Specify the software and configuration used to solve a project prioritization
problem(). By default, the best available exact algorithm
solver will be used.
The following solvers can be used to find solutions for a
This solver uses the best software currently installed on the system.
Gurobi is a state-of-the-art commercial optimization software with an R package interface. It is by far the fastest solver that can be used by this package, however, it is also the only solver that is not freely available. That said, licenses are available to academics at no cost. The gurobi package is distributed with the Gurobi software suite. This solver uses the gurobi package to solve problems.
SYMPHONY is an open-source integer programming solver that is part of the Computational Infrastructure for Operations Research (COIN-OR) project, an initiative to promote development of open-source tools for operations research (a field that includes linear programming). The Rsymphony package provides an interface to COIN-OR and is available on CRAN. This solver uses the Rsymphony package to solve problems.
The lpsymphony package provides a different interface to the COIN-OR software suite. Unlike the Rsymhpony package, the lpsymphony package is distributed through Bioconductor. The lpsymphony package may be easier to install on Windows or Max OSX systems than the Rsymphony package.
lp_solve is an open-source integer programming solver. The lpSolveAPI package provides an interface to this solver and is available on CRAN. Although this solver is the slowest currently supported solver, it is also the only exact algorithm solver that can be installed on all operating systems without any manual installation steps.
Generate solutions using a backwards heuristic algorithm. Although these types of algorithms have conventionally been used to solve project prioritization problems, they are extremely unlikely to identify optimal solutions and provide no guarantees concerning solution quality.
Generate solutions by
randomly funding actions. This can be useful when evaluating the
performance of a funding scheme—though it is strongly recommended
to evaluate the performance of a funding scheme by comparing it
to an optimal solution identified using exact algorithms (e.g.
# load data data(sim_projects, sim_features, sim_actions) # build problem p1 <- problem(sim_projects, sim_actions, sim_features, "name", "success", "name", "cost", "name") %>% add_max_richness_objective(budget = 200) %>% add_binary_decisions() # build another problem, with the default solver p2 <- p1 %>% add_default_solver() # build another problem, with the gurobi solver ## Not run: p3 <- p1 %>% add_gurobi_solver() ## End(Not run) # build another problem, with the Rsympony solver ## Not run: p4 <- p1 %>% add_rsymphony_solver() ## End(Not run) # build another problem, with the lpsymphony solver ## Not run: p5 <- p1 %>% add_lpsymphony_solver() ## End(Not run) # build another problem, with the lpSolveAPI solver p6 <- p1 %>% add_lpsolveapi_solver() # build another problem, with the heuristic solver p7 <- p1 %>% add_heuristic_solver() # build another problem, with the random solver p8 <- p1 %>% add_random_solver() ## Not run: # generate solutions using each of the solvers s <- rbind(solve(p2), solve(p3), solve(p4), solve(p5), solve(p6), solve(p7), solve(p8)) s$solver <- c("default", "gurobi", "Rsymphony", "lpsymphony", "lpSolveAPI", "heuristic", "random") # print solutions print(as.data.frame(s)) ## End(Not run)
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