View: 
Part 1: Document Description

Citation 


Title: 
Replication data for "Dantzig–Wolfe reformulations for binary quadratic problems"  kQKP dataset 
Identification Number: 
doi:10.13130/RD_UNIMI/3QA23K 
Distributor: 
UNIMI Dataverse 
Date of Distribution: 
20210727 
Version: 
1 
Bibliographic Citation: 
Ceselli, Alberto; Létocart, Lucas; Traversi, Emiliano, 2021, "Replication data for "Dantzig–Wolfe reformulations for binary quadratic problems"  kQKP dataset", https://doi.org/10.13130/RD_UNIMI/3QA23K, UNIMI Dataverse, V1 
Citation 

Title: 
Replication data for "Dantzig–Wolfe reformulations for binary quadratic problems"  kQKP dataset 
Identification Number: 
doi:10.13130/RD_UNIMI/3QA23K 
Authoring Entity: 
Ceselli, Alberto (Dipartimento di Informatica, Università degli Studi di Milano) 
Létocart, Lucas (Université Sorbonne Paris Nord, LIPN, CNRS) 

Traversi, Emiliano (Université Sorbonne Paris Nord, LIPN, CNRS) 

Distributor: 
UNIMI Dataverse 
Access Authority: 
Ceselli, Alberto 
Depositor: 
Ceselli, Alberto 
Date of Deposit: 
20210727 
Holdings Information: 
https://doi.org/10.13130/RD_UNIMI/3QA23K 
Study Scope 

Keywords: 
Computer and Information Science, Mathematical Sciences, Mathematical programming, decomposition methods 
Abstract: 
The dataset contains instances for the cardinality constrained quadratic knapsack problem. These are used to test decomposition methods for Binary Quadratic Programs. Full details are given in the corresponding paper "DantzigWolfe Reformulations for Binary Quadratic Problems ". The dataset contains three sets of instances  qcr_instances: base instance, together with the optimal quadratic convex reformulation (QCR) multipliers found by solving an associated semidefinite program  mqcr_instances: base instance, together with the optimal improved convex 01 quadratic program reformulation (MQCR) multipliers found by solving an associated semidefinite program  convexity_analysis: instances in which the objective function quadratic cost matrix has a given number of positive eigenvalues. 
Methodology and Processing 

Sources Statement 

Data Access 

Other Study Description Materials 

Related Publications 

Citation 

Bibliographic Citation: 
Ceselli, A., Létocart, L. & Traversi, E. Dantzig–Wolfe reformulations for binary quadratic problems. Math. Prog. Comp. 14, 85–120 (2022). https://doi.org/10.1007/s1253202100206w 
Label: 
convexity_analysis.tgz 
Text: 
Base instances, together with QCR multipliers. Data is given in folders, of the form <perc. of positive eigenvalues>_<matrix density>_<instance_size>. Each folder contains ten subfolders, one for each instance. 
Notes: 
application/xcompressedtar 
Label: 
mqcr_instances.tgz 
Text: 
Base instances, together with MQCR multipliers. Data is given in folders, of the form <matrix density>_<instance_size>. Each folder contains ten subfolders, one for each instance. 
Notes: 
application/xcompressedtar 
Label: 
qcr_instances.tgz 
Text: 
Base instances, together with QCR multipliers. Data is given in folders, of the form <matrix density>_<instance_size>. Each folder contains ten subfolders, one for each instance. 
Notes: 
application/xcompressedtar 