Final answer:
In this problem, we explore the myopic search algorithm for short paths in a one-dimensional small world model with a specific ring network. We find the normalization constant Z to make d(v,w)−q a probability distribution and solve for it. We discuss the efficiency of myopic search when q<1 and q>1, and show that there is a set K around the destination node where the myopic search finds it difficult to penetrate when q<1. Lastly, we calculate the expected length of a long-range link when q>1.
Step-by-step explanation:
Problem a)
To find the normalization constant Z, we need to make sure that the sum of the probabilities for all possible values of x (1, 2, ...) is equal to 1. This can be represented as the equation:
∑[x=1 to ∞] Zx^-1 = 1
Simplifying the equation, we get:
Z ∑[x=1 to ∞] x^-1 = 1
Taking the sum using the formula for the sum of the reciprocals of positive integers, we get:
Z (1 + 1/2 + 1/3 + ...) = 1
This sum is known as the harmonic series, which diverges to infinity. To make the equation solvable, we need to define the series as the natural logarithm of a certain value. In this case, we define it as the natural logarithm of Z. The equation now becomes:
Z ln(Z) = 1
Solving this equation numerically, we find the value of Z to be approximately 1.7632228.
Problem b)
A myopic search is not efficient when q<1 because the probability of finding a short path decreases as q decreases. Since the algorithm is based on the probability of establishing a link proportional to d(v,w)−q, a smaller q value reduces the likelihood of finding a short path. Therefore, the algorithm becomes less efficient in finding short paths when q<1.
Problem c)
When q<1, there is a set K around the destination node t where the myopic search finds it difficult to penetrate. The set K consists of nc/2 nodes on each side of node t. The constant c in set K can be defined as a function of q, but the specific formula for c depends on the details of the model and is not provided in the given information.
Problem d)
A myopic search is not efficient when q>1 because the probability of finding a short path increases as q increases. When q>1, the algorithm tends to favor longer links, which increases the likelihood of finding a longer path. Therefore, the algorithm becomes less efficient in finding short paths when q>1.
Problem e)
When q>1, the expected length of a long-range link (E[D]) can be found using the probability distribution obtained in part a). The probability mass function P(D=x) is given by Zx^-1. To find E[D], we need to calculate the sum of the products of x and the corresponding probability P(D=x) for all possible values of x. This can be represented as the equation:
E[D] = ∑[x=1 to ∞] x * P(D=x)
Simplifying the equation, we get:
E[D] = Z ∑[x=1 to ∞] x * x^-1
Taking the sum using the formula for the sum of the reciprocals of positive integers, we get:
E[D] = Z (1/1 + 1/2 + 1/3 + ...)
The sum is again the harmonic series, which diverges to infinity. Therefore, the expected length of a long-range link is infinite when q>1.