r - Linear programming: not all constraints should be satisfied -


using r, easy linear programming lpsolve , lpsolveapi packages. now, want linear programming part of constraints satisfied. say, 80% of constraints satisfied. can not predefine constraints should satisfied. how implement using r?

an example here. there 100 different tissue samples, each sample, 10000 ~ 20000 genes expressed. now, want select @ 10 samples purpose cover many genes possible. 1 solution based on lpsolve set 100 samples variables , 20000 genes constraints. first, manually filter genes, , lp, until solution hit 10 samples. however, laborious , not optimal.

set.seed(123) t.mt <- matrix(sample(c(0,1),100000,rep=true),nr=1000) f.obj <- rep(1,100) f.dir <- rep(">=",1000) f.rhs <- rep(1,1000) t.lp <- lp("min",f.obj,t.mt,f.dir,f.rhs,all.int=true,all.bin=true) t.lp$solution

is there possible solution using r problem?

thank you!


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