The document summarizes the solution to calculating the average leftover length of dental floss after cutting segments from two rolls until one runs out. It uses a binomial distribution to calculate the probability that the process ends with a given leftover length on one roll. Approximating the binomial coefficient using a Gaussian function allows simplifying the expression for the average leftover length to (2/√π)√Ld, where L is the initial total floss length and d is the segment length. So the average leftover amount is proportional to the geometric mean of the total length and segment length.
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The set covering problem and the minimum cost assignment problem (respectively known as unate and binate covering problem) are well-known NP-complete problems.We investigate the complexity and approximation ratio of two lower bound computation algorithms from both a theoretical and practical point of view, and produce an algorithm that is 2-3 order of magnitude faster.
The set covering problem and the minimum cost assignment problem (respectively known as unate and binate covering problem) are well-known NP-complete problems.We investigate the complexity and approximation ratio of two lower bound computation algorithms from both a theoretical and practical point of view, and produce an algorithm that is 2-3 order of magnitude faster.
Presented by Matthew Hill, 25th February 2015, at the Midlands Share and Learn event, part of NCVO's Volunteering in Care Homes Project:
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The Gaussian or normal distribution is one of the most widely used in statistics. Estimating its parameters using
Bayesian inference and conjugate priors is also widely used. The use of conjugate priors allows all the results to be
derived in closed form. Unfortunately, different books use different conventions on how to parameterize the various
distributions (e.g., put the prior on the precision or the variance, use an inverse gamma or inverse chi-squared, etc),
which can be very confusing for the student. In this report, we summarize all of the most commonly used forms. We
provide detailed derivations for some of these results; the rest can be obtained by simple reparameterization. See the
appendix for the definition the distributions that are used.
A Proof of Hypothesis Riemann and it is proven that apply only for equation ζ(z)=0.Also here it turns out that it does not apply in General Case.(DOI:10.13140/RG.2.2.10888.34563/5)
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Three Different Algorithms for GeneratingUniformly Distribut.docxherthalearmont
Three Different Algorithms for Generating
Uniformly Distributed Random Points on the
N-Sphere
Jan Poland
Oct 24, 2000
Abstract
We present and compare three different approaches to generate
random points on the N -sphere: A simple Monte Carlo algorithm, a
coordinate-by-coordinate strategy and a method based on the rotation
invariance the normal distribution. The latter algorithm is the fastest.
1 Introduction
Computer scientists sometimes encounter the problem of generating random
points on the N -dimensional unit sphere SN = {x ∈ RN +1 : ‖x‖2 = 1}.
In most cases, the random points should be distributed uniformly. For this
task, there exists a nice little algorithm already mentioned in Knuth ([3]),
that is based on the fact that the N -dimensional normal distribution is in-
variant under rotation. This algorithm is indubitably the most efficient, in
particular in high dimensions. But if a special non-uniform distribution is re-
quested, it may be profitable to know other approaches such as a coordinate-
by-coordinate strategy or a simple rejection method.
When N is small, each algorithm is sufficiently fast, and the problem
is not very interesting. For the 1-sphere, i.e. the unit circle in R2, U =
(cos(α), sin(α)) with α uniformly distributed in [0, 2π] does the job. Here, we
will concentrate on the high dimensional case. Applications are for example
in the initialization of a counterpropagation neural network (see [7]) or a
variant of generalized continuous recombination in an evolution strategy (cf.
[4]).
Other discussions of our problem can be found in [6] or [5].
2 A Monte-Carlo algorithm
An immediate algorithm is the following. Take a random point in [−1, 1]N +1
and project it onto the unit sphere . This results in a non-uniform distri-
bution, therefore one extra step is necessary. Generate a random point in
X ∈ [−1, 1]N +1 and reject it if it is outside the unit ball, i.e. ‖X‖ > 1. If
not, U = ‖X‖−1 ·X gives a random point on SN (neglecting the unlikely case
that X = 0), and produces a uniform distribution on the sphere.
This algorithm is often referred to as the rejection method (see [6]). The
major drawback of the algorithm is it’s expected running time: It grows
worse than exponentially in N . This is due to the fact that the volume of
the N -dimesional unit ball
V (N ) =
π
N
2
Γ(1 + N
2
)
more than exponentially decreases as N → ∞, where Γ(·) is the gamma
function
Γ(x) =
∫ ∞
0
e−t · tx−1 dt.
3 The coordinate approach
In high dimensions, the distribution of each single coordinate of a uniformly
distributed random point U on the N -sphere becomes quite complicated.
Suppose we are given a random vector U uniformly distributed on SN .
Consider the projection U1 of U to its first coordinate, lets say the x-coordinate.
Then the density function f1(x) of U1 is a bounded function from [−1, 1] into
R+. We now want to state the f1 explicitely.
The following computations will need the incomplete beta function
Bx(a, b) ...
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1. Solution
Week 62 (11/17/03)
Leftover dental floss
Let (x, y) denote the occurrence where x segments have been cut off the left roll,
and y segments have been cut off the right roll. In solving this problem, we’ll need
to first calculate the probability that the process ends at (N, n), which leaves a
length (N − n)d on the right roll. For this to happen, the process must first get to
(N −1, n), and then the left roll must be chosen for the last segment. The probability
of reaching the point (N − 1, n) is
PN−1,n =
1
2N−1+n
N − 1 + n
n
, (1)
because the binomial coefficient gives the number of different ways the N − 1 + n
choices of roll (each of which occurs with probability 1/2) can to end up at the point
(N − 1, n). The probability of then choosing the left roll for the next piece is 1/2.
Therefore, the probability of ending the process at (N, n) is
Pend
N,n =
1
2N+n
N − 1 + n
n
. (2)
The leftover length in this case is (N −n)d, so we see that the average leftover length
at the end of the process is
= 2d
N−1
n=0
(N − n)Pend
N,n
= 2d
N−1
n=0
N − n
2N+n
N − 1 + n
n
, (3)
where the factor of 2 out front comes from the fact that either roll may be the one
that runs out.
In order to simplify this result, we will use the standard result that for large N,
a binomial coefficient can be approximated by a Gaussian function. From Problem
of the Week 58, we have, for large N and x N,
2M
M − x
≈
22M
√
πM
e−x2/M
. (4)
To make use of this, we’ll first need to rewrite eq. (3) as
= 2d
N−1
n=0
N − n
2N+n
N
N + n
N + n
n
= 2d
N
z=1
z
22N−z
N
2N − z
2N − z
N − z
(with z ≡ N − n)
= 2d
N
z=1
z
22N−z
N
2N − z
2(N − z/2)
(N − z/2) − z/2
. (5)
1
2. Using eq. (4) to rewrite the binomial coefficient gives (with M ≡ N − z/2, and
x ≡ z/2)
≈ 2d
N
z=1
Nz
2N − z
e−z2/4(N−z/2)
π(N − z/2)
≈
d
√
πN
N
z=0
ze−z2/(4N)
≈
d
√
πN
∞
0
ze−z2/(4N)
dz
= 2d
N
π
. (6)
In obtaining the second line above, we have kept only the terms of leading order
in N. The exponential factor guarantees that only z values up to order
√
N will
contribute. Hence, z is negligible when added to N.
Without doing all the calculations, it’s a good bet that the answer should go like√
N d, but it takes some effort to show that the exact coefficient is 2/
√
π.
In terms of the initial length of floss, L ≡ Nd, the average leftover amount can
be written as ≈ (2/
√
π)
√
Ld. So we see that is proportional to the geometric
mean of L and d.
2