optimization - Constraining on a Minimum of Distinct Values with PuLP -
i've been using pulp library
side project (daily fantasy sports) optimize projected value of lineup based on series of constraints.
i've implemented of them, 1 constraint players must come @ least 3 separate teams.
this paper has implementation (page 18, 4.2), i've attached image:
it seems somehow derive indicator variable each team that's 1 if given team has @ least 1 player in lineup, , constrains sum of indicators greater or equal 3.
does know how implemented in pulp
?
similar examples helpful.
any assistance super appreciated!
in case define binary variable t
sets upper limit of x
variables. in python don't name variables single letter have nothing else go on here how in pulp.
assume variables lineups
, players
, players_by_team
, teams
set somewhere else
x_index = [i,p in lineups p in players] t_index = [i,t in lineups t in teams] x = lpvariable.dicts("x", x_index, lowbound=0) t = lpvariable.dicts("t", t_index, cat=lpbinary) l in teams: prob += t[i,l] <=lpsum([x[i,k] k in players_by_team[l]]) prob += lpsum([t[i,l] l in teams]) >= 3
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