r,linear-programming,lpsolve,absolute-value,lpsolveapi

To model |x| in an LP, you typically create two new variables, x^- and x^+. Constrain them both to be nonnegative: x^-, x^+ >= 0 Then each time you have x in your model, replace it with x^+ - x^-, and each time you have |x|, replace it with x^+...

You should call this script using sh ccc This information is also contained in file demo/readme.txt: To build the program under Linux/Unix, use sh ccc ...

You will need this file: lp_solve_5.5.2.0_Python2.5_exe_ux64.tar.gz Inside it you will find: liblpsolve55.so You need to put that file in a place accessible to the python path. Had problems doing that so it's in the project's folder. You also need this file: lp_solve_5.5.2.0_dev_ux64.tar.gz Inside it you will find: liblpsolve55.so This file...

linear-programming,ampl,lpsolve

You should put (a link to) the liblpsolve55.so somewhere on the library search paths, for example /usr/lib: $ sudo ln -s /usr/lib/lp_solve/liblpsolve55.so /usr/lib Also make sure that you have 32-bit (x86) version of liblpsolve55.so installed. For example, on 64-bit Ubuntu you can install 32-bit version of liblpsolve55.so as follows: $...

r,mathematical-optimization,lpsolve,lpsolveapi

I don't think you can mix between these two packages (lpSolveAPI doesn't import or depend on lpSolve). Consider a simple LP in lpSolve: library(lpSolve) costs <- c(1, 2) mat <- diag(2) dirs <- rep(">=", 2) rhs <- c(1, 1) x1 = lp("min", costs, mat, dirs, rhs) x1 # Success: the...

r,optimization,solver,maximize,lpsolve

What if you added something similar to the way you did the roles to con? If you add t(model.matrix(~ Team + 0, DF)) you'll have indicators for each team in your constraint. For the example you gave: > con <- rbind(t(model.matrix(~ Role + 0,DF)), t(model.matrix(~ Team + 0, DF)), rep(1,nrow(DF)),...