I'm looking to run a gradient descent optimization to minimize the cost of an instantiation of variables. My program is very computationally expensive, so I'm looking for a popular library with a fast implementation of GD. What is the recommended library/reference?
GSL is a great (and free) library that already implements common functions of mathematical and scientific interest.
You can peruse through the entire reference manual online. Poking around, this starts to look interesting, but I think we'd need to know more about the problem.
It sounds like you're fairly new to minimization methods. Whenever I need to learn a new set of numeric methods, I usually look in Numerical Recipes. It's a book that provides a nice overview of the most common methods in the field, their tradeoffs, and (importantly) where to look in the literature for more information. It's usually not where I stop, but it's often a helpful starting point.
For example, if your function is costly, then your goal is to minimization the number of evaluations to need to converge. If you have analytical expressions for the gradient, then a gradient-based method will probably work to your advantage, assuming that the function and its gradient are well-behaved (lack singularities) in the domain of interest.
If you don't have analytical gradients, then you're almost always better off using an approach like downhill simplex that only evaluates the function (not its gradients). Numerical gradients are expensive.
Also note that all of these approaches will converge to local minima, so they're fairly sensitive to the point at which you initially start the optimizer. Global optimization is a totally different beast.
As a final thought, almost all of the code you can find for minimization will be reasonably efficient. The real cost of minimization is in the cost function. You should spend time profiling and optimizing your cost function, and select an algorithm that will minimize the number of times you need to call it (methods like downhill simplex, conjugate gradient, and BFGS all shine on different kinds of problems).
In terms of actual code, you can find a lot of nice routines at NETLIB, in addition to the other libraries that have been mentioned. Most of the routines are in FORTRAN 77, but not all; to convert them to C, f2c is quite useful.
One of the best respected libraries for this kind of optimization work is the NAG libraries. These are used all over the world in universities and industry. They're available for C / FORTRAN. They're very non-free, and contain a lot more than just minimisation functions - A lot of general numerical mathematics is covered.
Anyway I suspect this library is overkill for what you need. But here are the parts pertaining to minimisation: Local Minimisation and Global Minimization.
Try CPLEX which is available for free for students.
I'm looking to run a gradient descent optimization to minimize the cost of an instantiation of variables. My program is very computationally expensive, so I'm looking for a popular library with a fast implementation of GD. What is the recommended library/reference?
GSL is a great (and free) library that already implements common functions of mathematical and scientific interest.
You can peruse through the entire reference manual online. Poking around, this starts to look interesting, but I think we'd need to know more about the problem.
It sounds like you're fairly new to minimization methods. Whenever I need to learn a new set of numeric methods, I usually look in Numerical Recipes. It's a book that provides a nice overview of the most common methods in the field, their tradeoffs, and (importantly) where to look in the literature for more information. It's usually not where I stop, but it's often a helpful starting point.
For example, if your function is costly, then your goal is to minimization the number of evaluations to need to converge. If you have analytical expressions for the gradient, then a gradient-based method will probably work to your advantage, assuming that the function and its gradient are well-behaved (lack singularities) in the domain of interest.
If you don't have analytical gradients, then you're almost always better off using an approach like downhill simplex that only evaluates the function (not its gradients). Numerical gradients are expensive.
Also note that all of these approaches will converge to local minima, so they're fairly sensitive to the point at which you initially start the optimizer. Global optimization is a totally different beast.
As a final thought, almost all of the code you can find for minimization will be reasonably efficient. The real cost of minimization is in the cost function. You should spend time profiling and optimizing your cost function, and select an algorithm that will minimize the number of times you need to call it (methods like downhill simplex, conjugate gradient, and BFGS all shine on different kinds of problems).
In terms of actual code, you can find a lot of nice routines at NETLIB, in addition to the other libraries that have been mentioned. Most of the routines are in FORTRAN 77, but not all; to convert them to C, f2c is quite useful.
One of the best respected libraries for this kind of optimization work is the NAG libraries. These are used all over the world in universities and industry. They're available for C / FORTRAN. They're very non-free, and contain a lot more than just minimisation functions - A lot of general numerical mathematics is covered.
Anyway I suspect this library is overkill for what you need. But here are the parts pertaining to minimisation: Local Minimisation and Global Minimization.
Try CPLEX which is available for free for students.
Using double type I made Cubic Spline Interpolation Algorithm.
That work was success as it seems, but there was a relative error around 6% when very small values calculated.
Is double data type enough for accurate scientific numerical analysis?
Double has plenty of precision for most applications. Of course it is finite, but it's always possible to squander any amount of precision by using a bad algorithm. In fact, that should be your first suspect. Look hard at your code and see if you're doing something that lets rounding errors accumulate quicker than necessary, or risky things like subtracting values that are very close to each other.
Scientific numerical analysis is difficult to get right which is why I leave it the professionals. Have you considered using a numeric library instead of writing your own? Eigen is my current favorite here: http://eigen.tuxfamily.org/index.php?title=Main_Page
I always have close at hand the latest copy of Numerical Recipes (nr.com) which does have an excellent chapter on interpolation. NR has a restrictive license but the writers know what they are doing and provide a succinct writeup on each numerical technique. Other libraries to look at include: ATLAS and GNU Scientific Library.
To answer your question double should be more than enough for most scientific applications, I agree with the previous posters it should like an algorithm problem. Have you considered posting the code for the algorithm you are using?
If double is enough for your needs depends on the type of numbers you are working with. As Henning suggests, it is probably best to take a look at the algorithms you are using and make sure they are numerically stable.
For starters, here's a good algorithm for addition: Kahan summation algorithm.
Double precision will be mostly suitable for any problem but the cubic spline will not work well if the polynomial or function is quickly oscillating or repeating or of quite high dimension.
In this case it can be better to use Legendre Polynomials since they handle variants of exponentials.
By way of a simple example if you use, Euler, Trapezoidal or Simpson's rule for interpolating within a 3rd order polynomial you won't need a huge sample rate to get the interpolant (area under the curve). However, if you apply these to an exponential function the sample rate may need to greatly increase to avoid loosing a lot of precision. Legendre Polynomials can cater for this case much more readily.
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It seems that many projects slowly come upon a need to do matrix math, and fall into the trap of first building some vector classes and slowly adding in functionality until they get caught building a half-assed custom linear algebra library, and depending on it.
I'd like to avoid that while not building in a dependence on some tangentially related library (e.g. OpenCV, OpenSceneGraph).
What are the commonly used matrix math/linear algebra libraries out there, and why would decide to use one over another? Are there any that would be advised against using for some reason? I am specifically using this in a geometric/time context*(2,3,4 Dim)* but may be using higher dimensional data in the future.
I'm looking for differences with respect to any of: API, speed, memory use, breadth/completeness, narrowness/specificness, extensibility, and/or maturity/stability.
Update
I ended up using Eigen3 which I am extremely happy with.
There are quite a few projects that have settled on the Generic Graphics Toolkit for this. The GMTL in there is nice - it's quite small, very functional, and been used widely enough to be very reliable. OpenSG, VRJuggler, and other projects have all switched to using this instead of their own hand-rolled vertor/matrix math.
I've found it quite nice - it does everything via templates, so it's very flexible, and very fast.
Edit:
After the comments discussion, and edits, I thought I'd throw out some more information about the benefits and downsides to specific implementations, and why you might choose one over the other, given your situation.
GMTL -
Benefits: Simple API, specifically designed for graphics engines. Includes many primitive types geared towards rendering (such as planes, AABB, quatenrions with multiple interpolation, etc) that aren't in any other packages. Very low memory overhead, quite fast, easy to use.
Downsides: API is very focused specifically on rendering and graphics. Doesn't include general purpose (NxM) matrices, matrix decomposition and solving, etc, since these are outside the realm of traditional graphics/geometry applications.
Eigen -
Benefits: Clean API, fairly easy to use. Includes a Geometry module with quaternions and geometric transforms. Low memory overhead. Full, highly performant solving of large NxN matrices and other general purpose mathematical routines.
Downsides: May be a bit larger scope than you are wanting (?). Fewer geometric/rendering specific routines when compared to GMTL (ie: Euler angle definitions, etc).
IMSL -
Benefits: Very complete numeric library. Very, very fast (supposedly the fastest solver). By far the largest, most complete mathematical API. Commercially supported, mature, and stable.
Downsides: Cost - not inexpensive. Very few geometric/rendering specific methods, so you'll need to roll your own on top of their linear algebra classes.
NT2 -
Benefits: Provides syntax that is more familiar if you're used to MATLAB. Provides full decomposition and solving for large matrices, etc.
Downsides: Mathematical, not rendering focused. Probably not as performant as Eigen.
LAPACK -
Benefits: Very stable, proven algorithms. Been around for a long time. Complete matrix solving, etc. Many options for obscure mathematics.
Downsides: Not as highly performant in some cases. Ported from Fortran, with odd API for usage.
Personally, for me, it comes down to a single question - how are you planning to use this. If you're focus is just on rendering and graphics, I like Generic Graphics Toolkit, since it performs well, and supports many useful rendering operations out of the box without having to implement your own. If you need general purpose matrix solving (ie: SVD or LU decomposition of large matrices), I'd go with Eigen, since it handles that, provides some geometric operations, and is very performant with large matrix solutions. You may need to write more of your own graphics/geometric operations (on top of their matrices/vectors), but that's not horrible.
So I'm a pretty critical person, and figure if I'm going to invest in a library, I'd better know what I'm getting myself into. I figure it's better to go heavy on the criticism and light on the flattery when scrutinizing; what's wrong with it has many more implications for the future than what's right. So I'm going to go overboard here a little bit to provide the kind of answer that would have helped me and I hope will help others who may journey down this path. Keep in mind that this is based on what little reviewing/testing I've done with these libs. Oh and I stole some of the positive description from Reed.
I'll mention up top that I went with GMTL despite it's idiosyncrasies because the Eigen2 unsafeness was too big of a downside. But I've recently learned that the next release of Eigen2 will contain defines that will shut off the alignment code, and make it safe. So I may switch over.
Update: I've switched to Eigen3. Despite it's idiosyncrasies, its scope and elegance are too hard to ignore, and the optimizations which make it unsafe can be turned off with a define.
Eigen2/Eigen3
Benefits: LGPL MPL2, Clean, well designed API, fairly easy to use. Seems to be well maintained with a vibrant community. Low memory overhead. High performance. Made for general linear algebra, but good geometric functionality available as well. All header lib, no linking required.
Idiocyncracies/downsides: (Some/all of these can be avoided by some defines that are available in the current development branch Eigen3)
Unsafe performance optimizations result in needing careful following of rules. Failure to follow rules causes crashes.
you simply cannot safely pass-by-value
use of Eigen types as members requires special allocator customization (or you crash)
use with stl container types and possibly other templates required
special allocation customization (or you will crash)
certain compilers need special care to prevent crashes on function calls (GCC windows)
GMTL
Benefits: LGPL, Fairly Simple API, specifically designed for graphics engines.
Includes many primitive types geared towards rendering (such as
planes, AABB, quatenrions with multiple interpolation, etc) that
aren't in any other packages. Very low memory overhead, quite fast,
easy to use. All header based, no linking necessary.
Idiocyncracies/downsides:
API is quirky
what might be myVec.x() in another lib is only available via myVec[0] (Readability problem)
an array or stl::vector of points may cause you to do something like pointsList[0][0] to access the x component of the first point
in a naive attempt at optimization, removed cross(vec,vec) and
replaced with makeCross(vec,vec,vec) when compiler eliminates
unnecessary temps anyway
normal math operations don't return normal types unless you shut
off some optimization features e.g.: vec1 - vec2 does not return a
normal vector so length( vecA - vecB ) fails even though vecC = vecA -
vecB works. You must wrap like: length( Vec( vecA - vecB ) )
operations on vectors are provided by external functions rather than
members. This may require you to use the scope resolution everywhere
since common symbol names may collide
you have to do
length( makeCross( vecA, vecB ) )
or
gmtl::length( gmtl::makeCross( vecA, vecB ) )
where otherwise you might try
vecA.cross( vecB ).length()
not well maintained
still claimed as "beta"
documentation missing basic info like which headers are needed to
use normal functionalty
Vec.h does not contain operations for Vectors, VecOps.h contains
some, others are in Generate.h for example. cross(vec&,vec&,vec&) in
VecOps.h, [make]cross(vec&,vec&) in Generate.h
immature/unstable API; still changing.
For example "cross" has moved from "VecOps.h" to "Generate.h", and
then the name was changed to "makeCross". Documentation examples fail
because still refer to old versions of functions that no-longer exist.
NT2
Can't tell because they seem to be more interested in the fractal image header of their web page than the content. Looks more like an academic project than a serious software project.
Latest release over 2 years ago.
Apparently no documentation in English though supposedly there is something in French somewhere.
Cant find a trace of a community around the project.
LAPACK & BLAS
Benefits: Old and mature.
Downsides:
old as dinosaurs with really crappy APIs
For what it's worth, I've tried both Eigen and Armadillo. Below is a brief evaluation.
Eigen
Advantages:
1. Completely self-contained -- no dependence on external BLAS or LAPACK.
2. Documentation decent.
3. Purportedly fast, although I haven't put it to the test.
Disadvantage:
The QR algorithm returns just a single matrix, with the R matrix embedded in the upper triangle. No idea where the rest of the matrix comes from, and no Q matrix can be accessed.
Armadillo
Advantages:
1. Wide range of decompositions and other functions (including QR).
2. Reasonably fast (uses expression templates), but again, I haven't really pushed it to high dimensions.
Disadvantages:
1. Depends on external BLAS and/or LAPACK for matrix decompositions.
2. Documentation is lacking IMHO (including the specifics wrt LAPACK, other than changing a #define statement).
Would be nice if an open source library were available that is self-contained and straightforward to use. I have run into this same issue for 10 years, and it gets frustrating. At one point, I used GSL for C and wrote C++ wrappers around it, but with modern C++ -- especially using the advantages of expression templates -- we shouldn't have to mess with C in the 21st century. Just my tuppencehapenny.
If you are looking for high performance matrix/linear algebra/optimization on Intel processors, I'd look at Intel's MKL library.
MKL is carefully optimized for fast run-time performance - much of it based on the very mature BLAS/LAPACK fortran standards. And its performance scales with the number of cores available. Hands-free scalability with available cores is the future of computing and I wouldn't use any math library for a new project doesn't support multi-core processors.
Very briefly, it includes:
Basic vector-vector, vector-matrix,
and matrix-matrix operations
Matrix factorization (LU decomp, hermitian,sparse)
Least squares fitting and eigenvalue problems
Sparse linear system solvers
Non-linear least squares solver (trust regions)
Plus signal processing routines such as FFT and convolution
Very fast random number generators (mersenne twist)
Much more.... see: link text
A downside is that the MKL API can be quite complex depending on the routines that you need. You could also take a look at their IPP (Integrated Performance Primitives) library which is geared toward high performance image processing operations, but is nevertheless quite broad.
Paul
CenterSpace Software ,.NET Math libraries, centerspace.net
What about GLM?
It's based on the OpenGL Shading Language (GLSL) specification and released under the MIT license.
Clearly aimed at graphics programmers
I've heard good things about Eigen and NT2, but haven't personally used either. There's also Boost.UBLAS, which I believe is getting a bit long in the tooth. The developers of NT2 are building the next version with the intention of getting it into Boost, so that might count for somthing.
My lin. alg. needs don't exteed beyond the 4x4 matrix case, so I can't comment on advanced functionality; I'm just pointing out some options.
I'm new to this topic, so I can't say a whole lot, but BLAS is pretty much the standard in scientific computing. BLAS is actually an API standard, which has many implementations. I'm honestly not sure which implementations are most popular or why.
If you want to also be able to do common linear algebra operations (solving systems, least squares regression, decomposition, etc.) look into LAPACK.
I'll add vote for Eigen: I ported a lot of code (3D geometry, linear algebra and differential equations) from different libraries to this one - improving both performance and code readability in almost all cases.
One advantage that wasn't mentioned: it's very easy to use SSE with Eigen, which significantly improves performance of 2D-3D operations (where everything can be padded to 128 bits).
Okay, I think I know what you're looking for. It appears that GGT is a pretty good solution, as Reed Copsey suggested.
Personally, we rolled our own little library, because we deal with rational points a lot - lots of rational NURBS and Beziers.
It turns out that most 3D graphics libraries do computations with projective points that have no basis in projective math, because that's what gets you the answer you want. We ended up using Grassmann points, which have a solid theoretical underpinning and decreased the number of point types. Grassmann points are basically the same computations people are using now, with the benefit of a robust theory. Most importantly, it makes things clearer in our minds, so we have fewer bugs. Ron Goldman wrote a paper on Grassmann points in computer graphics called "On the Algebraic and Geometric Foundations of Computer Graphics".
Not directly related to your question, but an interesting read.
FLENS
http://flens.sf.net
It also implements a lot of LAPACK functions.
I found this library quite simple and functional (http://kirillsprograms.com/top_Vectors.php). These are bare bone vectors implemented via C++ templates. No fancy stuff - just what you need to do with vectors (add, subtract multiply, dot, etc).
I am writing an application where in a certain block I need to exponentiate reals around 3*500*500 times. When I use the exp(y*log(x)) algorithm, the program noticeably lags. It is significantly faster if I use another algorithm based on playing with data types, but that algorithm isn't very precise, although provides decent results for the simulation, and it's still not perfect in terms of speed.
Is there any precise exponentiation algorithm for real powers faster than exp(y*log(x))?
Thank you in advance.
If you need good accuracy, and you don't know anything about the distribution of bases (x values) a priori, then pow(x, y) is the best portable answer (on many -- not all -- platforms, this will be faster than exp(y*log(x)), and is also better behaved numerically). If you do know something about what ranges x and y can lie in, and with what distribution, that would be a big help for people trying to offer advice.
The usual way to do it faster while keeping good accuracy is to use a library routine designed to do many of these computations simultaneously for an array of x values and an array of y values. The catch is that such library implementations tend to cost money (like Intel's MKL) or be platform-specific (vvpowf in the Accelerate.framework on OS X, for example). I don't know much about MinGW, so someone else will need to tell you what's available there. The GSL may have something along these lines.
Depending on your algorithm (in particular if you have few to no additions), sometimes you can get away with working (at least partially) in log-space. You've probably already considered this, but if your intermediate representation is log_x and log_y then log(x^y) = exp(log_y) * log_x, which will be faster. If you can even be selective about it, then obviously computing log(x^y) as y * log_x is even cheaper. If you can avoid even just a few exponentiations, you may win a lot of performance. If there's any way to rewrite whatever loops you have to get the exponentiation operations outside of the innermost loop, that's a fairly certain performance win.