Dataset

Costvector
 


〈−1,1,1,1〉

〈0,1,2,1〉

〈0,2,3,1〉



k
_{
start
}
→k
_{
A
}

\(\boldsymbol {o \rightarrow o_{k_{A}}}\)

#A

k
_{
start
}
→k
_{
A
}

\(\boldsymbol {o \rightarrow o_{k_{A}}}\)

#A

k
_{
start
}
→k
_{
A
}

\(\boldsymbol {o \rightarrow o_{k_{A}}}\)

#A


SFC

7→6

6→7

16

7→6

21→22

16

7→5

31→35

12

RH

6→5

8→12

16

6→5

43→48

192

6→5

62→68

48

COG3715

13→12

10→11

288

22→6

51→176

6

22→6

80→206

2

COG4964

22→4

20→208

30

13→12

33→34

288

13→12

49→50

288

 For some datasets (SFC, RH, COG3715 and, COG4964), the number of optimal timefeasible solutions is zero when reconciliations are obtained by using a given cost vector and unbounded k. After identifying k
_{
start
} (minimum k whose optimal cost o is equal to the optimal cost obtained for unbounded k), we decremented k until k
_{
A
} (maximum k which generates acyclic solutions) is found. For each pair (dataset, cost vector), the following values are given: the decrement of the bound (from k
_{
start
} to k
_{
A
}), the new optimum found (from o to o
_{
A
}) and the new number of acyclic solutions (#
A).