The Minkowski sum problem (MSP) over \(S\) subsets/subproblems can be stated as \[\min\{y \mid y \in Y = \oplus_{s \in \{1,\ldots,S\}} Y^s \}\] Each subset \(Y^s\) contain a set of nondominated vectors with \(p\) objectives. Note, the MSP is multi-objective in nature, since a solution to MSP is the nondominated set \(Y_N\) (the nondominated sum), of the Minkowski sum \(Y\).
For source code and further results see this repository.
To cite this repository use
@Electronic{MOrepo-Lyngesen24,
Title = {Minkowski sum problems (MOrepo-Lyngesen24)},
Author = {M. Lyngesen and L. R. Nielsen},
Url = {https://github.com/MCDMSociety/MOrepo-Lyngesen24},
Year = {2024},
Note = {Instance and result files at MOrepo.}
}
To cite the Multi-Objective Optimization Repository use
@Electronic{MOrepo,
Title = {Multi-Objective Optimization Repository (MOrepo)},
Author = {L. R. Nielsen},
Url = {https://github.com/MCDMSociety/MOrepo},
Year = {2017},
}
Since an MSP instance is defined by a set of subsets \(Y^s\), \(s = 1,\ldots,S\), we first have to generate subsets.
Each subset/subproblem is generated using either method l, u or m:
In total 600 subset instances was generated. A subset instance is
named
Lyngesen24-sp-<p>-<subset size>-<method>_<id>.json
and stored in the sp
folder. The json file is structured
like e.g. ´Lyngesen24-sp-2-10-m_1.json`:
{
"points": [
{
"z1": 1813,
"z2": 8622,
"cls": "us"
},
{
"z1": 5997,
"z2": 3449,
"cls": "us"
},
...
{
"z1": 9922,
"z2": 1059,
"cls": "se"
},
{
"z1": 3078,
"z2": 7170,
"cls": "us"
}
],
"statistics": {
"p": [2],
"card": [10],
"supported": [4],
"extreme": [4],
"unsupported": [6],
"min": [1599, 1059],
"max": [9922, 8734],
"width": [8323, 7675],
"method": ["m"]
}
}
The cls
entry contains strings us
(unsupported), se
(supported extreme) and sne
(supported non-extreme). We use the R package gMOIP
to
generate subproblems. A subproblem is generated such that all
nondominated points is integer and in the hypercube \([0, 10000]^p\). Ten instances were
generated for each \(p=2,\ldots, 5\),
subset size 10, 50, 100, 200, 300 and method u, l and m. For further
details see this report.
An MSP instances is defined by a set of subsets \(Y^s\), \(s = 1,\ldots,S\). Instances are named
Lyngesen24-msp-<objectives>-<subset 1 size>|...|<subset S size>-<subset 1 gen method>...<subset S gen method>-S_<id>.json
.
The json file is structured like e.g. ´Lyngesen24-msp-4-200|200|200|200|200-lllll-5_1.json`:
[
{
"V1": "subproblems/sp-4-200-l_6.json",
"V2": "subproblems/sp-4-200-l_10.json",
"V3": "subproblems/sp-4-200-l_3.json",
"V4": "subproblems/sp-4-200-l_4.json",
"V5": "subproblems/sp-4-200-l_5.json"
}
]
Five instances for each objective \(p=2,\ldots, 5\), number of subsets \(S = 2, \ldots 5\), subset size 50, 100, 200, 300 and method u, l, m and lu are generated, resulting in 1280 MSP instances in total. For further details see this report.
The results
folder contains subfolders with the non
dominated sum (files end with _Yn
). Under the
misc
entry the sizes of the generators are included in
entry genSize
. For further details see this report.