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Efficient bit manipulation in Java?

I am a Java programmer, and I have recently started competitive coding (Codechef, Hackerearth, etc..)
I have a feeling that bit manipulation is really very slow in Java when it comes to very large input values. From my experience, the same C/C++ code (By same i mean if converted from java with same algorithm, same strategy, etc. I am not changing my logic when converting from Java to C/C++ or vice à versa) which runs all test cases successfully, generates a time limit exceeded in Java. I am aware that most competitive programming sites provide 2x execution time for Java programs, but still it crosses the time limit.
In languages like C++, we have functions like __builtin_popcount which can exploit CPU inbuilt functions that are very fast. Such things are not available in Java. Some functions like java.lang.Integer.bitCount() will only work for a 32-bit int.
So should we prefer going with C++ for such problems? Should we even consider solving bit manipulation type problems using Java? If not, then are there any super fast efficient tricks rather than applying our own logic?
(There is also the fact that different architecture machines will take different amount of time, but lets ignore that. My question is in the context of competitive programming)

Long has bitCount too, and lowestOneBit, numberOfLeadingZeroes, numberOfTrailingZeroes etcetera.
And then there is BitSet, more for boolean purposes.
Those operations are in small short running programs horribly slow as the Just-In-Time compiler does not kick in.
Java is not C, but reverting inside a java program to C/C++ for those kind of operations, often do not merit because of the JNI overhead.
Till now I found java sufficient for reaching similar performance, even where one would not expect it. It is in allocations where java can beat C/C++ at times. But nobody beats C on the calculatory level, how for instance n % 12 is calculated.

(cast)Int vs float performance

I was searching trough google for an answer but i can't find something usefull. I am making games with libGDX and till now performance was not a big issue. But the game that i am working on now, will need some more optimization so here is my question:
With libGDX there is a lot of floats. I know that int is faster but what if i cast floats into int? Is this faster then using a float numbers or should just go with floats?
I don't need very high precision, becouse i mostly use that numbers for coordinates. But with much multiplications (i don't use division becouse it is slower) though the code i wonder what i should use.

Without even seeing your code the answer is: you're probably looking in the wrong place. The performance problem is not due to the use of floats, and switching to ints will not fix it.
As soon as you get performance problems (and even before) you must find ways to benchmark and measure the performance of your code. You might use profiling, or internal tracing, or some other way but you must do it.
Most performance problems are due to bad algorithms and/or a few small functions called millions of times. Fix the algorithms and recode the small critical functions and you will probably solve the problem. Switching your code to int is something you do after you've fixed everything else and need just a little more.
Put up some code on CodeReview and I'm sure you'll get lots more help with it.

Sensibility of converting Matlab program in Java to improve performance

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I have a somewhat hypothetical question. We've just programmed some code implementing genetic algorithm to find a solution to a sudoku game as part of the Computational Intelligence course project. Unfortunately it runs very slowly which limits our ability to perform adequate number of runs to find the optimal parameters. The question is whether reprogramming the whole thing - the code basis is not that big - into java would be a viable solution to boost up the speed of the software. Like we need 10x performance improvement really and i am doubtful that a Java version would be so much snappier. Any thoughts?
Thanks
=== Update 1 ===
Here is the code of the function that is computationally most expensive. It's a GA fitness function, that iterates through the population (different sudoku boards) and computes for each row and column how many elements are duplicates. The parameter n is passed, and is currently set to 9. That is, the function computes how many elements a row has that come up within the range 1 to 9 more then once. The higher the number the less is the fitness of the board, meaning that it is a weak candidate for the next generation.
The profiler reports that the two lines calling intersect in the for loops causing the poor performance, but we don't know how to really optimize the code. It follows below:
function [fitness, finished,d, threshold]=fitness(population_, n)
finished=false;
threshold=false;
V=ones(n,1);
d=zeros(size(population_,2),1);
s=[1:1:n];
for z=1:size(population_,2)
board=population_{z};
t=0;
l=0;
for i=1:n
l=l+n-length(intersect(s,board(:,i)'));
t=t+n-length(intersect(s,board(i,:)));
end
k=sum(abs(board*V-t));
f=t+l+k/50;
if t==2 &&l==2
threshold=true;
end
if f==0
finished=true;
else
fitness(z)=1/f;
d(z)=f;
end
end
end
=== Update 2 ===
Found a solution here: http://www.mathworks.com/matlabcentral/answers/112771-how-to-optimize-the-following-function
Using histc(V, 1:9), it's much faster :)

This is rather impossible to say without viewing your code, knowing if you use parallelization, etc. Indeed, as MrAzzaman says, profiling is the first thing to do. If you find a single bottleneck, especially if it is loop-heavy, it might be sufficient to write that part in C and connect it to Matlab via MEX.
In genetics algorithms, I'd believe that a 10x speed increase could be obtained rather than not. I do not quite agree with MrAzzaman here - in some cases (for loops, working with dynamic objects) is much, much slower than C/C++/Java. That is not to say that Matlab is always slow, for it is not, but there is plenty of algorithms where it would be slow.
I.e., I'd say that if you don't spend so much time looping over things, don't use objects, are not limited by Matlab's data structures, you might be ok with Matlab. That said, if I was to write GAs in Java or Matlab, I'd rather pick the former (and I'm using Matlab a lot more than Java these days, it's not just a matter of habit).
Btw. if you don't want to program it yourself, have a look at JGAP, it's a rather useful Java library for GAs.

OK, the first step is just to write a faster MATLAB function. Save the new languages for later.
I'm going to make the assumption that the board is full of valid guesses: that is, each entry is in [1, 9]. Now, what we're really looking for are duplicate entries in each row/column. To find duplicates, we sort. On a sorted row, if any element is equal to its neighbor, we have a duplicate. In MATLAB, the diff function does sliding pairwise differencing, and a zero in its output means that two neighboring values are equal. Both sort and diff operate on entire matrices, so no need for looping. Here's the code for the columnwise check:
l=sum(sum(diff(sort(board)) == 0));
The rowwise check is exactly the same, just using the transpose. Now let's put that in a test harness to compare results and timing with the previous version:
n = 9;
% Generate a test board. Random integers numbers from 1:n
board = randi(n, n);
s = 1:n;
K=1000; % number of iterations to use for timing
% Repeat current code for comparison
tic
for k=1:K
t=0;
l=0;
for i=1:n
l=l+n-length(intersect(s,board(:,i)'));
t=t+n-length(intersect(s,board(i,:)));
end
end
toc
% New code based on sort/diff for finding repeated values
tic
for k=1:K
l2=sum(sum(diff(sort(board)) == 0));
t2=sum(sum(diff(sort(board.')) == 0));
end
toc
% Check that reported values match
disp([l l2])
disp([t t2])
I encourage you to break down the sort/diff/sum code, and build it up on a sample board right at the command line, and try to understand exactly how it works.
On my system, the new code is about 330x faster.

For traditional GA applications for studying and research purposes it is better to use a native machine compiled source code programming language, like C, C++. Which I used when working with Genetic
Programming in the past and it is really fast.
However if you are planning to put this inside a more modern type of application that can be deployed in a web container or run in a mobile device, different OS, etc. Then Java is your best alternative as it is platform independent.
Another thing that can be important is about concurrency. For example lets us suppose that you want to put your GA in the Internet and you will have a growing number of users that are connected concurrently and all of them want to solve a different sudoku, Java applications are very good for scaling horizontally and works great with big number of concurrent connections.
Other thing that can be good if you migrate to Java is the number of libraries and frameworks that you can use, the Java universe is so big that you can find useful tools for almost any kind of application.
Java is a Virtual Machine compiled language, but it is important to note that currently the JVMs are very good in performance and are able to optimize the programs, for example they will find which methods are being more heavily used and compile them to native code, which means that for some applications you will find a Java program to be almost same fast than a native compiled from C.
Matlab is a platform that is very useful for engineering training and math, vector, matrix based calculations, also for some control stuff with Simulink. I used these products when in my electrical engineering bachelor, however those product's goal is to be mainly a tool for academic purposes I won't definitely go for Matlab if I am wanting to build a production application for the real world. It is not scalable, it is expensive to maintain and fine-tune it, also there are not lot of infrastructure providers that will support this kind of technology.
About the complexity of rewriting your code to Java, the Matlab code and Java code syntax is pretty similar, they also live in the same paradigm: Procedural OOP, even if you are not using OO in your code it can be easy rewritten in Java, the painful stuff will be when working with Matlab shortcuts to Math structures like matrix and passing functions as parameters.
For the matrix stuff, there are lot of java libraries like EJML that will make your life easier. About assigning functions to variables and then pass them as parameters to another functions, Java is not currently able to do that (Java 8 will be with Lambda Expressions) but you can have a equivalent functionality by using Class closures. Maybe these will be the only little painful things that you will find if migrating.

Found a solution here: http://www.mathworks.com/matlabcentral/answers/112771-how-to-optimize-the-following-function
Using histc(V, 1:9), it's much faster :)

java frameworks complexity statistics

It is extremely difficult to illustrate the complexity of frameworks (hibernate, spring, apache-commons, ...)
The only thing I could think of was to compare the file sizes of the jar libraries or even better, the number of classes contained in the jar files.
Of course this is not a mathematical sound proof of complexity. But at least it should make clear that some frameworks are lightweight compared to others.
Of course it would take quiet some time to calculate statistics. In an attempt to save time I was wondering if perhaps somebody did so already ?
EDIT:
Yes, there are a lot of tools to calculate the complexity of individual methods and classes. But this question is about third party jar files.
Also please note that 40% of phrases in my original question stress the fact that everybody is well aware of the fact that complexity is hard to measure and that file size and nr of classes may indeed not be sufficient. So, it is not necessary to elaborate on this any further.

There are tools out there that can measure the complexity of code. However this is more of a psychological question as you cannot mathematically define the term 'complex code'. And obviously giving two random persons some piece of code will give you very different answers.
In general the issue with complexity arises from the fact that a human brain cannot process more than a certain number of lines of code simultaneously (actually functional pieces, but normal lines of code should be exactly that). The exact number of lines that one can hold and understand in memory at the same time of course varies based on many factors (including time of day, day of the week and status of your coffee machine) and therefore completely depends on the audience. However less number of lines of code that you have to keep in your 'internal memory register' for one task is better, therefore this should be the general factor when trying to determine the complexity of an API.
There is however a pitfall with this way of calculating complexity, as many APIs offer you a fast way of solving a problem (easy entry level), but this solution later turns out to cause several very complex coding decisions, that on overall makes your code very difficult to understand. In contrast other APIs require you to do a very complex setup that is hard to understand at first, but the rest of your code will be extremely easy because of that initial setup.
Therefore a good way of measuring API complexity is to define a task to solve by that API that is representative and big enough, and then measure the average amount of simultaneous lines of code one has to keep in mind to implement that task.And once you're done, please publish the result in a scientific paper of your choice. ;)

Solving nonlinear equations numerically

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.)