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
Related
F(x1) > a;
F(x2) < b;
∀t, F'(x) >= 0 (derivative) ;
F(x) = ∑ ci*x^i; (i∈[0,n] ; c is a constant)
Your question is quite ambiguous, and stack-overflow works the best if you show what you tried and what problems you ran into.
Nevertheless, here's how one can code your problem for a specific function F = 2x^3 + 3x + 4, using the Python interface to z3:
from z3 import *
# Represent F as a function. Here we have 2x^3 + 3x + 4
def F(x):
return 2*x*x*x + 3*x + 4
# Similarly, derivative of F: 6x^2 + 3
def dF(x):
return 6*x*x + 3
x1, x2, a, b = Ints('x1 x2 a b')
s = Solver()
s.add(F(x1) > a)
s.add(F(x2) < b)
t = Int('t')
s.add(ForAll([t], dF(t) >= 0))
r = s.check()
if r == sat:
print s.model()
else:
print ("Solver said: %s" % r)
Note that I translated your ∀t, F'(x) >= 0 condition as ∀t. F'(t) >= 0. I assume you had a typo there in the bound variable.
When I run this, I get:
[x1 = 0, x2 = 0, b = 5, a = 3]
This method can be generalized to arbitrary polynomials with constant coefficients in the obvious way, but that's mostly about programming and not z3. (Note that doing so in SMTLib is much harder. This is where the facilities of host languages like Python and others come into play.)
Note that this problem is essentially non-linear. (Variables are being multiplied with variables.) So, SMT solvers may not be the best choice here, as they don't deal all that well with non-linear operations. But you can deal with those problems as they arise later on. Hope this gets you started!
The var c return 3 but 10/7=1.4285, the rest is 0.4285, operator % has a bug?
void main() {
var a = 10;
var b = 7;
var c;
c = a % b;
print(c);
}
From the documentation of the % operator on num in Dart:
Euclidean modulo operator.
Returns the remainder of the Euclidean division. The Euclidean division of two integers a and b yields two integers q and r such that a == b * q + r and 0 <= r < b.abs().
The Euclidean division is only defined for integers, but can be easily extended to work with doubles. In that case r may have a non-integer value, but it still verifies 0 <= r < |b|.
The sign of the returned value r is always positive.
See remainder for the remainder of the truncating division.
https://api.dart.dev/stable/2.8.4/dart-core/num/operator_modulo.html
The '%' operator returns the remainder left after dividing two numbers. It does not return the decimal part. For example:
10 / 7
1
______
7 ) 10
- 7
______
3
So it returns 3 which is what remains after dividing 10 by 7 without any decimals.
10 / 7 = 1 3/7
What you want to do can be accomplished like this:
var floatNumber = 12.5523;
var x = floatNumber - floatNumber.truncate();
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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've been working on a project that renders a Mandelbrot fractal. For those of you who know, it is generated by iterating through the following function where c is the point on a complex plane:
function f(c, z) return z^2 + c end
Iterating through that function produces the following fractal (ignore the color):
When you change the function to this, (z raised to the third power)
function f(c, z) return z^3 + c end
the fractal should render like so (again, the color doesn't matter):
(source: uoguelph.ca)
However, when I raised z to the power of 3, I got an image extremely similar as to when you raise z to the power of 2. How can I make the fractal render correctly? This is the code where the iterations are done: (the variables real and imaginary simply scale the screen from -2 to 2)
--loop through each pixel, col = column, row = row
local real = (col - zoomCol) * 4 / width
local imaginary = (row - zoomRow) * 4 / width
local z, c, iter = 0, 0, 0
while math.sqrt(z^2 + c^2) <= 2 and iter < maxIter do
local zNew = z^2 - c^2 + real
c = 2*z*c + imaginary
z = zNew
iter = iter + 1
end
So I recently decided to remake a Mandelbrot fractal generator, and it was MUCH more successful than my attempt last time, as my programming skills have increased with practice.
I decided to generalize the mandelbrot function using recursion for anyone who wants it. So, for example, you can do f(z, c) z^2 + c or f(z, c) z^3 + c
Here it is for anyone that may need it:
function raise(r, i, cr, ci, pow)
if pow == 1 then
return r + cr, i + ci
end
return raise(r*r-i*i, 2*r*i, cr, ci, pow - 1)
end
and it's used like this:
r, i = raise(r, i, CONSTANT_REAL_PART, CONSTANT_IMAG_PART, POWER)
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