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README.md
In highs: 'HiGHS' Optimization Solver
R HIGHS Interface
Florian Schwendinger
Updated: 2023-01-21
This repository contains an R interface to the
HiGHS solver. The
HiGHS solver, is a
high-performance open-source solver for solving linear
programming (LP), mixed-integer programming (MIP) and quadratic
programming (QP) optimization problems.
1 Installation
The package can be installed from
CRAN
install.packages("highs")
or GitLab.
remotes::install_gitlab("roigrp/solver/highs")
1.0.1 Using a preinstalled HiGHS library
It is possible to use a precompile HiGHS library by providing the system
variable R_HIGHS_LIB_DIR. For example I used
mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=/Z/bin/highslib -DCMAKE_POSITION_INDEPENDENT_CODE:bool=ON -DSHARED:bool=OFF -DBUILD_TESTING:bool=OFF
make install
to install the HiGHS library to /Z/bin/highslib
Sys.setenv(R_HIGHS_LIB_DIR = "/Z/bin/highslib")
install.packages("highs")
# or
# remotes::install_gitlab("roigrp/solver/highs")
2 Basic usage
library("highs")
args(highs_solve)
#> function (Q = NULL, L, lower, upper, A, lhs, rhs, types, maximum = FALSE,
#> offset = 0, control = list(), dry_run = FALSE)
#> NULL
2.1 LP
# Minimize
# x_0 + x_1 + 3
# Subject to
# x_1 <= 7
# 5 <= x_0 + 2 x_1 <= 15
# 6 <= 3 x_0 + 2 x_1
# 0 <= x_0 <= 4
# 1 <= x_1
A <- rbind(c(0, 1), c(1, 2), c(3, 2))
s <- highs_solve(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
A = A, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
offset = 3)
str(s)
#> List of 6
#> $ primal_solution: num [1:2] 0.5 2.25
#> $ objective_value: num 5.75
#> $ status : int 7
#> $ status_message : chr "Optimal"
#> $ solver_msg :List of 6
#> ..$ value_valid: logi TRUE
#> ..$ dual_valid : logi TRUE
#> ..$ col_value : num [1:2] 0.5 2.25
#> ..$ col_dual : num [1:2] 0 0
#> ..$ row_value : num [1:3] 2.25 5 6
#> ..$ row_dual : num [1:3] 0 0.25 0.25
#> $ info :List of 18
#> ..$ valid : logi TRUE
#> ..$ mip_node_count : num -1
#> ..$ simplex_iteration_count : int 2
#> ..$ ipm_iteration_count : int 0
#> ..$ qp_iteration_count : int 0
#> ..$ crossover_iteration_count : int 0
#> ..$ primal_solution_status : chr "Feasible"
#> ..$ dual_solution_status : chr "Feasible"
#> ..$ basis_validity : int 1
#> ..$ objective_function_value : num 5.75
#> ..$ mip_dual_bound : num 0
#> ..$ mip_gap : num Inf
#> ..$ num_primal_infeasibilities: int 0
#> ..$ max_primal_infeasibility : num 0
#> ..$ sum_primal_infeasibilities: num 0
#> ..$ num_dual_infeasibilities : int 0
#> ..$ max_dual_infeasibility : num 0
#> ..$ sum_dual_infeasibilities : num 0
2.2 QP
# Minimize
# 0.5 x^2 - 2 x + y
# Subject to
# x <= 3
zero <- .Machine$double.eps * 100
Q <- rbind(c(1, 0), c(0, zero))
L <- c(-2, 1)
A <- t(c(1, 0))
cntrl <- list(log_dev_level = 0L)
s <- highs_solve(Q = Q, L = L, A = A, lhs = 0, rhs = 3, control = cntrl)
str(s)
#> List of 6
#> $ primal_solution: num [1:2] 2e+00 -1e+07
#> $ objective_value: num -1e+07
#> $ status : int 7
#> $ status_message : chr "Optimal"
#> $ solver_msg :List of 6
#> ..$ value_valid: logi TRUE
#> ..$ dual_valid : logi TRUE
#> ..$ col_value : num [1:2] 2e+00 -1e+07
#> ..$ col_dual : num [1:2] 0 0
#> ..$ row_value : num 2
#> ..$ row_dual : num 0
#> $ info :List of 18
#> ..$ valid : logi TRUE
#> ..$ mip_node_count : num -1
#> ..$ simplex_iteration_count : int 0
#> ..$ ipm_iteration_count : int 0
#> ..$ qp_iteration_count : int 5
#> ..$ crossover_iteration_count : int 0
#> ..$ primal_solution_status : chr "Feasible"
#> ..$ dual_solution_status : chr "Feasible"
#> ..$ basis_validity : int 0
#> ..$ objective_function_value : num -1e+07
#> ..$ mip_dual_bound : num 0
#> ..$ mip_gap : num Inf
#> ..$ num_primal_infeasibilities: int 0
#> ..$ max_primal_infeasibility : num 0
#> ..$ sum_primal_infeasibilities: num 0
#> ..$ num_dual_infeasibilities : int 0
#> ..$ max_dual_infeasibility : num 0
#> ..$ sum_dual_infeasibilities : num 0
3 Additional information
3.1 Sparse matrices
The HiGHs C++ library internally supports the matrix formats csc
(compressed sparse column matrix) and csr (compressed Sparse Row array).
The highs package currently supports the following matrix classes:
"matrix" dense matrices, "dgCMatrix" compressed sparse column matrix from the Matrix
package, "dgRMatrix" compressed sparse row matrix from the Matrix
package, "matrix.csc" compressed sparse column matrix from the SparseM
package, "matrix.csr" compressed sparse row matrix from the SparseM
package, "simple_triplet_matrix" coordinate format from the slam
package.
If the constraint matrix A is provided as dgCMatrix, dgRMatrix,
matrix.csc or matrix.csr the underlying data is directly passed to
HiGHs otherwise it is first transformed into the csc format an
afterwards passed to HiGHs
library("Matrix")
A <- rbind(c(0, 1), c(1, 2), c(3, 2))
csc <- as(A, "CsparseMatrix") # dgCMatrix
s0 <- highs_solve(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
A = csc, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
offset = 3)
csr <- as(A, "RsparseMatrix") # dgRMatrix
s1 <- highs_solve(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
A = csr, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
offset = 3)
library("SparseM")
csc <- as.matrix.csc(A)
s2 <- highs_solve(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
A = csc, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
offset = 3)
csr <- as.matrix.csr(A)
s3 <- highs_solve(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
A = csr, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
offset = 3)
library("slam")
stm <- as.simple_triplet_matrix(A)
s4 <- highs_solve(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
A = stm, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
offset = 3)
4 Options
The function highs_available_solver_options lists the available solver
options
d <- highs_available_solver_options()
d[["option"]] <- sprintf("`%s`", d[["option"]])
knitr::kable(d, row.names = FALSE)
| option | type | category |
| :---------------------------------------------- | :------ | :------- |
| allow_unbounded_or_infeasible | bool | advanced |
| allowed_cost_scale_factor | integer | advanced |
| allowed_matrix_scale_factor | integer | advanced |
| cost_scale_factor | integer | advanced |
| dual_simplex_cost_perturbation_multiplier | double | advanced |
| dual_simplex_pivot_growth_tolerance | double | advanced |
| dual_steepest_edge_weight_error_tolerance | double | advanced |
| dual_steepest_edge_weight_log_error_threshold | double | advanced |
| factor_pivot_threshold | double | advanced |
| factor_pivot_tolerance | double | advanced |
| keep_n_rows | integer | advanced |
| less_infeasible_DSE_check | bool | advanced |
| less_infeasible_DSE_choose_row | bool | advanced |
| log_dev_level | integer | advanced |
| lp_presolve_requires_basis_postsolve | bool | advanced |
| max_dual_simplex_cleanup_level | integer | advanced |
| max_dual_simplex_phase1_cleanup_level | integer | advanced |
| mps_parser_type_free | bool | advanced |
| no_unnecessary_rebuild_refactor | bool | advanced |
| presolve_pivot_threshold | double | advanced |
| presolve_rule_logging | bool | advanced |
| presolve_rule_off | integer | advanced |
| presolve_substitution_maxfillin | integer | advanced |
| primal_simplex_bound_perturbation_multiplier | double | advanced |
| rebuild_refactor_solution_error_tolerance | double | advanced |
| run_crossover | bool | advanced |
| simplex_dualise_strategy | integer | advanced |
| simplex_initial_condition_check | bool | advanced |
| simplex_initial_condition_tolerance | double | advanced |
| simplex_permute_strategy | integer | advanced |
| simplex_price_strategy | integer | advanced |
| simplex_unscaled_solution_strategy | integer | advanced |
| start_crossover_tolerance | double | advanced |
| use_implied_bounds_from_presolve | bool | advanced |
| use_original_HFactor_logic | bool | advanced |
| dual_feasibility_tolerance | double | file |
| glpsol_cost_row_location | integer | file |
| highs_analysis_level | integer | file |
| highs_debug_level | integer | file |
| infinite_bound | double | file |
| infinite_cost | double | file |
| ipm_iteration_limit | integer | file |
| ipm_optimality_tolerance | double | file |
| large_matrix_value | double | file |
| log_file | string | file |
| objective_bound | double | file |
| objective_target | double | file |
| primal_feasibility_tolerance | double | file |
| random_seed | integer | file |
| simplex_crash_strategy | integer | file |
| simplex_dual_edge_weight_strategy | integer | file |
| simplex_iteration_limit | integer | file |
| simplex_max_concurrency | integer | file |
| simplex_min_concurrency | integer | file |
| simplex_primal_edge_weight_strategy | integer | file |
| simplex_scale_strategy | integer | file |
| simplex_strategy | integer | file |
| simplex_update_limit | integer | file |
| small_matrix_value | double | file |
| solution_file | string | file |
| threads | integer | file |
| write_model_file | string | file |
| write_model_to_file | bool | file |
| write_solution_style | integer | file |
| write_solution_to_file | bool | file |
| icrash | bool | icrash |
| icrash_approx_iter | integer | icrash |
| icrash_breakpoints | bool | icrash |
| icrash_dualize | bool | icrash |
| icrash_exact | bool | icrash |
| icrash_iterations | integer | icrash |
| icrash_starting_weight | double | icrash |
| icrash_strategy | string | icrash |
| log_to_console | bool | logging |
| output_flag | bool | logging |
| mip_abs_gap | double | mip |
| mip_detect_symmetry | bool | mip |
| mip_feasibility_tolerance | double | mip |
| mip_heuristic_effort | double | mip |
| mip_lp_age_limit | integer | mip |
| mip_max_improving_sols | integer | mip |
| mip_max_leaves | integer | mip |
| mip_max_nodes | integer | mip |
| mip_max_stall_nodes | integer | mip |
| mip_min_cliquetable_entries_for_parallelism | integer | mip |
| mip_pool_age_limit | integer | mip |
| mip_pool_soft_limit | integer | mip |
| mip_pscost_minreliable | integer | mip |
| mip_rel_gap | double | mip |
| mip_report_level | integer | mip |
| parallel | string | run-time |
| presolve | string | run-time |
| ranging | string | run-time |
| solver | string | run-time |
| time_limit | double | run-time |
for additional information see the HiGHS homepage.
5 Status codes
HiGHS currently has the following status codes defined in HConst.h".
| enumerator | status | message |
| :----------------------- | -----: | :--------------------------------- |
| kNotset | 0 | "Not Set" |
| kLoadError | 1 | "Load error" |
| kModelError | 2 | "Model error" |
| kPresolveError | 3 | "Presolve error" |
| kSolveError | 4 | "Solve error" |
| kPostsolveError | 5 | "Postsolve error" |
| kModelEmpty | 6 | "Empty" |
| kOptimal | 7 | "Optimal" |
| kInfeasible | 8 | "Infeasible" |
| kUnboundedOrInfeasible | 9 | "Primal infeasible or unbounded" |
| kUnbounded | 10 | "Unbounded" |
| kObjectiveBound | 11 | "Bound on objective reached" |
| kObjectiveTarget | 12 | "Target for objective reached" |
| kTimeLimit | 13 | "Time limit reached" |
| kIterationLimit | 14 | "Iteration limit reached" |
| kUnknown | 15 | "Unknown" |
| kMin | 0 | "Not Set" |
| kMax | 15 | "Unknown" |
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R HIGHS Interface
Florian Schwendinger Updated: 2023-01-21
This repository contains an R interface to the HiGHS solver. The HiGHS solver, is a high-performance open-source solver for solving linear programming (LP), mixed-integer programming (MIP) and quadratic programming (QP) optimization problems.
1 Installation
The package can be installed from CRAN
install.packages("highs")
or GitLab.
remotes::install_gitlab("roigrp/solver/highs")
1.0.1 Using a preinstalled HiGHS library
It is possible to use a precompile HiGHS library by providing the system
variable R_HIGHS_LIB_DIR. For example I used
mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=/Z/bin/highslib -DCMAKE_POSITION_INDEPENDENT_CODE:bool=ON -DSHARED:bool=OFF -DBUILD_TESTING:bool=OFF
make install
to install the HiGHS library to /Z/bin/highslib
Sys.setenv(R_HIGHS_LIB_DIR = "/Z/bin/highslib")
install.packages("highs")
# or
# remotes::install_gitlab("roigrp/solver/highs")
2 Basic usage
library("highs")
args(highs_solve)
#> function (Q = NULL, L, lower, upper, A, lhs, rhs, types, maximum = FALSE,
#> offset = 0, control = list(), dry_run = FALSE)
#> NULL
2.1 LP
# Minimize
# x_0 + x_1 + 3
# Subject to
# x_1 <= 7
# 5 <= x_0 + 2 x_1 <= 15
# 6 <= 3 x_0 + 2 x_1
# 0 <= x_0 <= 4
# 1 <= x_1
A <- rbind(c(0, 1), c(1, 2), c(3, 2))
s <- highs_solve(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
A = A, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
offset = 3)
str(s)
#> List of 6
#> $ primal_solution: num [1:2] 0.5 2.25
#> $ objective_value: num 5.75
#> $ status : int 7
#> $ status_message : chr "Optimal"
#> $ solver_msg :List of 6
#> ..$ value_valid: logi TRUE
#> ..$ dual_valid : logi TRUE
#> ..$ col_value : num [1:2] 0.5 2.25
#> ..$ col_dual : num [1:2] 0 0
#> ..$ row_value : num [1:3] 2.25 5 6
#> ..$ row_dual : num [1:3] 0 0.25 0.25
#> $ info :List of 18
#> ..$ valid : logi TRUE
#> ..$ mip_node_count : num -1
#> ..$ simplex_iteration_count : int 2
#> ..$ ipm_iteration_count : int 0
#> ..$ qp_iteration_count : int 0
#> ..$ crossover_iteration_count : int 0
#> ..$ primal_solution_status : chr "Feasible"
#> ..$ dual_solution_status : chr "Feasible"
#> ..$ basis_validity : int 1
#> ..$ objective_function_value : num 5.75
#> ..$ mip_dual_bound : num 0
#> ..$ mip_gap : num Inf
#> ..$ num_primal_infeasibilities: int 0
#> ..$ max_primal_infeasibility : num 0
#> ..$ sum_primal_infeasibilities: num 0
#> ..$ num_dual_infeasibilities : int 0
#> ..$ max_dual_infeasibility : num 0
#> ..$ sum_dual_infeasibilities : num 0
2.2 QP
# Minimize
# 0.5 x^2 - 2 x + y
# Subject to
# x <= 3
zero <- .Machine$double.eps * 100
Q <- rbind(c(1, 0), c(0, zero))
L <- c(-2, 1)
A <- t(c(1, 0))
cntrl <- list(log_dev_level = 0L)
s <- highs_solve(Q = Q, L = L, A = A, lhs = 0, rhs = 3, control = cntrl)
str(s)
#> List of 6
#> $ primal_solution: num [1:2] 2e+00 -1e+07
#> $ objective_value: num -1e+07
#> $ status : int 7
#> $ status_message : chr "Optimal"
#> $ solver_msg :List of 6
#> ..$ value_valid: logi TRUE
#> ..$ dual_valid : logi TRUE
#> ..$ col_value : num [1:2] 2e+00 -1e+07
#> ..$ col_dual : num [1:2] 0 0
#> ..$ row_value : num 2
#> ..$ row_dual : num 0
#> $ info :List of 18
#> ..$ valid : logi TRUE
#> ..$ mip_node_count : num -1
#> ..$ simplex_iteration_count : int 0
#> ..$ ipm_iteration_count : int 0
#> ..$ qp_iteration_count : int 5
#> ..$ crossover_iteration_count : int 0
#> ..$ primal_solution_status : chr "Feasible"
#> ..$ dual_solution_status : chr "Feasible"
#> ..$ basis_validity : int 0
#> ..$ objective_function_value : num -1e+07
#> ..$ mip_dual_bound : num 0
#> ..$ mip_gap : num Inf
#> ..$ num_primal_infeasibilities: int 0
#> ..$ max_primal_infeasibility : num 0
#> ..$ sum_primal_infeasibilities: num 0
#> ..$ num_dual_infeasibilities : int 0
#> ..$ max_dual_infeasibility : num 0
#> ..$ sum_dual_infeasibilities : num 0
3 Additional information
3.1 Sparse matrices
The HiGHs C++ library internally supports the matrix formats csc (compressed sparse column matrix) and csr (compressed Sparse Row array). The highs package currently supports the following matrix classes:
"matrix"dense matrices,"dgCMatrix"compressed sparse column matrix from the Matrix package,"dgRMatrix"compressed sparse row matrix from the Matrix package,"matrix.csc"compressed sparse column matrix from the SparseM package,"matrix.csr"compressed sparse row matrix from the SparseM package,"simple_triplet_matrix"coordinate format from the slam package.
If the constraint matrix A is provided as dgCMatrix, dgRMatrix,
matrix.csc or matrix.csr the underlying data is directly passed to
HiGHs otherwise it is first transformed into the csc format an
afterwards passed to HiGHs
library("Matrix")
A <- rbind(c(0, 1), c(1, 2), c(3, 2))
csc <- as(A, "CsparseMatrix") # dgCMatrix
s0 <- highs_solve(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
A = csc, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
offset = 3)
csr <- as(A, "RsparseMatrix") # dgRMatrix
s1 <- highs_solve(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
A = csr, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
offset = 3)
library("SparseM")
csc <- as.matrix.csc(A)
s2 <- highs_solve(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
A = csc, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
offset = 3)
csr <- as.matrix.csr(A)
s3 <- highs_solve(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
A = csr, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
offset = 3)
library("slam")
stm <- as.simple_triplet_matrix(A)
s4 <- highs_solve(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
A = stm, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
offset = 3)
4 Options
The function highs_available_solver_options lists the available solver
options
d <- highs_available_solver_options()
d[["option"]] <- sprintf("`%s`", d[["option"]])
knitr::kable(d, row.names = FALSE)
| option | type | category |
| :---------------------------------------------- | :------ | :------- |
| allow_unbounded_or_infeasible | bool | advanced |
| allowed_cost_scale_factor | integer | advanced |
| allowed_matrix_scale_factor | integer | advanced |
| cost_scale_factor | integer | advanced |
| dual_simplex_cost_perturbation_multiplier | double | advanced |
| dual_simplex_pivot_growth_tolerance | double | advanced |
| dual_steepest_edge_weight_error_tolerance | double | advanced |
| dual_steepest_edge_weight_log_error_threshold | double | advanced |
| factor_pivot_threshold | double | advanced |
| factor_pivot_tolerance | double | advanced |
| keep_n_rows | integer | advanced |
| less_infeasible_DSE_check | bool | advanced |
| less_infeasible_DSE_choose_row | bool | advanced |
| log_dev_level | integer | advanced |
| lp_presolve_requires_basis_postsolve | bool | advanced |
| max_dual_simplex_cleanup_level | integer | advanced |
| max_dual_simplex_phase1_cleanup_level | integer | advanced |
| mps_parser_type_free | bool | advanced |
| no_unnecessary_rebuild_refactor | bool | advanced |
| presolve_pivot_threshold | double | advanced |
| presolve_rule_logging | bool | advanced |
| presolve_rule_off | integer | advanced |
| presolve_substitution_maxfillin | integer | advanced |
| primal_simplex_bound_perturbation_multiplier | double | advanced |
| rebuild_refactor_solution_error_tolerance | double | advanced |
| run_crossover | bool | advanced |
| simplex_dualise_strategy | integer | advanced |
| simplex_initial_condition_check | bool | advanced |
| simplex_initial_condition_tolerance | double | advanced |
| simplex_permute_strategy | integer | advanced |
| simplex_price_strategy | integer | advanced |
| simplex_unscaled_solution_strategy | integer | advanced |
| start_crossover_tolerance | double | advanced |
| use_implied_bounds_from_presolve | bool | advanced |
| use_original_HFactor_logic | bool | advanced |
| dual_feasibility_tolerance | double | file |
| glpsol_cost_row_location | integer | file |
| highs_analysis_level | integer | file |
| highs_debug_level | integer | file |
| infinite_bound | double | file |
| infinite_cost | double | file |
| ipm_iteration_limit | integer | file |
| ipm_optimality_tolerance | double | file |
| large_matrix_value | double | file |
| log_file | string | file |
| objective_bound | double | file |
| objective_target | double | file |
| primal_feasibility_tolerance | double | file |
| random_seed | integer | file |
| simplex_crash_strategy | integer | file |
| simplex_dual_edge_weight_strategy | integer | file |
| simplex_iteration_limit | integer | file |
| simplex_max_concurrency | integer | file |
| simplex_min_concurrency | integer | file |
| simplex_primal_edge_weight_strategy | integer | file |
| simplex_scale_strategy | integer | file |
| simplex_strategy | integer | file |
| simplex_update_limit | integer | file |
| small_matrix_value | double | file |
| solution_file | string | file |
| threads | integer | file |
| write_model_file | string | file |
| write_model_to_file | bool | file |
| write_solution_style | integer | file |
| write_solution_to_file | bool | file |
| icrash | bool | icrash |
| icrash_approx_iter | integer | icrash |
| icrash_breakpoints | bool | icrash |
| icrash_dualize | bool | icrash |
| icrash_exact | bool | icrash |
| icrash_iterations | integer | icrash |
| icrash_starting_weight | double | icrash |
| icrash_strategy | string | icrash |
| log_to_console | bool | logging |
| output_flag | bool | logging |
| mip_abs_gap | double | mip |
| mip_detect_symmetry | bool | mip |
| mip_feasibility_tolerance | double | mip |
| mip_heuristic_effort | double | mip |
| mip_lp_age_limit | integer | mip |
| mip_max_improving_sols | integer | mip |
| mip_max_leaves | integer | mip |
| mip_max_nodes | integer | mip |
| mip_max_stall_nodes | integer | mip |
| mip_min_cliquetable_entries_for_parallelism | integer | mip |
| mip_pool_age_limit | integer | mip |
| mip_pool_soft_limit | integer | mip |
| mip_pscost_minreliable | integer | mip |
| mip_rel_gap | double | mip |
| mip_report_level | integer | mip |
| parallel | string | run-time |
| presolve | string | run-time |
| ranging | string | run-time |
| solver | string | run-time |
| time_limit | double | run-time |
for additional information see the HiGHS homepage.
5 Status codes
HiGHS currently has the following status codes defined in HConst.h".
| enumerator | status | message |
| :----------------------- | -----: | :--------------------------------- |
| kNotset | 0 | "Not Set" |
| kLoadError | 1 | "Load error" |
| kModelError | 2 | "Model error" |
| kPresolveError | 3 | "Presolve error" |
| kSolveError | 4 | "Solve error" |
| kPostsolveError | 5 | "Postsolve error" |
| kModelEmpty | 6 | "Empty" |
| kOptimal | 7 | "Optimal" |
| kInfeasible | 8 | "Infeasible" |
| kUnboundedOrInfeasible | 9 | "Primal infeasible or unbounded" |
| kUnbounded | 10 | "Unbounded" |
| kObjectiveBound | 11 | "Bound on objective reached" |
| kObjectiveTarget | 12 | "Target for objective reached" |
| kTimeLimit | 13 | "Time limit reached" |
| kIterationLimit | 14 | "Iteration limit reached" |
| kUnknown | 15 | "Unknown" |
| kMin | 0 | "Not Set" |
| kMax | 15 | "Unknown" |
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