Does anybody know how to construct a binary search tree using a genetic algorithm and if it's even possible? I wanted to try it as an assignment but I'm not sure where to begin. Any advice would be greatly appreciated.
Related
This is a question on nomenclature. In complexity theory of algorithms, there is a name for a test case constructed specifically to make the algorithm fail. I had that word in my head, but can't recall it for the life of me now. I'm hoping someone here can help. What is the word for an input that is incredibly unlikely to occur in the real world but someone who knows the algorithm can construct to make it fail or perform badly?
You may be thinking of an adversary:
The idea is that an all-powerful malicious adversary pretends to choose an input for the algorithm. When the algorithm wants looks at a bit, the adversary sets that bit to whatever value will make the algorithm do the most work.
The word I was looking for was "pathological case". When reading about the Cauchy distribution (https://en.wikipedia.org/wiki/Cauchy_distribution), I chanced upon it again seeing it described as a "pathological distribution".
I am trying to solve some problems that can be mapped in convex optimisation problem.
In particular is for analysis of quantum state tomography data.
In Matlab there are some tools to help you do this, like SeDuMi or CVX
http://sedumi.ie.lehigh.edu
http://cvxr.com/cvx/
But I could not find anything similar in Mathematica, on the web or in the forums.
Does anybody know if there is an easy way of implementing this kind of algorithm in Mathematica?
I would like to avoid to be forced to switch to Matlab to solve this problem. Nothing against it, but I have most of the programming for this state tomography developed in Mathematica.
Thank you very much.
I had also some troubles with Mathematica in
optimization, exactly on convex problems.
I suggest you export to CVX, which will require
some work because it wants the problem in matrix notation.
Otherwise, to remain with the algebraic formulation,
you could try with Maple, which has, as far
as I can tell, better optimizers than Mathematica.
(check the doc to have an idea)
Can anyone explain the Robson algorithm for tree traversal? I'm having trouble understanding what the steps of the algorithm are.
Do you happen to have an assignment due on 5/12 that you are trying to complete?
Robson tree traversals is just a way to traverse trees using a bunch of pointers. The Steps Outlined Here do a very good job at outlining the procedures.
I would recommend creating a tree with pen and paper and following the steps. It's the easiest way to wrap your head around all of pointers, and what they are doing.
While I am comfortable with optimization problems in Python (and 'R' for that matter) I am curious to know if it can be done in WEKA.
I have X,Y coordinates of several routes and I need to optimize the best overall solution.
Any help would be appreciated...
Weka is a machine learning, not optimization package. While you could try to predict the optimal solution using the machine learning algorithms in Weka, there's nothing to check that a prediction is the optimal solution or even a solution at all.
Definitely sounds to me like you're trying to use the wrong tool for the job.
I am a university student and trying to write a program to solve 1-D Schrodinger's equation with some kinda of potential equations.
I am not a CS major so really have no clue to start.
I did research online but didn't find thing that is suitable for entry level :( The only thing I understand now is that I probably need to use some algorithm solving differential equations
Could anyone give me some suggestions or references on how I could start? For example, how to transform the physical problem into computer science program and what kinda algorithm I should look for?
I am interested in computational so trying on this HARD problem ;)
Thank you all!
go to www.google.com
search for :how can i solve Schrodinger in matlab
go to here
Return stackoverflow