A MATLAB interface for L-BFGS-B
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A MATLAB interface for L-BFGS-B


As of March 24, 2014, the MATLAB code supports the latest version of the L-BFGS-B solver (version 3.0), and is compatible with GNU Octave. Thank you to José Vallet for providing these updates.

If you are having difficulties building the MEX files following the installation instructions below, see this alternate solution, which may work better for your setup.


L-BFGS-B is a collection of Fortran 77 routines for solving large-scale nonlinear optimization problems with bound constraints on the variables. One of the key features of the nonlinear solver is that knowledge of the Hessian is not required; the solver computes search directions by keeping track of a quadratic model of the objective function with a limited-memory BFGS (Broyden-Fletcher-Goldfarb-Shanno) approximation to the Hessian (see Note #1). The algorithm was developed by Ciyou Zhu, Richard Byrd and Jorge Nocedal. For more information, go to the original distribution site for the L-BFGS-B software package.

I've designed an interface to the L-BFGS-B solver so that it can be called like any other function in MATLAB (see Note #2). See the text below for more information on installing and calling this function in MATLAB. Along the way, I've also developed a C++ class that encapsulates all the "messy" details in executing the L-BFGS-B code. See below for instructions on how to incorporate this C++ class info your own code.

This code has been tested in MATLAB and Octave on Linux, and is integrated into GPML. It has also been executed successfully in Windows; see below.


Copyright (c) 2013, 2014 Peter Carbonetto

The lbfgsb-matlab project repository is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See LICENSE for more details.


Follow these steps to compile and install lbfgsb-matlab for MATLAB and Octave. Some of these steps are applicable to MATLAB or Octave only, and you can skip them if you want to install the interface for only one of them. These installation instructions assume you have Linux, although similar steps should also work for other UNIX-based operating systems, such as Mac OS X. These instructions also assume you have GNU Make installed. (We have successfully compiled this source code with gcc versions 4.6 and 5.4 under Ubuntu Linux 12.10, 13.10, 14.10, 15.10 and 16.10, and have successfully run the MATLAB scripts in Octave 3.6, 3.8, 4.0 and 4.2).

For Windows users: If you have a Windows operating system, Guillaume Jacquenot has generously provided instructions for compiling the software on Windows with gnumex and Mingw. These instructions are slightly out of date, and suggestions for improving these installation instructions are welcome.

We will create a MEX file, which is basically a file that contains a routine that can be called from MATLAB as if it were a built-it function. To learn about MEX files, I refer you to this document on the MathWorks website.

Download the source code. Clone or fork the this repository, or download this repository as a ZIP file and unpack the ZIP file. The src subdirectory contains some MATLAB files (ending in .m), some C++ source and header files (ending in .h and .cpp), a single Fortran 77 source file, solver.f, containing the L-BFGS-B routines, and a Makefile. I've included a version of the Fortran routines that is more recent than what is available for download at the distribution site at Northwestern University.

In the following, we will refer to the directory containing your local copy of the repository as LBFGSB_HOME. Wherever you find LBFGSB_HOME, you should substitute it with your own directory.

Install the C++ and Fortran 77 compilers. In order to build the MEX file, you will need both a C++ compiler and a Fortran 77 compiler. Unfortunately, you can't just use any compiler. You have to use the precise one supported by MATLAB. For instance, if you are running Mac OS X 7.2 on a Linux operating system, you will need to install GNU compiler collection (GCC) version 3.4.4. Even if you already have a compiler installed on Linux, it may be the wrong version, and if you use the wrong version things could go horribly wrong. It is important that you use the correct version of the compiler, otherwise you will encounter linking errors. Refer to this document to find out which compiler is supported by your version of MATLAB.

You may also be able to use the apt-get program to install gfortran, as in:

sudo apt-get install build-essential gfortran

Install Octave. You will of course need a working installation of Matlab or Octave. In the case of Octave, if you don't have it the easiest way to install it is from the repositories using apt-get:

sudo apt-get install octave

If you want to use a more recent version of Octave, you will have to install Octave from the source code. After that, find the path to the mkoctfile compiler (in the bin directory) and the include directory. You will need those paths for steps below.

Additionally you will need the Octave development tools, libs and header files to compile MEX files:

sudo apt-get install liboctave-dev

Configure MATLAB. Next, you need to set up and configure MATLAB to build MEX Files. This is explained quite nicely in this MathWorks support document.

Simple compilation procedure for MATLAB. (Thanks to Stefan Harmeling.) You might be able to build and compile the MEX File from source code simply by calling MATLAB's mex program with the C++ and Fortran source files as inputs, like so:

mex -output lbfgsb *.cpp solver.f

If that doesn't work (e.g. you get linking errors, or it makes MATLAB crash when you try to call it), then you may have to follow the more complicated installation instructions below.

Modify the Makefile. You are almost ready to build the MEX file. But before you do so, you need to edit the Makefile to coincide with your system setup. Edit the Makefile located inside the LBFGSB_HOME/src directory, following the instructions provided in this file. With this Makefile you may create:

  • A mex file for Matlab using the mex compiler (mex taget).
  • A mex file for Matlab using your C compiler (nomex target).
  • A mex file for Octave using mkoctfile (oct target).

Regarding the compilation for Octave, the variable OCTAVE_INCLUDE is the directory where the Octave header files required for development are installed. Make sure that it points to the correct directory corresponding to your Octave installation. OCT is the command to call the mkoctfile compiler. If mkoctfile is in the path, simply leave it as it is. If not, include in the variable the full path to it. If you have several versions of Octave installed, make sure that OCT points to the mkoctfile program that corresponds to the Octave version you would like to use.

For MATLAB, the variable MEX is the executable called to build the MEX file. The variables CXX and F77 must be the names of your C++ and Fortran 77 compilers. MEXSUFFIX is the MEX file extension specific to your platform. (For instance, the extension for Linux is mexglx.) The variable MATLAB_HOME must be the base directory of your MATLAB installation. Finally, CFLAGS and FFLAGS are options passed to the C++ and Fortran compilers, respectively. Often these flags coincide with your MEX options file (see here for more information on the options file). But often the settings in the MEX option file are incorrect, and so the options must be set manually. MATLAB requires some special compilation flags for various reasons, one being that it requires position-independent code. These instructions are vague (I apologize), and this step may require a bit of trial and error until before you get it right.

INSTALLDIR is the installation directory relative to the LBFGSB_HOME/src directory (see Installation below).

It may be helpful to look at the GCC documentation in order to understand what these various compiler flags mean.

Build the MEX file. If you are in the directory containing all the source files, calling make at the command prompt will first compile the Fortran and C++ source files into object code (.o files). After that, the make program calls the MEX script, which links all the object files together into a single MEX file. Call make from the LBFGSB_HOME/src directory using the appropriate target, one of:

make oct
make nomex 
make mex

If you didn't get any errors, then you are ready to try out the bound-constrained solver in MATLAB or Octave. Note that even if you didn't get any errors, there's still a possibility that you didn't link the MEX file properly, in which case executing the MEX file will cause MATLAB to crash. But there's only one way to find out: the hard way.

Final installation steps. Installing the mex file is as simple as placing it wherever MATLAB or Octave can find it; that is, in its "path". There are many ways to do this. Whatever approach you take, note that the mex file does not contain the usage documentation, which is provided in lbfgsb.m. So wherever you place lbfgsb.mex, make sure that you include lbfgsb.m in the same directory.

Perhaps the simplest approach to copy lbfgsb.mex and lbfgsb.m to a a specific folder. Alternatively,

make install

will create the .mex and .m files in the INSTALLDIR directory, and this directory will be automatically created if it does not exist.

Once you are in MATLAB or Octave, add the installation directory to the MATLAB or Octave path can be done using the command addpath. Note that once you exit MATLAB or Octave the updated path is not preserved, and you will need to use addpath in every new session. You can add the addpath command to ~/.octaverc or ~/matlab/startup. In this way, the path will automatically be updated every time you start a new session.

A brief tutorial

I've written a short script examplehs038.m which demonstrates how to call lbfgsb for solving a very small optimization problem, the Hock & Schittkowski test problem #38 (Note #3). The optimization problem has 4 variables. They are bounded from below by -10 and from above by +10. We've set the starting point to (-3, -1, -3, -1). We've written two MATLAB functions for computing the objective function and the gradient of the objective function at a given point. These functions can be found in the files computeObjectiveHS038.m and computeGradientHS038.m. If you run the script, the solver should very quickly progress toward the point (1, 1, 1, 1). The objective is 0 at this point.

The basic MATLAB function call is


The first input argument x0 declares the starting point for the solver. The second and third input arguments define the lower and upper bounds on the variables, respectively. If an entry of lb is set to -Inf, then this is equivalent to no lower bound for that entry (the same applies to upper bounds, except that it must be +Inf instead). The next two input arguments must be the names of MATLAB routines (M-files). The first routine must calculate the objective function at the current point. The output must be a scalar representing the objective evaluated at the current point. The second function must compute the gradient of the objective at the current point. The input is the same as objfunc, but it must return as many outputs as there are inputs. For a complete guide on using the MATLAB interface, type help lbfgsb in MATLAB.

The script exampleldaimages.m is a more complicated example. It uses the L-BFGS-B solver to compute some statistics of a posterior distribution for a text analysis model called latent Dirichlet allication (LDA). The model is used to estimate the topics for a collection of documents. The optimization problem is to compute an approximation to the posterior, since it is not possible to compute the posterior exactly. In this case, the optimization problem corresponds to minimizing the distance between the true posterior distribution and an approximate distribution. For more information, see the paper of Blei, Ng and Jordan, 2003 (Note #4). In order to run this example, you will need to install the lightspeed for MATLAB developed by Tom Minka at Microsoft Research.

The script starts by generating a bunch of canonical images that represent topics; if a pixel is white then a word at that position is more likely to appear in that topic. Note that the order of the pixels in the image is unimportant, since LDA is a "bag of words" model. In another figure we show a small sample of the data generated from the topics. Each image represents a document, and the pixels in the image depict the word proportions in that document (Note #5).

Next, the script runs the L-BFGS-B solver to find a local minimum to the variational objective. Buried in the M-file mflda.m is the call to the solver:

[xi gamma Phi] = lbfgsb({xi gamma Phi},...
    {repmat(lb,W,K)  repmat(lb,K,D)  repmat(lb,K,M)},...
    {repmat(inf,W,K) repmat(inf,K,D) repmat(inf,K,M)},...
    {nu eta L w lnZconst},'callbackMFLDA',...

This example will give us the opportunity to demonstrate some of the more complicated aspects of the our MATLAB interface. The first input to lbfgsb sets the initial iterate of the optimization algorithm. Notice that we've passed a cell array with entries that are matrices. Indeed, it is possible to pass either a matrix or a cell array. Whatever structure is passed, the other input arguments (and outputs) must also abide by this structure. The lower and upper bounds on variables are also cell arrays. All the upper bounds are set to infinity, which means that the variables are only bounded from below.

It is instructive to examine the callback routines. They look like:

f = computeObjectiveMFLDA(xi,gamma,Phi,auxdata)

[dxi, dgamma, dPhi] = ...

Notice that each entry to the cell array is its own input argument to the callback routines. The same applies for the output arguments of the gradient callback function.

The sixth input argument is a cell array that contains auxiliary data. This data will be passed to the MATLAB functions that evaluate the objective and gradient. The seventh input argument specifies a callback routine that is called exactly once for every iteration. This callback routine is most useful for examining the progress of the solver. The remaining input arguments are label/value pairs that override some of the default settings of L-BFGS-B.

After converging to a solution, we display topic samples drawn from the variational approximation. A good solution should come close to recovering the true topics from which the data was generated (although they might not be in the same order).

Don't be surprised if it takes many iterations before the optimization algorithm converges on a local minimum. (In repeated trials, I found it as many as 2000 iterations to converge to a stationary point of the objective.) This particular problem demonstrates the limitations of limited-memory quasi-Newton approximations to the Hessian; the low storage requirements can come at the cost of slow convergence to the solution.

The C++ interface

One of the nice byproducts of writing a MATLAB interface for L-BFGS-B is that I've ended up with a neat little C++ class that encapsulates the nuts and bolts of executing the solver. A brief description of the Program class can be found in the header file program.h.

"Program" is an abstract class, which means that its impossible to instantiate a Program object. This is because the class has member functions that aren't yet implemented. These are called pure virtual functions. In order to use the Program class, one needs to define another class that inherits the Program class and that implements the pure virtual functions. The class MatlabProgram (in matlabprogram.h) is an example of such a class.

The new child class must declare and implement these two functions:

virtual double computeObjective (int n, double* x);
virtual void computeGradient (int n, double* x, double* g);

See the header file for a detailed description of these functions. The only remaining detail is calling the Program constructor. After that, it is just a matter of declaring a new object of type MyProgram then calling the function runSolver.

Overview of source files

Fortran 77 source for the L-BFGS-B solver routines.

Header and source file for the C++ interface.

Header and source files for the MATLAB interface.

Usage instructions for the MEX file.

MATLAB script for solving the H&S test example #38.

MATLAB functions used by the script examplehs038.m.

MATLAB script that generates synthetic documents and topics, then computes a mean field variational approximation to the posterior distribution of the latent Dirichlet allocation model, then displays the result.

This function computes a mean field variational approximation to the posterior distribution of the latent Dirichlet allocation model by minimizing the distance between the variational distribution and the true distribution. It uses the L-BFGS-B to minimize the objective function subject to the bound constraints. The objective function also acts as a lower bound on the logarithm of the denominator that appears from the application of Bayes rule (Note #4).

Some functions used by exampleldaimages.m and mflda.m.


MATLAB interface Developed by
Peter Carbonetto
Dept. of Human Genetics
University of Chicago

Support for Octave contributed by
José Vallet
School of Electrical Engineering
Aalto University

Thank you to Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis for making their L-BFGS-B Fortran code available, and to Hannes Nickisch for ...


  1. Ciyou Zhu, Richard H. Byrd, Peihuang Lu and Jorge Nocedal (1997). Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on Mathematical Software 23(4): 550-560.

  2. Another MATLAB interface for the L-BFGS-B routines has been developed by Liam Stewart at the University of Toronto.

  3. Willi Hock and Klaus Schittkowski (1981). Test Examples for Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical Systems, Vol. 187, Springer-Verlag.

  4. David M. Blei, Andrew Y. Ng, Michael I. Jordan (2003). Latent Dirichlet allocation. Journal of Machine Learning Research Vol3: 993-1022.

  5. Tom L. Griffiths and Mark Steyvers (2004). Finding scientific topics. Proceedings of the National Academy of Sciences 101: 5228-5235.