SCOT is an open-source distributed optimization solver designed to tackle Sparse Convex Optimization (SCO) problems within computational networks.
SCOT can be employed both as a standalone executable application and as a Python API known as SCOTPY, offering flexibility in integrating it into your projects.
- Solve Sparse Convex Optimization problems with ease.
- Utilize SCOT via its Python API, SCOTPY.
- Distribute optimization tasks efficiently across computational networks.
- Compatible with various optimization algorithms for diverse problem domains.
- Easily customizable and extensible for specific use cases.
See INSTALL
file.
SCOT MPI Command-Line Interface (SMCLI) can be employed to utilize SCOT as a solver, provided appropriate input files
in .dist.json
format are available.
Here's how to solve a DSLogR problem using the SMCLI
interface:
- Prepare problem data for each node in
JSON
format. Assuming two nodes, name the JSON files following this convention:
node_{mpi_rank}_{problem_name}.dist.json
For two nodes and the problem name logistic_regression
, the files should be named as:
1. node_{0}_{logistic_regression}.dist.json
2. node_{1}_{logistic_regression}.dist.json
The exact JSON
format for these files can be found in the data
folder.
- Execute
SCOT
with default settings:
mpirun -n 2 ./bin/scot --dir=/path/to/.dist.json/files --input=logistic_regression --nz=2
- After successful execution, two output files will be created in the
scot_framework
folder:
1. rank_0_output.json
2. rank_1_output.json
These files contain essential information about the solution to the given problem.
- A. Olama, A Distributed Framework for Sparse Convex Optimization: Algorithms and Tools. PhD thesis, Federal University of Santa Catarina (UFSC), Brazil, 2023.
- A. Olama, E. Camponogara, and J. Kronqvist, Sparse convex optimization toolkit: a mixed-integer framework, Optimization Methods and Software, pp. 1–27, 2023.
- Olama, E. Camponogara, and P. R. Mendes, Distributed primal outer approximation algorithm for sparse convex programming with separable structures, Journal of Global Optimization, vol. 86, no. 3, pp. 637–670, 2023.
- A. Olama, N. Bastianello, P. R. Mendes, and E. Camponogara, Relaxed hybrid consensus ADMM for distributed convex optimisation with coupling constraints, IET Control Theory & Applications, vol. 13, no. 17, pp. 2828–2837, 2019.
This work is part of the project “Distributed Optimization for Cooperative Machine Learning in Complex Networks” (No PGR10067) which has received funding from Fundacao de Amparo a Pesquisa e Inovacao do Estado de Santa Catarina (FAPESC), in Brazil, under grant 2021TR2265 and the Ministero degli Affari Esteri e della Cooperazione Internazionale (MAECI), in Italy. Technical support from Digital Futures at KTH and C3.ai Digital Transformation Institute for the project "AI Techniques for Power Systems Under Cyberattacks" is also gratefully acknowledged.