The Solver API

While highs_solve() is convenient for one-shot problems, the solver API gives fine-grained control for building models incrementally, modifying problems, and performing warm-starts.

Creating a Solver

Build a model and create a solver from it:

library(highs)

model <- highs_model(
  L = c(2, 4, 3),
  lower = 0,
  A = matrix(c(3, 4, 2, 2, 1, 2, 1, 3, 2), nrow = 3, byrow = TRUE),
  rhs = c(60, 40, 80),
  maximum = TRUE
)
solver <- hi_new_solver(model)

Solving and Retrieving Results

hi_solver_run(solver)
#> [1] 0
hi_solver_status_message(solver)
#> [1] "Optimal"
sol <- hi_solver_get_solution(solver)
sol$col_value
#> [1]  0.000000  6.666667 16.666667

Solver Information

After solving, retrieve detailed solver information:

info <- hi_solver_info(solver)
info$objective_function_value
#> [1] 76.66667
info$simplex_iteration_count
#> [1] 2
info$primal_solution_status
#> [1] "Feasible"

Modifying the Problem

Change objective coefficients, bounds, and constraints without rebuilding from scratch:

# Change the objective coefficient for variable 1 (0-based indexing)
hi_solver_set_objective(solver, index = 0L, coeff = 10.0)
#> [1] 0

# Change variable bounds
hi_solver_set_variable_bounds(solver, index = 0L, lower = 1.0, upper = 20.0)
#> [1] 0

# Change constraint bounds
hi_solver_set_constraint_bounds(solver, index = 0L, lower = -Inf, upper = 50.0)
#> [1] 0

# Re-solve
hi_solver_run(solver)
#> [1] 0
hi_solver_get_solution(solver)$col_value
#> [1] 16.66667  0.00000  0.00000

Adding Variables and Constraints

Grow the model dynamically:

solver <- hi_new_solver(model)

# Add a new variable with bounds [0, 15]
hi_solver_add_vars(solver, lower = 0, upper = 15)
#> [1] 0
cat("Columns after add:", hi_solver_get_num_col(solver), "\n")
#> Columns after add: 4

# Add a new constraint: x1 + x2 + x3 + x4 <= 100
hi_solver_add_rows(solver,
  lhs = -Inf, rhs = 100,
  start = 0L,
  index = 0L:3L,
  value = rep(1.0, 4)
)
#> [1] 0
cat("Rows after add:", hi_solver_get_num_row(solver), "\n")
#> Rows after add: 4

Querying Model Data

solver <- hi_new_solver(model)
hi_solver_get_lp_costs(solver)
#> [1] 2 4 3
hi_solver_get_num_col(solver)
#> [1] 3
hi_solver_get_num_row(solver)
#> [1] 3

The `highs_solver()` Wrapper

For an object-oriented style, use highs_solver() which returns an environment with methods:

hw <- highs_solver(model)
hw$solve()
#> ERROR:   getOptionIndex: Option "pdlp_features_off" is unknown
#> [1] 0
hw$status_message()
#> [1] "Optimal"
hw$solution()$col_value
#> [1]  0.000000  6.666667 16.666667
hw$info()$objective_function_value
#> [1] 76.66667

# Modify and re-solve
hw$L(1, 10.0)     # set cost of variable 1 to 10
#> [1] 0
hw$vbounds(1, 1, 20)  # set bounds of variable 1 to [1, 20]
#> [1] 0
hw$solve()
#> ERROR:   getOptionIndex: Option "pdlp_features_off" is unknown
#> [1] 0
hw$solution()$col_value
#> [1] 20  0  0

Reading and Writing Models

solver <- hi_new_solver(model)
temp <- tempfile(fileext = ".mps")
hi_solver_write_model(solver, temp)
#> WARNING: Column names are not present, or contain duplicates: using names with prefix "c", beginning with suffix 0
#> 
#> WARNING: Row    names are not present, or contain duplicates: using names with prefix "r", beginning with suffix 0
#> 
#> Writing the model to /tmp/RtmpRAr2Nn/file53de732c74f61.mps
#> [1] 1

# Read into a fresh solver
solver2 <- hi_new_solver(highs_model(L = 0))
hi_solver_read_model(solver2, temp)
#> [1] 0
hi_solver_run(solver2)
#> [1] 0
hi_solver_get_solution(solver2)$col_value
#> [1]  0.000000  6.666667 16.666667
unlink(temp)

Version and Timing

solver <- hi_new_solver(model)
hi_solver_version(solver)
#> [1] "1.14.0"
hi_solver_run(solver)
#> [1] 0
hi_solver_get_run_time(solver)
#> [1] 0.000241518