Distributed Primal Outer Approximation Algorithm (DiPOA)

This repository holds the code of the paper titled Distributed Primal Outer Approximation Algorithm for Sparse Convex Programming with Separable Structures which introduces a distributed sparse convex optimization algorithm, named, Distributed Primal Outer Approximation Algorithm ( DiPOA ). DiPOA is implemented within dccp python package. In the following, a general instruction to install and run dccp is provided.

1. External Dependencies

1. Python Programming Language 3.7 or higher.
2. Gurobi Optimizer with a license quick start guide
3. Message Passing Interface - MPI
   1. OpenMPI
   2. MPICH
   3. Microsoft MPI (for windows)
4. GAMS with a license for solver comparison (optional) guide
5. Git

2. Supporting Platforms

1. Ubuntu 21.10 or WSL2 (preferred).
2. Microsoft Windows 10.

3. MPI Installation on Ubuntu OS

run the following commands

sudo apt update
sudo apt install openmpi-bin
sudo apt install libopenmpi-dev

4. MPI Installation on Microsoft Windows

please read the instruction here.

make sure mpiexec or mpirun executables are in the PATH environment variable of your preferred OS.

5. Prepare Running Environment

Ubuntu OS

1. Make sure Gurobi, MPI, and Python are installed properly.

2. Install python3-dev for installed python version (if not already installed)

sudo apt install python3-dev

3. Install python virtual environment

sudo apt install virtualenv

4. Clone dccp repository by executing the following command

git clone http://www.github.com/Alirezalm/dccp.git

5. Change directory to dccp and create a python virtual environment

cd dccp
python3 -m venv env

6. Activate the virtual environment

source ./env/bin/activate

7. Install python dependencies

pip install -r requirements.txt

Note: for the full list of requirements please see requirements.txt file

Microsoft Windows OS

1. Make sure Gurobi, MPI, and Python are installed properly.

2. Clone dccp repository by executing the following command

git clone http://www.github.com/Alirezalm/dccp.git

3. Change directory to dccp and create a python virtual environment

cd dccp
python -m venv env

4. Activate the virtual environment

.\env\Scripts\activate

5. Install python dependencies

pip install -r requirements.txt

6. General Usage

dccp package provides dipoa-cli.py, a simple command-line interface (CLI), to generate and solve random Distributed Sparse Logistic Regression (DSLR) and Distributed Sparse Quadratically Constrained Programming (DSQCP) problems. dipoa-cli.py consists of two sub-commands, namely, dslr and dsqcp that are responsible for solving DSLR and DSQCP problems, respectively. Finally, after the problem is solved successfully, a solution.json file will be generated.

DSLR

To generate and solve a DSLR problem, use the following command template

mpiexec -np <number-of-nodes> python dipoa-cli.py dslr <m> <n> <nz> --soc

where

1. m is the number of rows per node.
2. n is the number of decision variables (e.g. columns in DSLR).
3. nz is the number of non-zero elements.
4. --soc is a flag to indicate whether a second order cut generation is used.

Example

The following command generates and solves a DSLR problem with total 2000 rows per node, 50 variables with 5 non-zero elements, and 4 computational nodes (for this scenario, the total number of nodes is 8000.) As for the option --soc are used.

mpiexec -np 4 python dipoa-cli.py dslr 2000 50 5 --soc

DSQCP

Command template

mpiexec -np <number-of-nodes> python dipoa-cli.py dsqcp <n> <nz> --soc

where

1. n is the number of decision variables (e.g. columns in DSLR).
2. nz is the number of non-zero elements.
3. --soc is a flag to indicate whether a second order cut generation is used.

Example

The following command generates and solves a DSQCP problem with 50 variables with 5 non-zero elements, and 4 computational nodes. As for the option, --soc are used.

mpiexec -np 4 python dipoa-cli.py dsqcp 50 5  --soc

7. Comparison with GAMS MINLP Solvers

1. make sure gams executable is on OS PATH.
2. GAMS MINLP solvers are compared with DiPOA for DSLR problem instances.
3. To use gams MINLP solvers use the following command template

python gams-run.py <solver-name> <m> <n> <nz> <np>

where np is the number of nodes.

Example:

The following command runs bonmin for the DSLR problem with 1000 rows, 50 variables, 5 nonzero elements and, 4 nodes.

python gams-run.py bonmin 1000 50 5 4

The MINLP solver that are used in the paper are

1. bonmin
2. dicopt
3. knitro
4. dicopt
5. shot

8. Datasets Used in the Paper

The datasets used in the paper for both DSLR and DSQCP problems can are generated by executing the generate_datasets.py script as the following command shows.

python generate_datasets.py

generate_datasets.py creates data directory in the same directory that generate_datasets.py exists. The following directory tree, shows the structure of the data directory.

│── dslr
│   ├── sc_1
│   │   ├── data_10000_20.csv
│   │   ├── data_14000_20.csv
│   │   ├── data_18000_20.csv
│   │   ├── data_2000_20.csv
│   │   ├── data_22000_20.csv
│   │   ├── data_26000_20.csv
│   │   ├── data_30000_20.csv
│   │   ├── data_34000_20.csv
│   │   ├── data_38000_20.csv
│   │   ├── data_42000_20.csv
│   │   ├── data_46000_20.csv
│   │   ├── data_50000_20.csv
│   │   └── data_6000_20.csv
│   ├── sc_2
│   │   ├── data_50000_100.csv
│   │   ├── data_50000_120.csv
│   │   ├── data_50000_140.csv
│   │   ├── data_50000_160.csv
│   │   ├── data_50000_180.csv
│   │   ├── data_50000_200.csv
│   │   ├── data_50000_40.csv
│   │   ├── data_50000_60.csv
│   │   └── data_50000_80.csv
│   ├── sc_3
│   │   └── data_10000_20.csv
│   ├── sc_4
│   │   └── data_100000_200.csv
│   └── sc_5
│       └── data_300000_300.csv
└── dsqcp
    ├── sc_1
    │   ├── ch.csv
    │   ├── Ph.csv
    │   ├── q.csv
    │   └── Q.csv
    └── sc_2
        ├── ch.csv
        ├── Ph.csv
        ├── q.csv
        └── Q.csv

The dslr directory holds the datasets used in DSLR problem instances and consists of five subdirectories. Each directory holds the corresponding dataset for a specific scenario. Similarly, the dsqcp directory keeps the datasets of the scenarios used in DSQCP problem. All datasets are also generated in CSV format.