For a tutorial on usage see the folder 'Tutorial'
Program arguments: usage: ILP.py [-h] -e EXTANT -g GREEDY -o OUTPUT [-c Q_CON] [-m Q_MOD] [-t TIMELIMIT] [-n NUMCORES] [-s NUMSOLUTIONS] [-p POOLMODE] [-f FOCUS] [-r HEURISTICS]
ILP to reconstruct maximum likelihood network evolution history using Duplication Mutation with Complementarity (DMC) model. Uses Gurobi solver, see http://www.gurobi.com/documentation/8.0/refman/index.html
Required arguments: -e EXTANT, --extant EXTANT Extant filename. Format: one edge per line -g GREEDY, --greedy GREEDY Filename of Solution from Greedy Approach ReverseDMC -o OUTPUT, --output OUTPUT Output Filename
Optional arguments: -h, --help show this help message and exit -c Q_CON, --q_con Q_CON DMC Model Parameter q_con (default: 0.7) -m Q_MOD, --q_mod Q_MOD DMC Model Parameter q_mod (default: 0.4) -t TIMELIMIT, --timelimit TIMELIMIT Time Limit in hours (default: 24) -n NUMCORES, --numcores NUMCORES Number of cores. 0 uses all available cores (default: 0) -s NUMSOLUTIONS, --numsolutions NUMSOLUTIONS Number of ILP Solutions (default: 30) -p POOLMODE, --poolmode POOLMODE Pool mode, possible values 0,1,2 (default: 1). 0: finds one optimal solution. 1: find multiple solutions not necessarily the best. 2: find n best multiple solutions. -f FOCUS, --focus FOCUS MIP focus, possible values 1,2,3 (default: 1). 1: finds feasible solutions quickly. 2: to prove optimality, if good quality solutions can be found easily. 3: if the best objective bound is moving very slowly or not at all. -r HEURISTICS, --heuristics HEURISTICS Fraction of time spent on MIP heuristics, between 0 and 1 (default: 0.5). Larger values produce more and better feasible solutions, at a cost of slower progress in the best bound.