1 to 10 of 12 Results
Sep 14, 2023
Barbato, Michele; Luís Gouveia, 2023, "Experimental results of: The Hamiltonian p-Median Problem: Polyhedral Results and Branch-and-Cut Algorithms", https://doi.org/10.13130/RD_UNIMI/UDEATS, UNIMI Dataverse, V2
Dataset containing the experimental results of the research work "The Hamiltonian p-Median Problem: Polyhedral Results and Branch-and-Cut Algorithms". |
Feb 3, 2023
Bianchessi, Nicola, 2023, "Sub-Tree Scheduling for Wireless Sensor Networks with Partial Coverage (STSWSN-PC) Dataset", https://doi.org/10.13130/RD_UNIMI/IHTWC0, UNIMI Dataverse, V1, UNF:6:o+N3y3vWczdEhAUSUSFb7g== [fileUNF]
This dataset contains new benchmark instances for the optimization problem introduced by Adasme (2019) under the name Sub-Tree Scheduling for Wireless Sensor Networks with Partial Coverage (STSWSN-PC). The instances are those addressed in Bianchessi (2022), in which a complete de... |
Jan 3, 2023
Barbato, Michele; Ceselli, Alberto, 2022, "Technical Report of "Mathematical Programming for Simultaneous Feature Selection and Outlier Detection under l1 Norm"", https://doi.org/10.13130/RD_UNIMI/UA8PFI, UNIMI Dataverse, V3
Technical Report of "Mathematical Programming for Simultaneous Feature Selection and Outlier Detection under l1 Norm". Broad experimental campaign. |
Dec 27, 2022
PREMOLI, MARCO LUIGI; Alberto Ceselli, 2022, "Replication Data for: On good encodings for quantum annealing and digital optimization solvers: the cardinality constrained quadratic knapsack case", https://doi.org/10.13130/RD_UNIMI/Y3GKUF, UNIMI Dataverse, V1
Replication Data for "On good encodings for quantum annealing and digital optimization solvers: the cardinality constrained quadratic knapsack case". Please refer to file "readme.pdf" for further information about the dataset. |
Oct 26, 2022
Barbato, Michele; Ceselli, Alberto, 2022, "Replication Data for: "Mathematical Programming for Simultaneous Feature Selection and Outlier Detection under l1 Norm"", https://doi.org/10.13130/RD_UNIMI/1MZNNS, UNIMI Dataverse, V1, UNF:6:PWiIzt/vPLfrW/Rq52qghw== [fileUNF]
Training and test instances used in the work "Mathematical Programming for Simultaneous Feature Selection and Outlier Detection under l1 Norm" |
Oct 12, 2022
Ceselli, Alberto; Basso, Saverio, 2022, "MIPLib Random Decompositions Dataset", https://doi.org/10.13130/RD_UNIMI/T99WYI, UNIMI Dataverse, V1
The dataset contains a set of about 31000 random decompositions of MIPLib instances, together with (a) an evaluation of a set of 121 features over them (b) bound and time scores, obtained through optimization runs. Their detailed structure is described in the paper: S. Basso, A.... |
Jun 7, 2022
Premoli, Marco; Barbato, Michele; Ceselli, Alberto, 2021, "COVID-19 Hospital Resource Management Dataset - Lombardy 2020 - replication data", https://doi.org/10.13130/RD_UNIMI/WWUZIJ, UNIMI Dataverse, V3
This dataset contains realistic instances for the problem "hospital resource management" (HRM), corresponding to the first wave of COVID-19 in Spring 2020 in Lombardy Region (Italy). Refer to file 'SOURCES_FORMULAS_ASSUMPTIONS.pdf' for detailed information about data sources, for... |
Apr 15, 2022
PREMOLI, MARCO LUIGI; Alberto Ceselli; Marco Fiore; Stefano Secci, 2022, "Replication Data for: Optimized Assignment Patterns in Mobile Edge Cloud Networks", https://doi.org/10.13130/RD_UNIMI/X3VG12, UNIMI Dataverse, V1
Replication data for "Optimized assignment patterns in Mobile Edge Cloud networks." Computers & Operations Research 106 (2019): 246-259. The full description of the dataset is given in the README file. |
Jan 11, 2022
Ceselli, Alberto; Casazza, Marco, 2022, "Maximum Lifetime Data Gathering Tree in Wireless Sensors Networks - Dataset", https://doi.org/10.13130/RD_UNIMI/VHW2QV, UNIMI Dataverse, V1, UNF:6:U/L+h6e36wYpccCRTD5/Rg== [fileUNF]
Dataset of instances used in the paper "Exact algorithms for Maximum Lifetime Data Gathering Tree in Wireless Sensors Networks". The repository contains two folders (CCD and ZTD) corresponding to the instances used in the paper. Their full description is given in two README files... |
Jul 27, 2021
Ceselli, Alberto; Létocart, Lucas; Traversi, Emiliano, 2021, "Binary Quadratic Problem decomposition methods - kQKP dataset", https://doi.org/10.13130/RD_UNIMI/3QA23K, UNIMI Dataverse, V1
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 ".... |
