Suppose some fixed size data is to be transferred between machines. Let us consider two scenarios:
1 target: Data is transferred from machine A to machine B. Suppose this takes x seconds.
2 targets: Data is transferred from machine A to machine B and C. Suppose this takes y seconds.
What can be said about the relationship between x and y? I think usually y should be greater than x. Am I right? Is there any work/paper/blog that gives such an insight?
I think the relationship between x and y should depend on the topology of network connection between the machines too.
Related
I have a question regarding transforming one set of random variables to another.
Suppose that we have a sample of data collected from two competing exponential distributions, with rate a and b. We know that the random variables X follow an exponential distribution with rate (a + b).
Now suppose that we know the rate a, and we want to transform the random variables to a new set of random variables, so that the new set of random variables Y so that Y follows the exponential distribution with rate b.
Is there a way to do so? I would appreciate any answers or references.
Best regards.
We have looked into convolution of variables, but it seems not to fit since X cannot be represented as sum of two random variables.
L_mult={a^i b^ij c^j:i,j≥0}, what would be the diagram of turing machine.
Here the number of b's will be equal to the number of a's times number of c's. How can draw the turning machine of this lanague.
I'm working on implementing a model in Python. As part of this model, I have a set of agents (e.g. humans) that need to visit a set of targets (e.g. places). Each agent has its own initial location (i.e. starting point) and I can calculate the distance from each agent to each target.
What I need at this point is to allocate a first job to each agent in a way that the sum of all travel distances for agents from their starting location to their first job is minimum.
I considered greedy algorithm, but I found examples that proves order of allocation can lead to non-optimal solutions. I also looked into nearest neighbour algorithm in TSP, but all I could find was for one agent (or salesman) not multiple.
Could someone point me to any (non-exhaustive search) algorithm/approach that could be used for this purpose please? Thanks
If the number of agents = number of targets, we end up with a standard assignment problem. This can be solved in different ways:
as an LP (linear programming problem). Technically a MIP but variables are automatically integer-valued, so an LP solver suffices.
as a network problem
or using specialized algorithms.
If, say, the number of locations > number of agents, we still can use an LP/MIP:
min sum((i,j), d(i,j)*x(i,j))
sum(j, x(i,j)) = 1 for all agents i (each agent should be assigned to exactly one location)
sum(i, x(i,j)) <= 1 for all locations j (each location should be assigned to at most one agent)
x(i,j) in {0,1}
For the network approach, we would need to add some dummy nodes.
All these methods are quite fast (this is an easy model). To give you an indication: I solved a random example with 500 agents and 1000 locations as an LP and it took 0.3 seconds.
Figure shows a model of a real-time application.
The system consumes
periodically the inputs X and Y and produces the output Z. The inputs X and Y
are not updated simultaneously, but input Y is delayed 500ms compared with
input X. Also the subsystems A, B and C have different execution times (eA ≈
100ms, eB ≈ 200ms, eC ≈ 200ms).
How ever I don't understand the correct functionality of the application.
I have so far understood that the subtask A,B and C has the following period and execution time
A(0,100)
B(600, 200)
C(800,200)
But I don't understand how to sketch the table for this and how to handle the execution times and periods for the X and Y and for the subsystem A,B and C.
Any hint would be great thanks
As I read there is to kind of XYZ format:
x y z <--- in one line
and
x y z nx ny nz <--- in one line.
the function CGAL::make_surface_mesh() is extreamly slow if I use just x y z (without normals).
What is the proper way to retrieve normals from PCD-format (PCL-lib) ?
Or how to generate it manually (by my own code)?
There are several methods to estimate normals. One possibility is to insert all the points in a KdTree, then get a certain number of nearest neighbors from each point. Once you get the nearest neighbors, you can either fit a higher-order surface (quadric) to the points and compute its normal, or you can do a principal component analysis of the points and take the eigenvector associated with the smallest eigenvalue. Both methods as well as several refinements are implemented in the Point Cloud Processing package of CGAL:
http://doc.cgal.org/latest/Point_set_processing_3/index.html#Point_set_processing_3NormalEstimation
Depending on your input pointset, different methods / tunings will perform differently (it may require experimentation / parameter tuning).
Note: you may also try the different reconstruction algorithms available there:
http://doc.cgal.org/latest/Surface_reconstruction_points_3/