An Exact Solver for Semi-supervised Minimum Sum-of-Squares Clustering

Overview

PC-SOS-SDP: an Exact Solver for Semi-supervised Minimum Sum-of-Squares Clustering

PC-SOS-SDP is an exact algorithm based on the branch-and-bound technique for solving the semi-supervised Minimum Sum-of-Squares Clustering (MSSC) problem with pairwise constraints (i.e. must-link and cannot-link constraints) described in the paper "An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering". This repository contains the C++ source code, the MATLAB scripts, and the datasets used for the experiments.

Installation

PC-SOS-SDP calls the semidefinite programming solver SDPNAL+ by using the MATLAB Engine API for C++. It requires the MATLAB engine library libMatlabEngine and the Matlab Data Array library libMatlabDataArray. PC-SOS-SDP calls the integer programming solver Gurobi. PC-SOS-SDP uses the Armadillo library to handle matrices and linear algebra operations efficiently. Before installing Armadillo, first install OpenBLAS and LAPACK along with the corresponding development files. PC-SOS-SDP implements a configurable thread pool of POSIX threads to speed up the branch-and-bound search.

Ubuntu and Debian instructions:

  1. Install MATLAB (>= 2016b)
  2. Install Gurobi (>= 9.0)
  3. Install CMake, OpenBLAS, LAPACK and Armadillo:
sudo apt-get update
sudo apt-get install cmake libopenblas-dev liblapack-dev libarmadillo-dev
  1. Open the makefile clustering_c++/Makefile
    • Set the variable matlab_path with your MATLAB folder.
    • Set the variable gurobi_path with your Gurobi folder.
  2. Compile the code:
cd clustering_c++/
make
  1. Download SDPNAL+, move the folder clustering_matlab containing the MATLAB source code of PC-SOS-SDP in the SDPNAL+ main directory and set the parameter SDP_SOLVER_FOLDER of the configuration file accordingly. This folder and its subfolders will be automatically added to the MATLAB search path when PC-SOS-SDP starts.

The code has been tested on Ubuntu Server 20.04 with MATLAB R2020b, Gurobi 9.2 and Armadillo 10.2.

Configuration

Various parameters used in PC-SOS-SDP can be modified in the configuration file clustering_c++/config.txt:

  • BRANCH_AND_BOUND_TOL - optimality tolerance of the branch-and-bound
  • BRANCH_AND_BOUND_PARALLEL - thread pool size: single thread (1), multi-thread (> 1)
  • BRANCH_AND_BOUND_MAX_NODES - maximum number of nodes
  • BRANCH_AND_BOUND_VISITING_STRATEGY - best first (0), depth first (1), breadth first (2)
  • SDP_SOLVER_SESSION_THREADS_ROOT - number of threads for the MATLAB session at the root
  • SDP_SOLVER_SESSION_THREADS - number of threads for the MATLAB session for the ML and CL nodes
  • SDP_SOLVER_FOLDER - full path of the SDPNAL+ folder
  • SDP_SOLVER_TOL - accuracy of SDPNAL+
  • SDP_SOLVER_VERBOSE - do not display log (0), display log (1)
  • SDP_SOLVER_MAX_CP_ITER_ROOT - maximum number of cutting-plane iterations at the root
  • SDP_SOLVER_MAX_CP_ITER - maximum number of cutting-plane iterations for the ML and CL nodes
  • SDP_SOLVER_CP_TOL - cutting-plane tolerance between two consecutive cutting-plane iterations
  • SDP_SOLVER_MAX_INEQ - maximum number of valid inequalities to add
  • SDP_SOLVER_INHERIT_PERC - fraction of inequalities to inherit
  • SDP_SOLVER_EPS_INEQ - tolerance for checking the violation of the inequalities
  • SDP_SOLVER_EPS_ACTIVE - tolerance for detecting the active inequalities
  • SDP_SOLVER_MAX_PAIR_INEQ - maximum number of pair inequalities to separate
  • SDP_SOLVER_PAIR_PERC - fraction of the most violated pair inequalities to add
  • SDP_SOLVER_MAX_TRIANGLE_INEQ - maximum number of triangle inequalities to separate
  • SDP_SOLVER_TRIANGLE_PERC - fraction of the most violated triangle inequalities to add

Usage

cd clustering_c++/
./bb <DATASET> <K> <CONSTRAINTS> <LOG> <RESULT>
  • DATASET - path of the dataset
  • K - number of clusters
  • CONSTRAINTS - path of the constraints
  • LOG - path of the log file
  • RESULT - path of the optimal cluster assignment matrix

File DATASET contains the data points x_ij and the must include an header line with the problem size n and the dimension d:

n d
x_11 x_12 ... x_1d
x_21 x_22 ... x_2d
...
...
x_n1 x_n2 ... x_nd

File CONSTRAINTS should include indices (i, j) of the data points involved in must-link (ML) and/or cannot-link (CL) constraints:

CL i1 j1
CL i2 j2
...
...
ML i3 j3
ML i4 j4

If it does not contain any constraint (empty file), PC-SOS-SDP becomes SOS-SDP (the exact solver for unsupervised MSSC).

Log

The log file reports the progress of the algorithm:

  • N - size of the current node
  • NODE_PAR - id of the parent node
  • NODE - id of the current node
  • LB_PAR - lower bound of the parent node
  • LB - lower bound of the current node
  • FLAG - termination flag of SDPNAL+
    • 0 - SDP is solved to the required accuracy
    • 1 - SDP is not solved successfully
    • -1, -2, -3 - SDP is partially solved successfully
  • TIME (s) - running time in seconds of the current node
  • CP_ITER - number of cutting-plane iterations
  • CP_FLAG - termination flag of the cutting-plane procedure
    • -3 - current bound is worse than the previous one
    • -2 - SDP is not solved successfully
    • -1 - maximum number of iterations
    • 0 - no violated inequalities
    • 1 - maximum number of inequalities
    • 2 - node must be pruned
    • 3 - cutting-plane tolerance
  • CP_INEQ - number of inequalities added in the last cutting-plane iteration
  • PAIR TRIANGLE CLIQUE - average number of added cuts for each class of inequalities
  • UB - current upper bound
  • GUB - global upper bound
  • I J - current branching decision
  • NODE_GAP - gap at the current node
  • GAP - overall gap
  • OPEN - number of open nodes

Log file example:

DATA_PATH, n, d, k: /home/ubuntu/PC-SOS-SDP/instances/glass.txt 214 9 6
CONSTRAINTS_PATH: /home/ubuntu/PC-SOS-SDP/instances/constraints/glass/ml_50_cl_50_3.txt
LOG_PATH: /home/ubuntu/PC-SOS_SDP/logs/glass/log_ml_50_cl_50_3.txt

BRANCH_AND_BOUND_TOL: 1e-4
BRANCH_AND_BOUND_PARALLEL: 16
BRANCH_AND_BOUND_MAX_NODES: 200
BRANCH_AND_BOUND_VISITING_STRATEGY: 0

SDP_SOLVER_SESSION_THREADS_ROOT: 16
SDP_SOLVER_SESSION_THREADS: 1
SDP_SOLVER_FOLDER: /home/ubuntu/PC-SOS-SDP/SDPNAL+/
SDP_SOLVER_TOL: 1e-05
SDP_SOLVER_VERBOSE: 0
SDP_SOLVER_MAX_CP_ITER_ROOT: 80
SDP_SOLVER_MAX_CP_ITER: 40
SDP_SOLVER_CP_TOL: 1e-06
SDP_SOLVER_MAX_INEQ: 100000
SDP_SOLVER_INHERIT_PERC: 1
SDP_SOLVER_EPS_INEQ: 0.0001
SDP_SOLVER_EPS_ACTIVE: 1e-06
SDP_SOLVER_MAX_PAIR_INEQ: 100000
SDP_SOLVER_PAIR_PERC: 0.05
SDP_SOLVER_MAX_TRIANGLE_INEQ: 100000
SDP_SOLVER_TRIANGLE_PERC: 0.05


|    N| NODE_PAR|    NODE|      LB_PAR|          LB|  FLAG|  TIME (s)| CP_ITER| CP_FLAG|   CP_INEQ|     PAIR  TRIANGLE    CLIQUE|          UB|         GUB|     I      J|     NODE_GAP|          GAP|  OPEN|
|  164|       -1|       0|        -inf|     93.3876|     0|       110|       7|      -3|      6456|  242.571      4802   8.14286|     93.5225|    93.5225*|    -1     -1|   0.00144229|   0.00144229|     0|
|  163|        0|       1|     93.3876|     93.4388|     0|        35|       2|      -3|      5958|        1      3675         0|     93.4777|    93.4777*|    79    142|  0.000416211|  0.000416211|     0|
|  164|        0|       2|     93.3876|     93.4494|     0|        47|       2|      -3|      6888|        0      4635         0|     93.5225|     93.4777|    79    142|  0.000302427|  0.000302427|     0|
|  162|        1|       3|     93.4388|      93.506|     0|        27|       1|       2|      6258|        9      3759         0|         inf|     93.4777|   119    152| -0.000302724| -0.000302724|     0|
|  163|        1|       4|     93.4388|     93.4536|     0|        47|       4|      -3|      3336|        0      1789         0|     93.4777|     93.4777|   119    152|   0.00025747|   0.00025747|     0|
|  164|        2|       5|     93.4494|     93.4549|     0|        37|       1|      -3|      6888|        0      5000         0|     93.5225|     93.4777|    47     54|  0.000243844|  0.000243844|     0|
|  163|        2|       6|     93.4494|     93.4708|     0|        51|       2|       2|      7292|       11      4693         0|     93.5559|     93.4777|    47     54|  7.36443e-05|  7.36443e-05|     0|
|  164|        5|       7|     93.4549|      93.475|     0|        22|       0|       2|      6888|        0         0         0|     93.5225|     93.4777|   122    153|  2.82805e-05|  2.82805e-05|     0|
|  163|        4|       8|     93.4536|     93.4536|     0|        38|       2|      -3|      3257|        0     668.5         0|     93.4704|    93.4704*|    47     54|  0.000180057|  0.000180057|     0|
|  163|        5|       9|     93.4549|     93.5216|     0|        41|       1|       2|      6893|        8      5000         0|         inf|     93.4704|   122    153| -0.000547847| -0.000547847|     0|
|  163|        8|      10|     93.4536|     93.4536|     0|        27|       1|      -3|      3257|        0       879         0|     93.4704|     93.4704|    37     45|  0.000180057|  0.000180057|     0|
|  162|        8|      11|     93.4536|     93.4838|     0|        33|       1|       2|      6158|       24      4233         0|         inf|     93.4704|    37     45| -0.000143677| -0.000143677|     0|
|  162|        4|      12|     93.4536|     93.4658|     0|        75|       5|      -3|      2793|      4.6      2379         0|     93.5111|     93.4704|    47     54|  4.89954e-05|  4.89954e-05|     0|
|  162|       10|      13|     93.4536|     93.5053|     0|        19|       0|       2|      3122|        0         0         0|         inf|     93.4704|    37     99|  -0.00037365|  -0.00037365|     0|
|  163|       10|      14|     93.4536|     93.4701|     0|        31|       0|       2|      3257|        0         0         0|     93.4704|     93.4704|    37     99|  3.13989e-06|  3.13989e-06|     0|

WALL_TIME: 304 sec
N_NODES: 15
AVG_INEQ: 2788.05
AVG_CP_ITER: 1.93333
ROOT_GAP: 0.00144229
GAP: 0
BEST: 93.4704
Owner
Antonio M. Sudoso
Antonio M. Sudoso
Generating Images with Recurrent Adversarial Networks

Generating Images with Recurrent Adversarial Networks Python (Theano) implementation of Generating Images with Recurrent Adversarial Networks code pro

Daniel Jiwoong Im 121 Sep 08, 2022
《Geo Word Clouds》paper implementation

《Geo Word Clouds》paper implementation

Russellwzr 2 Jan 28, 2022
Repositório para arquivos sobre o Módulo 1 do curso Top Coders da Let's Code + Safra

850-Safra-DS-ModuloI Repositório para arquivos sobre o Módulo 1 do curso Top Coders da Let's Code + Safra Para aprender mais Git https://learngitbranc

Brian Nunes 7 Dec 10, 2022
Implementation for Learning to Track with Object Permanence

Learning to Track with Object Permanence A video-based MOT approach capable of tracking through full occlusions: Learning to Track with Object Permane

Toyota Research Institute - Machine Learning 91 Jan 03, 2023
Pytorch implementation of the paper DocEnTr: An End-to-End Document Image Enhancement Transformer.

DocEnTR Description Pytorch implementation of the paper DocEnTr: An End-to-End Document Image Enhancement Transformer. This model is implemented on to

Mohamed Ali Souibgui 74 Jan 07, 2023
This is an official implementation of "Polarized Self-Attention: Towards High-quality Pixel-wise Regression"

Polarized Self-Attention: Towards High-quality Pixel-wise Regression This is an official implementation of: Huajun Liu, Fuqiang Liu, Xinyi Fan and Don

DeLightCMU 212 Jan 08, 2023
Fast and robust clustering of point clouds generated with a Velodyne sensor.

Depth Clustering This is a fast and robust algorithm to segment point clouds taken with Velodyne sensor into objects. It works with all available Velo

Photogrammetry & Robotics Bonn 957 Dec 21, 2022
AOT (Associating Objects with Transformers) in PyTorch

An efficient modular implementation of Associating Objects with Transformers for Video Object Segmentation in PyTorch

162 Dec 14, 2022
SNIPS: Solving Noisy Inverse Problems Stochastically

SNIPS: Solving Noisy Inverse Problems Stochastically This repo contains the official implementation for the paper SNIPS: Solving Noisy Inverse Problem

Bahjat Kawar 35 Nov 09, 2022
A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).

Scalable Incomplete Network Embedding ⠀⠀ A PyTorch implementation of Scalable Incomplete Network Embedding (ICDM 2018). Abstract Attributed network em

Benedek Rozemberczki 69 Sep 22, 2022
Neighborhood Reconstructing Autoencoders

Neighborhood Reconstructing Autoencoders The official repository for Neighborhood Reconstructing Autoencoders (Lee, Kwon, and Park, NeurIPS 2021). T

Yonghyeon Lee 24 Dec 14, 2022
PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

PyDEns PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks. With PyDEns one can solve PD

Data Analysis Center 220 Dec 26, 2022
Code to reproduce the experiments in the paper "Transformer Based Multi-Source Domain Adaptation" (EMNLP 2020)

Transformer Based Multi-Source Domain Adaptation Dustin Wright and Isabelle Augenstein To appear in EMNLP 2020. Read the preprint: https://arxiv.org/a

CopeNLU 36 Dec 05, 2022
The Deep Learning with Julia book, using Flux.jl.

Deep Learning with Julia DL with Julia is a book about how to do various deep learning tasks using the Julia programming language and specifically the

Logan Kilpatrick 67 Dec 25, 2022
Optimizing synthesizer parameters using gradient approximation

Optimizing synthesizer parameters using gradient approximation NASH 2021 Hackathon! These are some experiments I conducted during NASH 2021, the Neura

Jordie Shier 10 Feb 10, 2022
NuPIC Studio is an all­-in-­one tool that allows users create a HTM neural network from scratch

NuPIC Studio is an all­-in-­one tool that allows users create a HTM neural network from scratch, train it, collect statistics, and share it among the members of the community. It is not just a visual

HTM Community 93 Sep 30, 2022
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning

Understanding Hyperdimensional Computing for Parallel Single-Pass Learning Authors: Tao Yu* Yichi Zhang* Zhiru Zhang Christopher De Sa *: Equal Contri

Cornell RelaxML 4 Sep 08, 2022
CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks

CALVIN CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks Oier Mees, Lukas Hermann, Erick Rosete,

Oier Mees 107 Dec 26, 2022
Python implementation of Lightning-rod Agent, the Stack4Things board-side probe

Iotronic Lightning-rod Agent Python implementation of Lightning-rod Agent, the Stack4Things board-side probe. Free software: Apache 2.0 license Websit

2 May 19, 2022
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Molecular Sets (MOSES): A benchmarking platform for molecular generation models Deep generative models are rapidly becoming popular for the discovery

MOSES 656 Dec 29, 2022