Determining the square root through successive approximation is implemented using the following algorithm:
Begin by guessing that the square root is x / 2. Call that guess g.
The actual square root must lie between g and x/g. At each step in the successive approximation, generate a new guess by averaging g and x/g.
Repeat step 2 until the values of g and x/g are as close together as the precision of the hardware allows. In Java, the best way to check for this condition is to test whether the average is equal to either of the values used to generate it.
What really confuses me is the last statement of step 3. I interpreted it as follows:
private double sqrt(double x) {
double g = x / 2;
while(true) {
double average = (g + x/g) / 2;
if(average == g || average == x/g) break;
g = average;
}
return g;
}
This seems to just cause an infinite loop. I am following the algorithm exactly, if the average equals either g or x/g (the two values used to generate it) then we have our answer ?
Why would anyone ever use that approach, when they could simply use the formulas for (2n^2) = 4n^2 and (n + 1)^2 = n^2 + 2n + 1, to populate each bit in the mantissa, and divide the exponent by two, multiplying the mantissa by two iff the the mod of the exponent with two equals 1?
To check if g and x/g are as close as the HW allow, look at the relative difference and compare
it with the epsilon for your floating point format. If it is within a small integer multiple of epsilon, you are OK.
Relative difference of x and y, see https://en.wikipedia.org/wiki/Relative_change_and_difference
The epsilon for 32-bit IEEE floats is about 1.0e-7, as in one of the other answers here, but that answer used the absolute rather than the relative difference.
In practice, that means something like:
Math.abs(g-x/g)/Math.max(Math.abs(g),Math.abs(x/g)) < 3.0e-7
Never compare floating point values for equality. The result is not reliable.
Use a epsilon like so:
if(Math.abs(average-g) < 1e-7 || Math.abs(average-x/g) < 1e-7)
You can change the epsilon value to be whatever you need. Probably best is something related to the original x.
Suppose i have an algorithm by which i can compute an infinitely precise floating point number (depending from a parameter N) lets say in pseudocode:
arbitrary_precision_float f = computeValue(n); //it could be a function which compute a specific value, like PI for instance.
I guess i can implement computeValue(int) with the library mpf of the gnump library for example...
Anyway how can i split such number in sums of floating point number where each number has L Mantissa digits?
//example
f = x1 + x2 + ... + xn;
/*
for i = 1:n
xi = 2^ei * Mi
Mi has exactly p digits.
*/
I don't know if i'm clear but i'm looking for something "simple".
You can use a very simple algorithm. Assume without loss of generality that the exponent of your original number is zero; if it's not, then you just add that exponent to all the exponents of the answer.
Split your number f into groups of L digits and treat each group as a separate xi. Any such group can be represented in the form you need: the mantissa will be exactly that group, and the exponent will be negated start position of the group in the original number (that is, i*L, where i is the group number).
If any of the resulting xis starts from zero, you just shift its mantissa correcting the exponent correspondingly.
For example, for L=4
f = 10010011100
1001
0011
100
-> x1=1.001 *2^0
x2=0.011 *2^{-4} = 1.1*2^{-6}
x3=1.00 *2^{-8}
Another question arises if you want to minimize the amount of numbers you get. In the example above, two numbers are sufficient: 1.001*2^0+1.11*2^{-6}. This is a separate question, and in fact is a simple problem for dynamic programming.
I am reading a book "kernel methods for pattern analysis". For the least square approximation, it is to minimise the sum of the square of the discrepancies:
e=y-Xw
Therefore it is to minimize
L(w,S)=(y-Xw)'(y-Xw)
Leading to
$$ w=(X'X)^-1 X'y $$
I understand until now.
But how does it leads to this? What is a exactly? Is it constant?
The same way you would solve for the minima (or maxima) of a quadratic function in only one variable: By solving for the zero in the first derivative:
diff((y-Xw)' (y-Xw), w) = 0
(only that this "0" is a row vector with as many elements as w.)
after performing the differentiation we get the following. (note that ' is the transpose, not a differentiation operator.)
-2y'X + 2w'X'X = 0
we transpose the whole expression (so 0 is a column vector) and divide by two:
-X'y + X'Xw = 0
and finally solve for w:
w = (X'X)^-1 X'y
Regarding your second question: The alpha is simply the whole expression X(X'X)^-2X'y. The point is that w can be written as the dot product of X' and some vector, which means that w is a linear combination of the columns of X' (rows of X).
this is my first question on this website. I'm looking at a Matlab problem, and don't seem to know how to do it. Before I type the question, I want to make it clear that I'm looking for an UNDERSTANDING, NOT an ANSWER. Although, I must admit, I won't be angry if an answer is posted. But more importantly, I need to understand this.
"The matrix factorization LU = PA can be used to compute the determinant of A. We have
det(L)det(U) = det(P)det(A).
Because L is triangular with ones on the diagonal, det(L) = 1. Because U is
triangular, det(U) = u 11 u 22 · · · u nn . Because P is a permutation, det(P) =
+1 if the number of interchanges is even and −1 if it is odd. So
det(A) = ±u 11 u 22 · · · u nn .
Modify the lutx function so that it returns four outputs.
function [L,U,p,sig] = lutx(A)
%LU Triangular factorization
% [L,U,p,sig] = lutx(A) computes a unit lower triangular
% matrix L, an upper triangular matrix U, a permutation
% vector p, and a scalar sig, so that L*U = A(p,:) and
% sig = +1 or -1 if p is an even or odd permutation.
Write a function mydet(A) that uses your modified lutx to compute the
determinant of A. In Matlab, the product u 11 u 22 · · · u nn can be computed
by the expression prod(diag(U))."`
The lutx code can be found here:
I'm having difficulty understanding the concept of the problem, and also the code that needs to be written. Any help would be very appreciated. Thank you.
As you mentioned in your problem in the following equation:
det(L)det(U) = det(P)det(A)
actually the lutx function decompose the input matrix and returns the decomposed elements. It means if you give it the A matrix, it will calculate the L,U,p. you can check the source code.
actually in your problem, three out of four elements are 'known', so you can use the lutx function to find the det(A).
because :
det(A) = det(L)det(U) / det(P);
so what you can do is this:
[L,U,p,sig] = lutx(A); % here I am using the modified version of lutx that you mentioned
DetA = 1 * prod(diag(U)) * sig;
because, det(L) = 1 (I mention it in the previous line of code just for underestanding), and det(U) = prod(diag(U)), and sig gives the sign.
finally you can compare your result with matlab function: det(A).
The exercise appears to be mainly to compute "sig", which lutx currently doesn't return. As a hint, you must compute
delta_p = (1:length(p))-p;
and check whether delta_p has an even or odd number of non-zero elements. That will determine the sign of sig.
Suppose I have a real number. I want to approximate it with something of the form a+sqrt(b) for integers a and b. But I don't know the values of a and b. Of course I would prefer to get a good approximation with small values of a and b. Let's leave it undefined for now what is meant by "good" and "small". Any sensible definitions of those terms will do.
Is there a sane way to find them? Something like the continued fraction algorithm for finding fractional approximations of decimals. For more on the fractions problem, see here.
EDIT: To clarify, it is an arbitrary real number. All I have are a bunch of its digits. So depending on how good of an approximation we want, a and b might or might not exist. Brute force is naturally not a particularly good algorithm. The best I can think of would be to start adding integers to my real, squaring the result, and seeing if I come close to an integer. Pretty much brute force, and not a particularly good algorithm. But if nothing better exists, that would itself be interesting to know.
EDIT: Obviously b has to be zero or positive. But a could be any integer.
No need for continued fractions; just calculate the square-root of all "small" values of b (up to whatever value you feel is still "small" enough), remove everything before the decimal point, and sort/store them all (along with the b that generated it).
Then when you need to approximate a real number, find the radical whose decimal-portion is closet to the real number's decimal-portion. This gives you b - choosing the correct a is then a simple matter of subtraction.
This is actually more of a math problem than a computer problem, but to answer the question I think you are right that you can use continued fractions. What you do is first represent the target number as a continued fraction. For example, if you want to approximate pi (3.14159265) then the CF is:
3: 7, 15, 1, 288, 1, 2, 1, 3, 1, 7, 4 ...
The next step is create a table of CFs for square roots, then you compare the values in the table to the fractional part of the target value (here: 7, 15, 1, 288, 1, 2, 1, 3, 1, 7, 4...). For example, let's say your table had square roots for 1-99 only. Then you would find the closest match would be sqrt(51) which has a CF of 7: 7,14 repeating. The 7,14 is the closest to pi's 7,15. Thus your answer would be:
sqrt(51)-4
As the closest approximation given a b < 100 which is off by 0.00016. If you allow larger b's then you could get a better approximation.
The advantage of using CFs is that it is faster than working in, say, doubles or using floating point. For example, in the above case you only have to compare two integers (7 and 15), and you can also use indexing to make finding the closest entry in the table very fast.
This can be done using mixed integer quadratic programming very efficiently (though there are no run-time guarantees as MIQP is NP-complete.)
Define:
d := the real number you wish to approximate
b, a := two integers such that a + sqrt(b) is as "close" to d as possible
r := (d - a)^2 - b, is the residual of the approximation
The goal is to minimize r. Setup your quadratic program as:
x := [ s b t ]
D := | 1 0 0 |
| 0 0 0 |
| 0 0 0 |
c := [0 -1 0]^T
with the constraint that s - t = f (where f is the fractional part of d)
and b,t are integers (s is not)
This is a convex (therefore optimally solvable) mixed integer quadratic program since D is positive semi-definite.
Once s,b,t are computed, simply derive the answer using b=b, s=d-a and t can be ignored.
Your problem may be NP-complete, it would be interesting to prove if so.
Some of the previous answers use methods that are of time or space complexity O(n), where n is the largest “small number” that will be accepted. By contrast, the following method is O(sqrt(n)) in time, and O(1) in space.
Suppose that positive real number r = x + y, where x=floor(r) and 0 ≤ y < 1. We want to approximate r by a number of the form a + √b. If x+y ≈ a+√b then x+y-a ≈ √b, so √b ≈ h+y for some integer offset h, and b ≈ (h+y)^2. To make b an integer, we want to minimize the fractional part of (h+y)^2 over all eligible h. There are at most √n eligible values of h. See following python code and sample output.
import math, random
def findb(y, rhi):
bestb = loerror = 1;
for r in range(2,rhi):
v = (r+y)**2
u = round(v)
err = abs(v-u)
if round(math.sqrt(u))**2 == u: continue
if err < loerror:
bestb, loerror = u, err
return bestb
#random.seed(123456) # set a seed if testing repetitively
f = [math.pi-3] + sorted([random.random() for i in range(24)])
print (' frac sqrt(b) error b')
for frac in f:
b = findb(frac, 12)
r = math.sqrt(b)
t = math.modf(r)[0] # Get fractional part of sqrt(b)
print ('{:9.5f} {:9.5f} {:11.7f} {:5.0f}'.format(frac, r, t-frac, b))
(Note 1: This code is in demo form; the parameters to findb() are y, the fractional part of r, and rhi, the square root of the largest small number. You may wish to change usage of parameters. Note 2: The
if round(math.sqrt(u))**2 == u: continue
line of code prevents findb() from returning perfect-square values of b, except for the value b=1, because no perfect square can improve upon the accuracy offered by b=1.)
Sample output follows. About a dozen lines have been elided in the middle. The first output line shows that this procedure yields b=51 to represent the fractional part of pi, which is the same value reported in some other answers.
frac sqrt(b) error b
0.14159 7.14143 -0.0001642 51
0.11975 4.12311 0.0033593 17
0.12230 4.12311 0.0008085 17
0.22150 9.21954 -0.0019586 85
0.22681 11.22497 -0.0018377 126
0.25946 2.23607 -0.0233893 5
0.30024 5.29150 -0.0087362 28
0.36772 8.36660 -0.0011170 70
0.42452 8.42615 0.0016309 71
...
0.93086 6.92820 -0.0026609 48
0.94677 8.94427 -0.0024960 80
0.96549 11.95826 -0.0072333 143
0.97693 11.95826 -0.0186723 143
With the following code added at the end of the program, the output shown below also appears. This shows closer approximations for the fractional part of pi.
frac, rhi = math.pi-3, 16
print (' frac sqrt(b) error b bMax')
while rhi < 1000:
b = findb(frac, rhi)
r = math.sqrt(b)
t = math.modf(r)[0] # Get fractional part of sqrt(b)
print ('{:11.7f} {:11.7f} {:13.9f} {:7.0f} {:7.0f}'.format(frac, r, t-frac, b,rhi**2))
rhi = 3*rhi/2
frac sqrt(b) error b bMax
0.1415927 7.1414284 -0.000164225 51 256
0.1415927 7.1414284 -0.000164225 51 576
0.1415927 7.1414284 -0.000164225 51 1296
0.1415927 7.1414284 -0.000164225 51 2916
0.1415927 7.1414284 -0.000164225 51 6561
0.1415927 120.1415831 -0.000009511 14434 14641
0.1415927 120.1415831 -0.000009511 14434 32761
0.1415927 233.1415879 -0.000004772 54355 73441
0.1415927 346.1415895 -0.000003127 119814 164836
0.1415927 572.1415909 -0.000001786 327346 370881
0.1415927 911.1415916 -0.000001023 830179 833569
I do not know if there is any kind of standard algorithm for this kind of problem, but it does intrigue me, so here is my attempt at developing an algorithm that finds the needed approximation.
Call the real number in question r. Then, first I assume that a can be negative, in that case we can reduce the problem and now only have to find a b such that the decimal part of sqrt(b) is a good approximation of the decimal part of r. Let us now write r as r = x.y with x being the integer and y the decimal part.
Now:
b = r^2
= (x.y)^2
= (x + .y)^2
= x^2 + 2 * x * .y + .y^2
= 2 * x * .y + .y^2 (mod 1)
We now only have to find an x such that 0 = .y^2 + 2 * x * .y (mod 1) (approximately).
Filling that x into the formulas above we get b and can then calculate a as a = r - b. (All of these calculations have to be carefully rounded of course.)
Now, for the time being I am not sure if there is a way to find this x without brute forcing it. But even then, one can simple use a simple loop to find an x good enough.
I am thinking of something like this(semi pseudo code):
max_diff_low = 0.01 // arbitrary accuracy
max_diff_high = 1 - max_diff_low
y = r % 1
v = y^2
addend = 2 * y
x = 0
while (v < max_diff_high && v > max_diff_low)
x++;
v = (v + addend) % 1
c = (x + y) ^ 2
b = round(c)
a = round(r - c)
Now, I think this algorithm is fairly efficient, while even allowing you to specify the wished accuracy of the approximation. One thing that could be done that would turn it into an O(1) algorithm is calculating all the x and putting them into a lookup table. If one only cares about the first three decimal digits of r(for example), the lookup table would only have 1000 values, which is only 4kb of memory(assuming that 32bit integers are used).
Hope this is helpful at all. If anyone finds anything wrong with the algorithm, please let me know in a comment and I will fix it.
EDIT:
Upon reflection I retract my claim of efficiency. There is in fact as far as I can tell no guarantee that the algorithm as outlined above will ever terminate, and even if it does, it might take a long time to find a very large x that solves the equation adequately.
One could maybe keep track of the best x found so far and relax the accuracy bounds over time to make sure the algorithm terminates quickly, at the possible cost of accuracy.
These problems are of course non-existent, if one simply pre-calculates a lookup table.