What would be a relatively easy algorithm to code in Java for solving a Rubik's cube. Efficiency is also important but a secondary consideration.
Perform random operations until you get the right solution. The easiest algorithm and the least efficient.
The simplest non-trivial algorithm I've found is this one:
http://www.chessandpoker.com/rubiks-cube-solution.html
It doesn't look too hard to code up. The link mentioned in Yannick M.'s answer looks good too, but the solution of 'the cross' step looks like it might be a little more complex to me.
There are a number of open source solver implementations which you might like to take a look at. Here's a Python implementation. This Java applet also includes a solver, and the source code is available. There's also a Javascript solver, also with downloadable source code.
Anthony Gatlin's answer makes an excellent point about the well-suitedness of Prolog for this task. Here's a detailed article about how to write your own Prolog solver. The heuristics it uses are particularly interesting.
Might want to check out: http://peter.stillhq.com/jasmine/rubikscubesolution.html
Has a graphical representation of an algorithm to solve a 3x3x3 Rubik's cube
I understand your question is related to Java, but on a practical note, languages like Prolog are much better suited problems like solving a Rubik's cube. I assume this is probably for a class though and you may have no leeway as to the choice of tool.
You can do it by doing BFS(Breadth-First-Search). I think the implementation is not that hard( It is one of the simplest algorithm under the category of the graph). By doing it with the data structure called queue, what you will really work on is to build a BFS tree and to find a so called shortest path from the given condition to the desire condition. The drawback of this algorithm is that it is not efficient enough( Without any modification, even to solver a 2x2x2 cubic the amount time needed is ~5 minutes). But you can always find some tricks to boost the speed.
To be honest, it is one of the homework of the course called "Introduction of Algorithm" from MIT. Here is the homework's link: http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/assignments/MIT6_006F11_ps6.pdf. They have a few libraries to help you to visualize it and to help you avoid unnecessary effort.
For your reference, you can certainly look at this java implementation. -->
Uses two phase algorithm to solve rubik's cube. And have tried this code and it works as well.
One solution is to I guess simultaneously run all possible routes. That does sound stupid but here's the logic - over 99% of possible scrambles will be solvable in under 20 moves. This means that although you cycle through huge numbers of possibilities you are still going to do it eventually. Essentially this would work by having your first step as the scrambled cube. Then you would have new cubes stored in variables for each possible move on that first cube. For each of these new cubes you do the same thing. After each possible move check if it is complete and if so then that is the solution. Here to make sure you have the solution you would need an extra bit of data on each Rubiks cube saying the moves done to get to that stage.
Related
The Goal Seek Excel function (often referred to as What-if-Analysis) is a method of solving for the desired output by changing an assumption that drives it. The function essentially uses a trial and error approach to back-solving the problem by plugging in guesses until it arrives at the answer.
There are multiple algorithms to solve this problem.
It is called multi dimension optimization.
There are group of probabilistic algorithms usually refereed as random search algorithms. Most famous is genetic optimization.
Also there is more classical approaches: Gradient descend, simplex method, etc.
I am trying to implement a paper and I am facing problem while representing linear equations mentioned in the paper. I am using LPsolve (linear problem solver) to solve the equations. But not able to represent some equations in Java so that LPSOLVE can resolve. Anyone with expertise in this please do help me.
paper i am trying to implement is http://www.cs.cmu.edu/~dshahaf/kdd2010-shahaf-guestrin.pdf and equations are mentioned in section 2.2.1
Based on what I can tell, you seem to have trouble figuring out o implementing some functions that would represent how certain mathematical functions work. It doesn't sound like you've run into an error, so I'll write down a few tips I can think of.
First off, check if the functions you are looking for already exist in the basic library by taking a look in the documentation. Maybe it doesn't state it exactly like you want, but perhaps some of the functionality is there.
http://lpsolve.sourceforge.net/5.5/Java/docs/api/
If you can't find everything you want, then you've got two options. One is to program the functions you desire yourself, and the other is to use another fleshed out Java library such as Colt which has many features.
http://dst.lbl.gov/ACSSoftware/colt/
I have a set of subgraphs and I need to match them on the graph they were extracted from. I also need to count how many times each subgraph shows up in such graph (I need to store all possible matches). There must be a perfect match considering the edges' labels of both subgraph and graph, the vertices' labels, however, don´t need to match each other. I built my system using JUNG API, so I would like a solution (api, algorithm etc) that could deal with the Graph structure provided by JUNG. Any thoughts?
JUNG is very full-featured, so if there isn't a graph analysis algorithm in JUNG for what you need, there's usually a strong, theoretical reason for it. To me, your problem sounds like an instance of the Subgraph Isomorphism problem, which is NP-Complete:
http://en.wikipedia.org/wiki/Subgraph_isomorphism_problem
NP-Completeness may or may not be familiar to you (it took me 7 years of college and Master's Degree in Computer Science to understand it!), so I'll give a high-level description here. Certain problems, like sorting, can be solved in Polynomial time with respect to their input size. For example, if I have a list of N elements, I can sort it in O(N log(N)) time. More specifically, if I can solve a problem in Polynomial time, this means I can solve the problem without exhausting every possible solution. In the sorting case, I could traverse every possible permutation of the list and, if I found a permutation of the list that was sorted, return it. This is obviously not the fastest way to solve the problem though! Some very clever mathematicians were able to get it down to its theoretical minimum of O(N log(N)), thus we can sort really big lists of things quite quickly using computers today.
On the flip-side, NP-Complete problems are thought to have no Polynomial time solution (I say thought because no one has ever proven it, although evidence strongly suggests this is the case). Anyway, what this means is that you cannot definitively solve an NP-Complete problem without first exhausting every possible solution. The time complexity of NP-Complete problems are always O(c ^ N) or worse, where c is some constant greater than 1. This means that the time required to solve the problem grows exponentially with every incremental increase in problem size.
So what does this have to do with my problem???
What I'm getting at here is that, if the Subgraph Isomorphism problem is NP-Complete, then the only way you can determine if one graph is a subgraph of another graph is by exhausting every possible solution. So you can solve this, but probably only up to graphs of a few nodes or so (since the problem's time complexity grows exponentially with every incremental increase in graph size). This means that it is computationally infeasible to compute a solution for your problem because as soon as you reach a certain graph size, it will quite literally take forever to find a solution.
More practically, if your boss asks you to do something that is provably NP-Complete, you can simply say it's impossible and he will have to listen to you. If your professor asks you to do something that is provably NP-Complete, show him that it's NP-Complete and you'll probably get an A for the course. If YOU are trying to do something NP-Complete of your own accord, it's better to just move on to the next project... ;)
Well, I had to solve the problem by implementing it from scratch. I followed the strategy suggested in the topic Any working example of VF2 algorithm?. So, if someone is in doubt about this problem too, I suggest to take a look at Rich Apodaca's answer in the aforementioned topic.
I looking for a program or library in Java capable of finding non-random properties of a byte sequence. Something when given a huge file, runs some statistical tests and reports if the data show any regularities.
I know three such programs, but not in Java. I tried all of them, but they don't really seem to work for me (which is quite surprising as one of them is by NIST). The oldest of them, diehard, works fine, but it's a bit hard to use.
As some of the commenters have stated, this is really an expert mathematics problem. The simplest explanation I could find for you is:
Run Tests for Non-randomness
Autocorrelation
It's interesting, but as it uses 'heads or tails' to simplify its example, you'll find you need to go much deeper to apply the same theory to encryption / cryptography etc - but it's a good start.
Another approach would be using Fuzzy logic. You can extract fuzzy associative rules from sets of data. Those rules are basically implications in the form:
if A then B, interpreted for example "if 01101 (is present) then 1111 (will follow)"
Googling "fuzzy data mining"/"extracting fuzzy associative rules" should yield you more than enough results.
Your problem domain is quite huge, actually, since this is what data/text mining is all about. That, and statistical & combinatorial analysis, just to name a few.
About a program that does that - take a look at this.
Not so much an answer to your question but to your comment that "any observable pattern is bad". Which got me thinking that randomness wasn't the problem but rather observable patterns, and to tackle this problem surely you need observers. So, in short, just set up a website and crowdsource it.
Some examples of this technique applied to colour naming: http://blog.xkcd.com/2010/05/03/color-survey-results/ and http://www.hpl.hp.com/personal/Nathan_Moroney/color-name-hpl.html
I need to solve nonlinear minimization (least residual squares of N unknowns) problems in my Java program. The usual way to solve these is the Levenberg-Marquardt algorithm. I have a couple of questions
Does anybody have experience on the different LM implementations available? There exist slightly different flavors of LM, and I've heard that the exact implementation of the algorithm has a major effect on the its numerical stability. My functions are pretty well-behaved so this will probably not be a problem, but of course I'd like to choose one of the better alternatives. Here are some alternatives I've found:
FPL Statistics Group's Nonlinear Optimization Java Package. This includes a Java translation of the classic Fortran MINPACK routines.
JLAPACK, another Fortran translation.
Optimization Algorithm Toolkit.
Javanumerics.
Some Python implementation. Pure Python would be fine, since it can be compiled to Java with jythonc.
Are there any commonly used heuristics to do the initial guess that LM requires?
In my application I need to set some constraints on the solution, but luckily they are simple: I just require that the solutions (in order to be physical solutions) are nonnegative. Slightly negative solutions are result of measurement inaccuracies in the data, and should obviously be zero. I was thinking to use "regular" LM but iterate so that if some of the unknowns becomes negative, I set it to zero and resolve the rest from that. Real mathematicians will probably laugh at me, but do you think that this could work?
Thanks for any opinions!
Update: This is not rocket science, the number of parameters to solve (N) is at most 5 and the data sets are barely big enough to make solving possible, so I believe Java is quite efficient enough to solve this. And I believe that this problem has been solved numerous times by clever applied mathematicians, so I'm just looking for some ready solution rather than cooking my own. E.g. Scipy.optimize.minpack.leastsq would probably be fine if it was pure Python..
The closer your initial guess is to the solution, the faster you'll converge.
You said it was a non-linear problem. You can do a least squares solution that's linearized. Maybe you can use that solution as a first guess. A few non-linear iterations will tell you something about how good or bad an assumption that is.
Another idea would be trying another optimization algorithm. Genetic and ant colony algorithms can be a good choice if you can run them on many CPUs. They also don't require continuous derivatives, so they're nice if you have discrete, discontinuous data.
You should not use an unconstrained solver if your problem has constraints. For
instance if know that some of your variables must be nonnegative you should tell
this to your solver.
If you are happy to use Scipy, I would recommend scipy.optimize.fmin_l_bfgs_b
You can place simple bounds on your variables with L-BFGS-B.
Note that L-BFGS-B takes a general nonlinear objective function, not just
a nonlinear least-squares problem.
I agree with codehippo; I think that the best way to solve problems with constraints is to use algorithms which are specifically designed to deal with them. The L-BFGS-B algorithm should probably be a good solution in this case.
However, if using python's scipy.optimize.fmin_l_bfgs_b module is not a viable option in your case (because you are using Java), you can try using a library I have written: a Java wrapper for the original Fortran code of the L-BFGS-B algorithm. You can download it from http://www.mini.pw.edu.pl/~mkobos/programs/lbfgsb_wrapper and see if it matches your needs.
The FPL package is quite reliable but has a few quirks (array access starts at 1) due to its very literal interpretation of the old fortran code. The LM method itself is quite reliable if your function is well behaved. A simple way to force non-negative constraints is to use the square of parameters instead of the parameters directly. This can introduce spurious solutions but for simple models, these solutions are easy to screen out.
There is code available for a "constrained" LM method. Look here http://www.physics.wisc.edu/~craigm/idl/fitting.html for mpfit. There is a python (relies on Numeric unfortunately) and a C version. The LM method is around 1500 lines of code, so you might be inclined to port the C to Java. In fact, the "constrained" LM method is not much different than the method you envisioned. In mpfit, the code adjusts the step size relative to bounds on the variables. I've had good results with mpfit as well.
I don't have that much experience with BFGS, but the code is much more complex and I've never been clear on the licensing of the code.
Good luck.
I haven't actually used any of those Java libraries so take this with a grain of salt: based on the backends I would probably look at JLAPACK first. I believe LAPACK is the backend of Numpy, which is essentially the standard for doing linear algebra/mathematical manipulations in Python. At least, you definitely should use a well-optimized C or Fortran library rather than pure Java, because for large data sets these kinds of tasks can become extremely time-consuming.
For creating the initial guess, it really depends on what kind of function you're trying to fit (and what kind of data you have). Basically, just look for some relatively quick (probably O(N) or better) computation that will give an approximate value for the parameter you want. (I recently did this with a Gaussian distribution in Numpy and I estimated the mean as just average(values, weights = counts) - that is, a weighted average of the counts in the histogram, which was the true mean of the data set. It wasn't the exact center of the peak I was looking for, but it got close enough, and the algorithm went the rest of the way.)
As for keeping the constraints positive, your method seems reasonable. Since you're writing a program to do the work, maybe just make a boolean flag that lets you easily enable or disable the "force-non-negative" behavior, and run it both ways for comparison. Only if you get a large discrepancy (or if one version of the algorithm takes unreasonably long), it might be something to worry about. (And REAL mathematicians would do least-squares minimization analytically, from scratch ;-P so I think you're the one who can laugh at them.... kidding. Maybe.)