ABOUT THIS PACKAGE: 

* SCOPT stands for Self-Concordant OPTimization

* This is a software package written in MATLAB that implements the several 
  first order and second order optimization algorithms for solving composite 
  minimization problems of a self-concordant (like) function and a convex and 
  possibly nonsmooth function

                 min_x { F(x) = f(x) + rho*g(x) }

  where f is a smooth convex function and self-concordant (like)
        g is a convex and possibly nonsmooth function,
        rho is a regularization parameter.

  Note that when f(x) is a barrier functions of a given convex set C. 
  Then, a path-following solver is also provided for this case.
  
REFERENCES:

[1]. Q. Tran Dinh, A. Kyrillidis and V. Cevher: A proximal Newton 
     framework for composite minimization: Graph learning without 
     Cholesky decompositions and matrix inversions. ICML 2013, Alanta.

[2]. Q. Tran Dinh, A. Kyrillidis and V. Cevher: Self-Concordant 
     Minimization, Tech. Report, LIONS, EPFL, 2013.  

[3]. Quoc Tran Dinh, Volkan Cevher and Yen-Huan Li: Minimization of 
     self-concordant-like functions, Tech. Report., LIONS, EPFL, 2013.

[4]. Q. Tran Dinh, A. Kyrillidis and V. Cevher: No heavy lifting required: 
     A proximal path following scheme for conic programming, Tech. Report, 
     LIONS, EPFL, 2013.  

INSTALLATION:

* Download SCOPT.zip at http://lions.epfl.ch/software/SCOPT.

* Unzip the SCOPT.zip file to create the SCOPT folder.

* Open MATLAB, browse to the SCOPT/ folder and run the scopt_setup.m file 
  to perform the basic installation steps.

* Now you are ready to use SCOPT.

STRUCTURES:

* Some notes on the contents of the directory:

   1.  The main folder contains the main routines that implement the algorithms presented 
       in the paper. This includes: first order gradient and fast gradient methods, 
       proximal newton methods and path-following proximal newton methods for graphical 
       learning, logistic regression, maxnorm clustering, and poisson imaging application 
       examples.

   2.  "/examples" - contains the main routines that call the aforementioned functions for 
       different problem cases.

   3.  "/functions" - contains a variety of helper functions, used by the implemented 
       algorithms. 

   4.  "/utilities" - contains general purpose functions (norm computation, functions that 
       print and second the information per iteration, etc.)
 
USAGE:
* For usage details on the configuration of the algorithms, type "help scopt_optimset" in 
  the Matlab prompt.


DOWNLOAD AND INSTALL THIRD-PARTY SOFTWARE:
Most of the example files contain comparison codes with off-the-shelf state-of-the-art codes. These include:

* SCOPT is compared with YALMIP as a modelling language in the maxnorm clustering example. 
  It can be downloaded at http://users.isy.liu.se/johanl/yalmip/.

* SCOPT is compared with CVX and TFOCS software packages in most application examples. 
  They can be downloaded at http://cvxr.com/.

* SCOPT is compared with QUIC algorithm in the graphical learning problem. The code for 
  QUIC can be downloaded at http://www.cs.utexas.edu/~sustik/QUIC/.

* For the logistic regression examples, our test codes utilize the libsvm package.
  This package can be downloaded at http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/

CONTACT: 
* This code is in development stage; any comments or bug reports are very welcome.

  Contacts: 
    Quoc TRAN DINH,
    Volkan CEVHER,
    Anastasios KYRILLIDIS,
    Laboratory for Information and Inference Systems (LIONS),
    EPFL STI IEL, Ecole Polytechnique Federale de Lausanne (EPFL),
    Station 11, CH1015-Lausanne, Switzerland.
            
    Email:  quoc.trandinh@epfl.ch
            volkan.cevher@epfl.ch
            anastasios.kyrillidis@epfl.ch
***