Python Wrapper for Optimization Software Suite SuiteOPT

# SuiteOPT

SuiteOPT is designed to solve optimization problems of the form

min f(x)    subject to    bl <= Ax <= bu, lo <= x <= hi

where x is a vector of length ncol, f(x) is a smooth (i.e., continuously differentiable) real-valued function, A is a real-valued nrow by ncol matrix, bl and bu are real-valued vectors of length nrow, and lo and hi are real-valued vectors of length ncol. In problems where f(x) is quadratic or linear, the user can provide special evaluation routines and/or vectors for optimized performance. Additionally, SuiteOPT can solve unconstrained or partially constrained problems by omitting any of the constraints when specifying the problem data.

## Installation

SuiteOPT for Python has been tested with Python versions 2.7, 3.5, 3.6, and 3.7 on Ubuntu. SuiteOPT for python can be installed using the package manager pip which will build all of the required dynamic libraries for SuiteOPT with Python. As part of the installation routine, SuiteOPT for Python will build the required dynamic libraries for the C/C++ packages SuiteSparse and SuiteOPT. The dynamic libraries for the C/C++ packages SuiteSparse and SuiteOPT require BLAS and LAPACK. The default installation is configured to use the OpenBLAS library for BLAS and LAPACK if installed on the user's system, however, users can provide desired libraries for BLAS and LAPACK using custom installation options.

#### 1. Installing Dependencies

Several dependencies are required before calling the pip install command to install SuiteOPT for Python. These dependencies are outlined below.

##### 1.1 Python Dependencies: Setuptools, Numpy, and Scipy

Inside the setup.py file used to install SuiteOPT, the Python packages setuptools and numpy are imported to complete the installation and the package and scipy is required for SuiteOPT to run. As a result, users missing one or more of these packages when installing SuiteOPT will encounter an error. These packages can be installed using pip by entering any of the following commands into the terminal to install the packages missing on the user's system:

$pip install setuptools$ pip install numpy
$pip install scipy Note: Numpy version >= 1.16.x should be installed as errors have been observed with previous versions. For users with Python 2.7 we have found that Scipy version = 1.2.2 works successfully while users with Python version 3.5 or greater can use Scipy 1.3.1 as it is compatible with SuiteOPT. Other versions may work as well but no errors with SuiteOPT involving Numpy or Scipy have occurred when using these versions. ##### 1.2 SuiteOPT and SuiteSparse Dependencies: BLAS, LAPACK, and CMake Users with BLAS, LAPACK, and CMake already available on their system may proceed to step 2. Users without BLAS and LAPACK on should choose desired BLAS and LAPACK libraries suitable for their system. SuiteOPT is configured to use OpenBLAS by default but can be customized at installation to use other BLAS and LAPACK configurations. SuiteOPT has been successfully tested with OpenBLAS and Intel's MKL BLAS and LAPACK. Note: Users should ensure that all dependencies required by their version of BLAS and LAPACK are installed prior to proceeding to the following step. Users without CMake should install it on their system before proceeding to the following step. CMake is required to build the dynamic library for METIS which is used by SuiteSparse. #### 2. Installing SuiteOPT for Python If OpenBLAS is installed on your system and the directory containing libopenblas and liblapack is located in a directory found during compilation by default, then follow the instructions for Default Installation. Otherwise, follow the instructions for Custom Installation. • Default Installation Type the following command into the terminal window to install SuiteOPT for Python with default BLAS and LAPACK configurations:$ pip install SuiteOPT --user

Note: If any errors involving BLAS and LAPACK are raised during the installation routine then it is likely that the installer was unable to identify information necessary for using the user's BLAS and LAPACK libraries when building the dynamic libraries for the C/C++ packages SuiteSparse and SuiteOPT. Users experiencing this problem should follow the instructions in Custom Installation.

• Custom Installation Users wishing to use custom BLAS and LAPACK configurations for SuiteOPT can pass them to pip during the installation procedure. There are two ways to provide custom BLAS and LAPACK configurations both of which are outlined below.

Option 1: Pass File Containing BLAS, LAPACK, and LDLIBS. With this option, users can create a file with variables BLAS, LAPACK, and LDLIBS that specify their BLAS and LAPACK libraries and the location on these libraries on their system, LDLIBS. The file should have one line for each variable. An example configuration for Intel's MKL BLAS and LAPACK is provided below.

Example: Custom Configuration File Using Intel's MKL BLAS and LAPACK.

# Contents of user created file 'mklconfig.txt'
LAPACK = -lmkl_lapack95_lp64
LDLIBS = -L/opt/intel/mkl/lib/intel64 -L/opt/intel/compilers_and_libraries_2019.5.281/linux/compiler/lib/intel64

Alternatively, the user can omit the LDLIBS variable if either the location of the BLAS and LAPACK libraries is known by the compiler or the full path to each library is included. As an example, a configuration for OpenBLAS using the alternative convention is provided below.

Example: Custom Configuration File Using OpenBLAS.

# Contents of user created file 'openblasconfig.txt'
LAPACK = /full/path/to/liblapack.a

Once the user's configuration for BLAS and LAPACK is saved to a file of their choice, say myconfig.txt, the full path to the file can be provided as an --install-option during the pip installation routine for SuiteOPT by entering the following command into the terminal:

$pip install SuiteOPT --user --install-option="--blfile=/full/path/to/myconfig.txt" Note: In particular, the --install-option is used to set the flag --blfile equal to the full path to the user's BLAS and LAPACK configuration file. Option 2: Pass BLAS, LAPACK, and LDLIBS as Individual --install-option Flags. With this option, the user can specify their BLAS and LAPACK libraries and the location(s) of these libraries directly as part of the pip install command by using --install-option flags to set the --blas, --lapack, and --ldlibs flags equal to the user's BLAS libraries, LAPACK libraries, and the absolute path location(s) of their BLAS and LAPACK libraries, respectively. Examples using various BLAS and LAPACK configurations are provided below. Example: Custom Configuration Using Intel's MKL BLAS and LAPACK.$ pip install SuiteOPT --user \
--install-option="--lapack=-lmkl_lapack95_lp64" \
--install-option="--ldlibs=-L/opt/intel/mkl/lib/intel64 -L/opt/intel/compilers_and_libraries_2019.5.281/linux/compiler/lib/intel64"

Example: Custom Configuration Using OpenBLAS.

$pip install SuiteOPT --user \ --install-option="--blas=-lopenblas -lgfortran -lpthread" \ --install-option="--lapack=-llapack" \ --install-option="--ldlibs=-L/full/path/to/openblas/directory" #### Notes on Troubleshooting Issues with BLAS and LAPACK Steps 0 -- 2 should be sufficient for installing SuiteOPT, however if errors are occurring at runtime involving BLAS and/or LAPACK please refer to this section to check for potential solutions to your problems. During runtime, SuiteOPT for Python will require the user's BLAS and LAPACK libraries and the C/C++ dynamic libraries for SuiteSparse and SuiteOPT compiled during step 2 of the installation. The installation routine in the setup.py file is designed to use the provided BLAS and LAPACK information to prevent runtime errors involving BLAS and LAPACK from occurring. If any of these libraries cannot be located runtime errors may occur when using SuiteOPT for Python. This step outlines how to set certain environment variables so that the necessary libraries can be found at runtime. For users encountering runtime errors involving the libraries libpasa, libpproj, libcg_descent, libnapheap, libamd, libcamd, libcolamd, libccolamd, libcholmod, or libmetis, see 1. For users encountering runtime errors involving BLAS or LAPACK libraries see 2. 1. Add SuiteSparse and SuiteOPT dynamic library directories to$LD_LIBRARY_PATH environment variable. Directories containing the dynamic libraries built by SuiteOPT and SuiteSparse need to be found in the user's $LD_LIBRARY_PATH. During installation, the required dynamic libraries for SuiteOPT and SuiteSparse are saved in directories called SuiteOPT-libraries and SuiteSparse-libraries, respectively, and should be located where python saves installed package information. The full path to these directories should be added to the$LD_LIBRARY_PATH environment variable. Examples are provided below for various shell configurations.

Example: Bourne/bash shell

export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/full/path/to/SuiteOPT-libraries" export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/full/path/to/SuiteSparse-libraries"

Example: TCSH/CSH shell

setenv LD_LIBRARY_PATH ${LD_LIBRARY_PATH}:/full/path/to/SuiteOPT-libraries setenv LD_LIBRARY_PATH${LD_LIBRARY_PATH}:/full/path/to/SuiteSparse-libraries

2. Add BLAS and LAPACK dynamic library directories to $LD_LIBRARY_PATH environment variable. Directories containing the user's BLAS and LAPACK dynamic libraries also need to be found in the user's$LD_LIBRARY_PATH. Below we provide information and examples for two popular versions of BLAS and LAPACK that should translate to other BLAS and LAPACK configurations.

Users with Openblas. When using OpenBLAS, users should be able to bypass this step as it is common for the directory containing libopenblas and liblapack to be contained in a subdirectory of /usr/lib (on Linux) and, hence, located in a directory that is checked by default for dynamic libraries. However, if an error is encountered indicating that OpenBLAS cannot be found, then add the absolute path to the directory containing libopenblas and liblapack to the $LD_LIBRARY_PATH environment variable. Examples are provided below for various shell and BLAS/LAPACK configurations. Example: Bourne/bash shell using Openblas export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/full/path/to/openblas/dir"

Example: TCSH/CSH shell using Openblas

setenv LD_LIBRARY_PATH ${LD_LIBRARY_PATH}:/full/path/to/openblas/dir Users with Intel's MKL BLAS and LAPACK. It is not common for the directory containing Intel's MKL BLAS and LAPACK to be installed in a directory that is checked by default for dynamic libraries (at least on Linux operating systems). Hence, the absolute path to the directory containing Intel's MKL BLAS and LAPACK dynamic libraries should be added to the$LD_LIBRARY_PATH environment variable. Additionally, it is a common requirement to link to the library libiomp5 included with the Intel MKL package when using Intel's MKL BLAS and LAPACK and, thus, the user should also add the full path to the directory containing the libiomp5 dynamic library to the $LD_LIBRARY_PATH environment variable. Additionally, users should add MKL dynamic libraries to the$LD_PRELOAD environment variable. When the MKL dynamic libraries are not added to the $LD_PRELOAD environment variable it has been observed that either (a) runtime errors or (b) poor performance of SuiteOPT for Python occurs. Some information outlining the prescribed solution included here was found in Intel's Forum on MKL. Examples are provided below for various shell configurations but note that the required dynamic libraries may vary from system to system. Example: Bourne/bash shell using Intel's MKL BLAS and LAPACK export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/opt/intel/compilers_and_libraries/linux/mkl/lib/intel64"
export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/opt/intel/compilers_and_libraries_2019.5.281/linux/compiler/lib/intel64" export LD_PRELOAD="${LD_PRELOAD}:/opt/intel/compilers_and_libraries/linux/mkl/lib/intel64/libmkl_def.so:/opt/intel/compilers_and_libraries/linux/mkl/lib/intel64/libmkl_avx2.so:/opt/intel/compilers_and_libraries/linux/mkl/lib/intel64/libmkl_intel_lp64.so:/opt/intel/compilers_and_libraries/linux/mkl/lib/intel64/libmkl_intel_thread.so:/opt/intel/compilers_and_libraries/linux/mkl/lib/intel64/libmkl_core.so:/opt/intel/compilers_and_libraries_2019.5.281/linux/compiler/lib/intel64/libiomp5.so"

Example: TCSH/CSH shell using Intel's MKL BLAS and LAPACK

setenv LD_LIBRARY_PATH ${LD_LIBRARY_PATH}:/opt/intel/compilers_and_libraries/linux/mkl/lib/intel64 setenv LD_LIBRARY_PATH${LD_LIBRARY_PATH}:/opt/intel/compilers_and_libraries_2019.5.281/linux/compiler/lib/intel64

## Usage

We now outline how to import the SuiteOPT module, set up a problem to solve with SuiteOPT, how to solve a problem with SuiteOPT, how to view problem statistics with SuiteOPT, and conclude with references to several demo files illustrating how to solve various problem configurations using SuiteOPT. Throughout the usage section, we provide sample blocks of code illustrating how to work with each component of SuiteOPT.

#### Importing SuiteOPT module

The SuiteOPT module can be imported within python by using the following command in python:

import SuiteOPT

#### SuiteOPT.problem class

Problem data is expected to be provided as an instance of the class SuiteOPT.problem. The attributes of the SuiteOPT.problem class are listed below. Note that each attribute of the SuiteOPT.problem class that is an array must be provided as a numpy array.

• ncol : dimension of initial guess x (also, number of columns in constraint matrix A)
• nrow : number of rows in matrix A
• x : Initial guess for primal variable (numpy array of length ncol)
• lo : lower bounds for x (numpy array of length ncol)
• hi : upper bounds for x (numpy array of length ncol)
• A : constraint matrix (numpy matrix or scipy.sparse.csc_matrix of dimension nrow by ncol)
• bl : lower bounds for Ax (numpy array of length nrow)
• bu : upper bounds for Ax (numpy array of length nrow)
• pylambda : multiplier for Ax (dual variable; numpy array of length nrow)
• y : Project y onto polyhedron (if solving polyhedral projection problem; numpy array of length ncol)
• a : Napsack constraint vector bl <= a'x <= bu (if solving napsack problem; numpy array of length ncol)
• d : Diagonal of hessian (if solving napsack problem; numpy array of length nrow)
• c : Linear term in objective function (if using Quadratic or Linear mode)
• objective : Objective function (Python function)
• hprod : Product of objective hessian times vector (if using quadratic mode; Python function)
• cg_hprod : Product of objective hessian times vector (unconstrained problem in quadratic mode; Python function)
• parm : Custom SuiteOPT.solve() parameter values (Python dict; keys = parm names, values = parm values)
• stat : Problem statistics after solving problem (Python dict; keys = stat names, values = stat values)
##### Initializing SuiteOPT.problem class

To initialize an instance of the class containing the problem data, for example, call

problem = SuiteOPT.problem(ncol, nrow)

If the problem has no matrix A then the value for nrow can be omitted and it will be initialized to zero. Alternatively, the values for ncol and nrow can be omitted upon initialization and they will be set to zero.

##### Setting problem data

Initial Guess. If the user wishes to provide an initial guess for the primal (decision) variable then the attribute x should be updated after initializing an instance of the SuiteOPT.problem class. If the user wishes to provide an initial guess for the dual variable (KKT multiplier) then the attribute pylambda should be updated after initializing an instance of the SuiteOPT.problem class.

# Suppose that x and pylambda have been set to desired numpy arrays
problem.x  = x   # Initial guess for solution
problem.pylambda  = pylambda   # Initial guess for dual variable

Bound Constraints. If the user wishes to enforce the constraint lo <= x then lo is required. If the user wishes to enforce the constraint x <= hi then hi is required. If lo = hi for the user's problem, then lo and hi must both be provided with lo = hi. If lo is not provided then it will be assumed that there is no lower bound on x. Similarly, if hi is not provided then it will be assumed that there is no upper bound on x.

# Suppose that lo and hi have been set to desired numpy arrays
problem.lo = lo  # Lower bound on primal variable,   lo <= x
problem.hi = hi  # Upper bound on primal variable,   x <= hi

Polyhedral Constraints. If the user wishes to enforce the constraint bl <= Ax <= bu then A and at least one of the vectors bl and bu are required. If bl is not provided then it will be assumed that there is no lower bound on Ax. Similarly, if bu is not provided then it will be assumed that there is no upper bound on Ax. If bl = bu in your problem, both bl and bu must be provided as the same vector. If the user does not wish to enforce this constraint then A, bl, and bu should not be updated after initializing an instance of the SuiteOPT.problem class.

# Suppose that bl and bl have been set to desired numpy arrays and A set to desired numpy matrix or csc_matrix
problem.A  = A   # Linear constraint matrix A,       bl <= Ax <= bu
problem.bl = bl  # Lower bound on linear constraint, bl <= Ax
problem.bu = bu  # Upper bound on linear constraint, Ax <= bu

Special Attributes. The attributes y, a, d, and c should only be provided for certain problem formulations. In particular, special implementations are used by SuiteOPT for solving polyhedral projection, separable convex quadratic knapsack, and unconstrained problems. More information on these problem formulations and how to solve them efficiently using SuiteOPT can be found in the Special Modes and Problem Formulations in SuiteOPT subsection.

Objective Function. As a general rule for determining which function attributes in SuiteOPT.problem are necessary for solving a problem, ensure that the attributes provided in SuiteOPT.problem are sufficient for computing (a) the objective function and (b) the gradient of the objective function. For straightforward use note that it is always sufficient to provide the attributes SuiteOPT.problem.objective and SuiteOPT.problem.gradient. However, alternative choices of the function attributes can be used depending on the type of problem in order to obtain improved performance. More detailed information on which attributes of SuiteOPT.problem are sufficient and optimized for solving problems of various types (nonlinear objective, quadratic objective, ...) can be found in the Special Modes and Problem Formulations in SuiteOPT subsection.

Customizing SuiteOPT.solve() Parameters. If the user wishes to provide custom parameter values for the solver SuiteOPT.solve(), the user can modify the values of each parameter by name by inserting it into the SuiteOPT.problem.parm dictionary. To see a list of all parameters and their default values, the user can call the method SuiteOPT.print_parm(). The following calls to this function can be used to view full or partial lists of customizable parameters within SuiteOPT:

SuiteOPT.print_parm("all")     : List of all default parameters values
SuiteOPT.print_parm("parm")    : List of default pasa parameter values
SuiteOPT.print_parm("pproj")   : List of default pproj parameter values
SuiteOPT.print_parm("cg")      : List of default cg parameter values
SuiteOPT.print_parm("napheap") : List of default napheap parameter values

Once the desired parameters have been identified, they can be modified by inserting them into the SuiteOPT.problem.parm dictionary attribute using the parameter name as the key and the desired parameter value as the value.

# Suppose we want to set the parameter 'PrintLevel' to be 3 (maximum printing during SuiteOPT)
# and the stopping tolerance, 'grad_tol' to be 1e-8
# Provide custom parameters to instance of SuiteOPT.problem class
problem.parm = parm  # custom parameter values (optional)

#### Special Modes and Problem Formulations in SuiteOPT

##### Nonlinear Mode

Here we outline which functions are required and optional when f(x) is a nonlinear cost function.

• Required Functions : SuiteOPT.problem.objective and SuiteOPT.problem.gradient

If the objective f(x) is quadratic, say of the form f(x) = 0.5 x^T H x + c^T x, the user should provide a routine, hprod, to evaluate the product between the objective Hessian H and the nonzero components of a vector x, so as to compute Hx, and the linear term in the objective, c.

• Required Attributes : SuiteOPT.problem.hprod and SuiteOPT.problem.c

Note that hprod must be a function with two inputs and one output. Using the above form for f(x), hprod should compute the vector Hx given a vector x and an array containing the nonzero indices of x. In particular, the first input of the function SuiteOPT.problem.hprod should be the vector to take the product with, the second input should be a vector containing the indices for which the input vector is nonzero, and the output should be the matrix-vector product.

For unconstrained problems with quadratic cost functions the user should instead provide cg_hprod. cg_hprod must be a function with one input, the vector x to compute the matrix-vector product with, and one output, the matrix-vector product Hx.

##### Linear Mode

If the objective f(x) is linear, say of the form f(x) = c^T x, the user should provide the linear term in the objective, c.

• Required Input : SuiteOPT.problem.c
##### Polyhedral Projection Problem

SuiteOPT implements a special routine, called PPROJ, for solving a polyhedral projection problem of the form

min  0.5 * || x - y || ** 2    subject to    bl <= Ax <= bu, lo <= x <= hi

where y is the point to be projected onto the polyhedral set {x : bl <= Ax <= bu, lo <= x <= hi}. When solving a problem of this form, it is sufficient to provide the following information to compute the objective and gradient values:

• Required Input : SuiteOPT.problem.y
##### Separable Convex Quadratic Knapsack Problem

SuiteOPT implements a special routine, called NAPHEAP, for solving a separable convex quadratic knapsack problem of the form

min  0.5 * x^T D x - c^T x    subject to    bl <= a^T x <= bu, lo <= x <= hi

where D is a diagonal matrix with nonnegative diagonal equal to the vector SuiteOPT.problem.d, a is a vector of length ncol, and bl and bu are scalars. When solving a problem of this form, it is sufficient to provide the following information to compute the objective and gradient values:

• Required Input : SuiteOPT.problem.d, SuiteOPT.problem.c

Additionally, instead of providing a constraint matrix A, the user should provide a constraint vector as a numpy array in the attribute SuiteOPT.problem.a.

##### Unconstrained Problem

SuiteOPT implements a special routine, called CG_DESCENT, for solving unconstrained optimization problems of the form

min  f(x)    subject to    x in R^n

In addition to nonlinear mode, quadratic mode can be used to evaluate the objective and gradient for unconstrained problems. Be sure to follow the details in the subsection Special Modes and Problem Formulations in SuiteOPT: Quadratic Mode to use quadratic mode with unconstrained problems.

#### SuiteOPT.solve() method

SuiteOPT.solve() requires a single input which is an instance of the SuiteOPT.problem class and returns a single output argument which is a numpy array containing the problem solution.

# Solve problem using SuiteOPT and store solution in xnew
xnew = SuiteOPT.solve(problem)

#### Problem Statistics

Problem statistics generated during a run of SuiteOPT.solve() are stored in the SuiteOPT.problem.stats attribute as a dictionary and can be printed using the method SuiteOPT.problem.print_stats().

### SuiteOPT Examples

Example files illustrating how to setup an instance of SuiteOPT.problem, call SuiteOPT.solve(), and print SuiteOPT.problem.stats are available in the Demo directory at https://github.com/chrundle/python-SuiteOPT. The type of problem and corresponding demo file are listed below:

Nonlinear Objective : demo.py
Linear Objective    : demoLP.py

Users with the pycutest package installed may also run the demo_cutest.py file to solve any problem from the CUTEst optimization test suite. When running demo_cutest.py, the user will be prompted to enter the name of the problem from the CUTEst test set that they wish to solve using SuiteOPT.

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