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Part 1: Document Description
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Citation |
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Title: |
Replication data for "Dantzig–Wolfe reformulations for binary quadratic problems" - kQKP dataset |
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Identification Number: |
doi:10.13130/RD_UNIMI/3QA23K |
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Distributor: |
UNIMI Dataverse |
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Date of Distribution: |
2021-07-27 |
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Version: |
1 |
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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 |
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Citation |
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Title: |
Replication data for "Dantzig–Wolfe reformulations for binary quadratic problems" - kQKP dataset |
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Identification Number: |
doi:10.13130/RD_UNIMI/3QA23K |
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Authoring Entity: |
Ceselli, Alberto (Dipartimento di Informatica, Università degli Studi di Milano) |
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Létocart, Lucas (Université Sorbonne Paris Nord, LIPN, CNRS) |
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Traversi, Emiliano (Université Sorbonne Paris Nord, LIPN, CNRS) |
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Distributor: |
UNIMI Dataverse |
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Access Authority: |
Ceselli, Alberto |
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Depositor: |
Ceselli, Alberto |
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Date of Deposit: |
2021-07-27 |
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Holdings Information: |
https://doi.org/10.13130/RD_UNIMI/3QA23K |
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Study Scope |
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Keywords: |
Computer and Information Science, Mathematical Sciences, Mathematical programming, decomposition methods |
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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 "Dantzig-Wolfe 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 0-1 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. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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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/s12532-021-00206-w |
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Label: |
convexity_analysis.tgz |
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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. |
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Notes: |
application/x-compressed-tar |
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Label: |
mqcr_instances.tgz |
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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. |
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Notes: |
application/x-compressed-tar |
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Label: |
qcr_instances.tgz |
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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. |
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Notes: |
application/x-compressed-tar |