss function as a bowl. If your learning rate is too high, it is possible to overshoot over the lowest point of the bowl. On the contrary, if the learning rate is too low, your learning will become too slow and won't reach the lowest point of the bowl before all iterations are done.
That's why in machine learning, the learning rate is an important hyperparameter to tune.
Actually, once we reach a slope 0; b_gradient and m_gradient will become 0;
thus, for :
new_b = b_current - (learningRate * b_gradient)
new_m = m_current - (learningRate * m_gradient)
new_b and new_m will remain the old correct values; as nothing will be subtracted from them.
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Sorry I have tried a lot to solve this recurrence equation
T (n) = 3T (n / 3) + Ѳ (log3n)
with the replacement method but I can not get the required result:
1) T (n) = O (nlogn)
2) Induction
Base: for every n = 1 -> 1log1 + 1 = 1 = T (1)
Inductive step: T (k) = klogk + k for each k <n
Use k = n / 3
T (n) = 3T (n / 3) + Ѳ (log₃n)
1) T (n) = O (nlogn)
2) Induction
Base: for every n = 1 -> 1log1 + 1 = 1 = T (1)
Inductive step: T (k) = klogk + k for each k <n
Use k = n / 3
T (n) = 3T (n / 3) + Ѳ (log₃n)
= 3 [n / 3logn / 3 + n / 3] + (log₃n)
= nlogn / 3 + n + (log₃n)
= n(logn-log3) + n + (log₃n)
= nlogn-nlog3 + n + (log3n)
Firstly we can (eventually) ignore the base 3 in the Theta-notation, as it amounts to a multiplicative factor as is therefore irrelevant. Then we can try the following method:
1. Hypothesis by inspection:
If we re-substitute T into itself multiple times, we get:
What is the upper limit m? We need to assume that T(n) has a stopping condition, i.e. some value of n where it stops recursing. Assuming that it is n = 1 (it really doesn't matter, as long as it's a constant much smaller than n). Continuing (and briefly restoring the base 3):
Surprisingly the answer is not Ө(n log n).
2. Induction base case
We don't use induction to prove the final result, but the series result we deduced by inspecting the behaviour of the expansion.
For the base case n / 3 = 1, we have:
Which is consistent.
3. Induction recurrence
Again, consistent. Thus by induction the summation result is correct, and T(n) is indeed Ө(n).
4. Numerical tests:
Just in case you still cannot believe that it is Ө(n), here is a numerical test to prove the result.
Javascript code:
function T(n) {
return n <= 1 ? 0 : 3*T(floor(n/3)) + log(n);
}
Results:
n T(n)
--------------------------
10 5.598421959
100 66.33828212
1000 702.3597066
10000 6450.185742
100000 63745.45154
1000000 580674.1886
10000000 8924162.276
100000000 81068207.64
Graph:
The linear relationship is clear.
I have the following problem:
Solve the following recurrence relation, simplifying your final answer
using 'O' notation.
f(0)=3
f(1)=12
f(n)=6f(n-1)-9f(n-2)
We know this is a homogeneous 2nd order relation so we write the characteristic equation: a^2-6a+9=0 and the solutions are a1,2=3.
The problem is when I replace these values I get:
f(n)=c1*3^n+c2*3^n
and using the 2 initial relations I have:
f(0)=c1+c2=3
f(1)=3(c1+c2)=12
which gives me that there no values such that c1 and c2 such that these 2 relation are true.
Am I doing something wrong? Is the way it should be solved different when it comes to identical roots for the characteristic equation?
You can't solve it this way, because your matrix A is not diagonalizable.
However, here is what you get if you use Jordan's normal form instead:
f(n) = 3^{n-1}(3n + 9)
The Jordan matrix and the basis (with notation from wikipedia + Octave) is:
J := [3,1;0,3]
P := [3,4;1,1]
such that PJP^{-1} = A, where
A := [6,-9;1,0]
is your recurrence matrix. Furthermore, the Jordan matrix is almost as good as a diagonal matrix for computing powers:
J^n = 3^(n-1) * [3,n;0,3].
The recurrence is then:
[f(n+1); f(n)] = A^n [12,3] = PJ^nP^-1[12,3] = (<whatever>, 3^(n-1)*(3n+9)).
Here a quick numerical check (Scala, but you can take whatever you want, Octave or I whatever you like):
scala> def f(n: Int): Int = { if (n == 0) 3 else if (n == 1) 12 else (6 * f(n-1) - 9 * f(n-2)) }
f: (n: Int)Int
scala> for (i <- 0 until 20) println(f(i))
3
12
45
162
567
1944
6561
21870
72171
236196
767637
2480058
7971615
25509168
81310473
258280326
817887699
^
scala> def explicit(n: Int): Int = (Math.pow(3, n -1) * (3 * n + 9)).toInt
explicit: (n: Int)Int
scala> for (i <- 0 until 20) println(explicit(i))
3
12
45
162
567
1944
6561
21870
72171
236196
767637
2480058
7971615
25509168
81310473
258280326
817887699
I have a pseudocode which I'm trying to make a detailed analysis, analyze runtime, and asymptotic analysis:
sum = 0
i = 1
while (i ≤ n){
sum = sum + i
i = 2i
}
return sum
My assignment requires that I write the cost/runtime for every line, add these together, and find a Big-Oh notation for the runtime. My analysis looks like this for the moment:
sum = 0 1
long i = 1 1
while (i ≤ n){ log n + 1
sum = sum + i n log n
i = 2i n log n
}
return sum 1
=> 2 n log n + log n + 4 O(n log n)
is this correct ? Also: should I use n^2 on the while loop instead ?
Because of integer arithmetic, the runtime is
O(floor(ln(n))+1) = O(ln(n)).
Let's step through your pseudocode. Consider the case that n = 5.
iteration# i ln(i) n
-------------------------
1 1 0 5
2 2 1 5
3 4 2 5
By inspection we see that
iteration# = ln(i)+1
So in summary:
sum = 0 // O(1)
i = 1 // O(1)
while (i ≤ n) { // O(floor(ln(n))+1)
sum = sum + i // 1 flop + 1 mem op = O(1)
i = 2i // 1 flop + 1 mem op = O(1)
}
return sum // 1 mem op = O(1)