Nothing
README.md
In highs: 'HiGHS' Optimization Solver
R HIGHS Interface
Florian Schwendinger
Updated: 2025-04-20
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 Package
The highs package provides an API similar to Rglpk and a low
level API to the HiGHS solver. For most users using highs_solve as
shown below should be the best choice.
The package functions can be grouped into the following categories:
- The main function
highs_solve.
- Object style wrappers for the model and the solver
highs_model
and highs_solver.
- Function
highs_control to construct the control object for
highs_solveand highs_solver.
- Low-level API wrapper functions to create and modify models
hi_new_model and other functions starting with
hi_model_.
- Low-level API wrapper functions to create and modify solvers
hi_new_solver and other functions starting with
hi_solver_.
- Functions to create example models
example_model and solvers
example_solver for the documentation examples.
- Function
highs_available_solver_options to get the available
solver options.
- Function
highs_write_model to write the model to a file.
library("highs")
2.1 Examples
The the example models and solvers are included to have small examples
available for the manual.
writeLines(ls("package:highs", pattern = "^example"))
#> example_model
#> example_solver
2.2 Low level model functions
The low-level model functions allow to create and modify models. More
details and examples can be found in the manual.
writeLines(ls("package:highs", pattern = "^hi(|_new)_model"))
#> hi_model_get_ncons
#> hi_model_get_nvars
#> hi_model_set_constraint_matrix
#> hi_model_set_hessian
#> hi_model_set_lhs
#> hi_model_set_lower
#> hi_model_set_ncol
#> hi_model_set_nrow
#> hi_model_set_objective
#> hi_model_set_offset
#> hi_model_set_rhs
#> hi_model_set_sense
#> hi_model_set_upper
#> hi_model_set_vartype
#> hi_new_model
2.3 Low level solver functions
The low-level solver functions allow to create and modify solvers. More
details and examples can be found in the manual.
writeLines(ls("package:highs", pattern = "^hi(|_new)_solver"))
#> hi_new_solver
#> hi_solver_add_cols
#> hi_solver_add_rows
#> hi_solver_add_vars
#> hi_solver_change_constraint_bounds
#> hi_solver_change_variable_bounds
#> hi_solver_clear
#> hi_solver_clear_model
#> hi_solver_clear_solver
#> hi_solver_get_bool_option
#> hi_solver_get_constraint_bounds
#> hi_solver_get_constraint_matrix
#> hi_solver_get_dbl_option
#> hi_solver_get_int_option
#> hi_solver_get_lp_costs
#> hi_solver_get_model
#> hi_solver_get_num_col
#> hi_solver_get_num_row
#> hi_solver_get_option
#> hi_solver_get_options
#> hi_solver_get_sense
#> hi_solver_get_str_option
#> hi_solver_get_variable_bounds
#> hi_solver_get_vartype
#> hi_solver_infinity
#> hi_solver_info
#> hi_solver_run
#> hi_solver_set_coeff
#> hi_solver_set_constraint_bounds
#> hi_solver_set_integrality
#> hi_solver_set_objective
#> hi_solver_set_offset
#> hi_solver_set_option
#> hi_solver_set_options
#> hi_solver_set_sense
#> hi_solver_set_variable_bounds
#> hi_solver_solution
#> hi_solver_status
#> hi_solver_status_message
#> hi_solver_write_basis
#> hi_solver_write_model
2.4 High level functions
The high level functions allow to work with models and solvers. More
details and examples can be found in the manual.
args(highs_model)
#> function (Q = NULL, L, lower, upper, A = NULL, lhs = NULL, rhs = NULL,
#> types = rep.int(1L, length(L)), maximum = FALSE, offset = 0)
#> NULL
args(highs_solver)
#> function (model, control = highs_control())
#> NULL
args(highs_control)
#> function (threads = 1L, time_limit = Inf, log_to_console = FALSE,
#> ...)
#> NULL
args(highs_write_model)
#> function (model, file)
#> NULL
2.5 Main function
The main function highs_solve.
library("highs")
args(highs_solve)
#> function (Q = NULL, L, lower, upper, A = NULL, lhs = NULL, rhs = NULL,
#> types = rep.int(1L, length(L)), maximum = FALSE, offset = 0,
#> control = highs_control())
#> NULL
2.6 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 0
#> ..$ ipm_iteration_count : int 5
#> ..$ 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
2.7 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] 3 0
#> $ objective_value: num -6
#> $ status : int 10
#> $ status_message : chr "Unbounded"
#> $ solver_msg :List of 6
#> ..$ value_valid: logi TRUE
#> ..$ dual_valid : logi TRUE
#> ..$ col_value : num [1:2] 3 0
#> ..$ col_dual : num [1:2] 0 1
#> ..$ row_value : num 3
#> ..$ row_dual : num -2
#> $ info :List of 18
#> ..$ valid : logi TRUE
#> ..$ mip_node_count : num -1
#> ..$ simplex_iteration_count : int 1
#> ..$ ipm_iteration_count : int 0
#> ..$ qp_iteration_count : int 0
#> ..$ crossover_iteration_count : int 0
#> ..$ primal_solution_status : chr "Feasible"
#> ..$ dual_solution_status : chr "Infeasible"
#> ..$ basis_validity : int 1
#> ..$ objective_function_value : num -6
#> ..$ 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 1
#> ..$ max_dual_infeasibility : num 1
#> ..$ sum_dual_infeasibilities : num 1
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 |
|:------------------------------------------------|:--------|
| presolve | string |
| solver | string |
| parallel | string |
| run_crossover | string |
| time_limit | double |
| read_solution_file | string |
| read_basis_file | string |
| write_model_file | string |
| solution_file | string |
| write_basis_file | string |
| random_seed | integer |
| ranging | string |
| infinite_cost | double |
| infinite_bound | double |
| small_matrix_value | double |
| large_matrix_value | double |
| primal_feasibility_tolerance | double |
| dual_feasibility_tolerance | double |
| ipm_optimality_tolerance | double |
| primal_residual_tolerance | double |
| dual_residual_tolerance | double |
| objective_bound | double |
| objective_target | double |
| threads | integer |
| user_bound_scale | integer |
| user_cost_scale | integer |
| highs_debug_level | integer |
| highs_analysis_level | integer |
| simplex_strategy | integer |
| simplex_scale_strategy | integer |
| simplex_crash_strategy | integer |
| simplex_dual_edge_weight_strategy | integer |
| simplex_primal_edge_weight_strategy | integer |
| simplex_iteration_limit | integer |
| simplex_update_limit | integer |
| simplex_min_concurrency | integer |
| simplex_max_concurrency | integer |
| log_file | string |
| write_model_to_file | bool |
| write_presolved_model_to_file | bool |
| write_solution_to_file | bool |
| write_solution_style | integer |
| glpsol_cost_row_location | integer |
| write_presolved_model_file | string |
| output_flag | bool |
| log_to_console | bool |
| timeless_log | bool |
| ipm_iteration_limit | integer |
| pdlp_native_termination | bool |
| pdlp_scaling | bool |
| pdlp_iteration_limit | integer |
| pdlp_e_restart_method | integer |
| pdlp_d_gap_tol | double |
| qp_iteration_limit | integer |
| qp_nullspace_limit | integer |
| iis_strategy | integer |
| blend_multi_objectives | bool |
| log_dev_level | integer |
| log_githash | bool |
| solve_relaxation | bool |
| allow_unbounded_or_infeasible | bool |
| use_implied_bounds_from_presolve | bool |
| lp_presolve_requires_basis_postsolve | bool |
| mps_parser_type_free | bool |
| use_warm_start | bool |
| keep_n_rows | integer |
| cost_scale_factor | integer |
| allowed_matrix_scale_factor | integer |
| allowed_cost_scale_factor | integer |
| ipx_dualize_strategy | integer |
| simplex_dualize_strategy | integer |
| simplex_permute_strategy | integer |
| max_dual_simplex_cleanup_level | integer |
| max_dual_simplex_phase1_cleanup_level | integer |
| simplex_price_strategy | integer |
| simplex_unscaled_solution_strategy | integer |
| presolve_reduction_limit | integer |
| restart_presolve_reduction_limit | integer |
| presolve_substitution_maxfillin | integer |
| presolve_rule_off | integer |
| presolve_rule_logging | bool |
| presolve_remove_slacks | bool |
| simplex_initial_condition_check | bool |
| no_unnecessary_rebuild_refactor | bool |
| simplex_initial_condition_tolerance | double |
| rebuild_refactor_solution_error_tolerance | double |
| dual_steepest_edge_weight_error_tolerance | double |
| dual_steepest_edge_weight_log_error_threshold | double |
| dual_simplex_cost_perturbation_multiplier | double |
| primal_simplex_bound_perturbation_multiplier | double |
| dual_simplex_pivot_growth_tolerance | double |
| presolve_pivot_threshold | double |
| factor_pivot_threshold | double |
| factor_pivot_tolerance | double |
| start_crossover_tolerance | double |
| less_infeasible_DSE_check | bool |
| less_infeasible_DSE_choose_row | bool |
| use_original_HFactor_logic | bool |
| run_centring | bool |
| max_centring_steps | integer |
| centring_ratio_tolerance | double |
| icrash | bool |
| icrash_dualize | bool |
| icrash_strategy | string |
| icrash_starting_weight | double |
| icrash_iterations | integer |
| icrash_approx_iter | integer |
| icrash_exact | bool |
| icrash_breakpoints | bool |
| mip_detect_symmetry | bool |
| mip_allow_restart | bool |
| mip_max_nodes | integer |
| mip_max_stall_nodes | integer |
| mip_max_start_nodes | integer |
| mip_max_leaves | integer |
| mip_max_improving_sols | integer |
| mip_lp_age_limit | integer |
| mip_pool_age_limit | integer |
| mip_pool_soft_limit | integer |
| mip_pscost_minreliable | integer |
| mip_min_cliquetable_entries_for_parallelism | integer |
| mip_report_level | integer |
| mip_feasibility_tolerance | double |
| mip_rel_gap | double |
| mip_abs_gap | double |
| mip_heuristic_effort | double |
| mip_min_logging_interval | double |
| mip_heuristic_run_rins | bool |
| mip_heuristic_run_rens | bool |
| mip_heuristic_run_root_reduced_cost | bool |
| mip_heuristic_run_zi_round | bool |
| mip_heuristic_run_shifting | bool |
| mip_improving_solution_save | bool |
| mip_improving_solution_report_sparse | bool |
| mip_improving_solution_file | string |
| mip_root_presolve_only | bool |
| mip_lifting_for_probing | integer |
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" |
Try the highs package in your browser
Any scripts or data that you put into this service are public.
highs documentation built on June 8, 2025, 10:36 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
R HIGHS Interface
Florian Schwendinger Updated: 2025-04-20
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 Package
The highs package provides an API similar to Rglpk and a low
level API to the HiGHS solver. For most users using highs_solve as
shown below should be the best choice.
The package functions can be grouped into the following categories:
- The main function
highs_solve. - Object style wrappers for the model and the solver
highs_modelandhighs_solver. - Function
highs_controlto construct the control object forhighs_solveandhighs_solver. - Low-level API wrapper functions to create and modify models
hi_new_modeland other functions starting withhi_model_. - Low-level API wrapper functions to create and modify solvers
hi_new_solverand other functions starting withhi_solver_. - Functions to create example models
example_modeland solversexample_solverfor the documentation examples. - Function
highs_available_solver_optionsto get the available solver options. - Function
highs_write_modelto write the model to a file.
library("highs")
2.1 Examples
The the example models and solvers are included to have small examples available for the manual.
writeLines(ls("package:highs", pattern = "^example"))
#> example_model
#> example_solver
2.2 Low level model functions
The low-level model functions allow to create and modify models. More details and examples can be found in the manual.
writeLines(ls("package:highs", pattern = "^hi(|_new)_model"))
#> hi_model_get_ncons
#> hi_model_get_nvars
#> hi_model_set_constraint_matrix
#> hi_model_set_hessian
#> hi_model_set_lhs
#> hi_model_set_lower
#> hi_model_set_ncol
#> hi_model_set_nrow
#> hi_model_set_objective
#> hi_model_set_offset
#> hi_model_set_rhs
#> hi_model_set_sense
#> hi_model_set_upper
#> hi_model_set_vartype
#> hi_new_model
2.3 Low level solver functions
The low-level solver functions allow to create and modify solvers. More details and examples can be found in the manual.
writeLines(ls("package:highs", pattern = "^hi(|_new)_solver"))
#> hi_new_solver
#> hi_solver_add_cols
#> hi_solver_add_rows
#> hi_solver_add_vars
#> hi_solver_change_constraint_bounds
#> hi_solver_change_variable_bounds
#> hi_solver_clear
#> hi_solver_clear_model
#> hi_solver_clear_solver
#> hi_solver_get_bool_option
#> hi_solver_get_constraint_bounds
#> hi_solver_get_constraint_matrix
#> hi_solver_get_dbl_option
#> hi_solver_get_int_option
#> hi_solver_get_lp_costs
#> hi_solver_get_model
#> hi_solver_get_num_col
#> hi_solver_get_num_row
#> hi_solver_get_option
#> hi_solver_get_options
#> hi_solver_get_sense
#> hi_solver_get_str_option
#> hi_solver_get_variable_bounds
#> hi_solver_get_vartype
#> hi_solver_infinity
#> hi_solver_info
#> hi_solver_run
#> hi_solver_set_coeff
#> hi_solver_set_constraint_bounds
#> hi_solver_set_integrality
#> hi_solver_set_objective
#> hi_solver_set_offset
#> hi_solver_set_option
#> hi_solver_set_options
#> hi_solver_set_sense
#> hi_solver_set_variable_bounds
#> hi_solver_solution
#> hi_solver_status
#> hi_solver_status_message
#> hi_solver_write_basis
#> hi_solver_write_model
2.4 High level functions
The high level functions allow to work with models and solvers. More details and examples can be found in the manual.
args(highs_model)
#> function (Q = NULL, L, lower, upper, A = NULL, lhs = NULL, rhs = NULL,
#> types = rep.int(1L, length(L)), maximum = FALSE, offset = 0)
#> NULL
args(highs_solver)
#> function (model, control = highs_control())
#> NULL
args(highs_control)
#> function (threads = 1L, time_limit = Inf, log_to_console = FALSE,
#> ...)
#> NULL
args(highs_write_model)
#> function (model, file)
#> NULL
2.5 Main function
The main function highs_solve.
library("highs")
args(highs_solve)
#> function (Q = NULL, L, lower, upper, A = NULL, lhs = NULL, rhs = NULL,
#> types = rep.int(1L, length(L)), maximum = FALSE, offset = 0,
#> control = highs_control())
#> NULL
2.6 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 0
#> ..$ ipm_iteration_count : int 5
#> ..$ 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
2.7 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] 3 0
#> $ objective_value: num -6
#> $ status : int 10
#> $ status_message : chr "Unbounded"
#> $ solver_msg :List of 6
#> ..$ value_valid: logi TRUE
#> ..$ dual_valid : logi TRUE
#> ..$ col_value : num [1:2] 3 0
#> ..$ col_dual : num [1:2] 0 1
#> ..$ row_value : num 3
#> ..$ row_dual : num -2
#> $ info :List of 18
#> ..$ valid : logi TRUE
#> ..$ mip_node_count : num -1
#> ..$ simplex_iteration_count : int 1
#> ..$ ipm_iteration_count : int 0
#> ..$ qp_iteration_count : int 0
#> ..$ crossover_iteration_count : int 0
#> ..$ primal_solution_status : chr "Feasible"
#> ..$ dual_solution_status : chr "Infeasible"
#> ..$ basis_validity : int 1
#> ..$ objective_function_value : num -6
#> ..$ 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 1
#> ..$ max_dual_infeasibility : num 1
#> ..$ sum_dual_infeasibilities : num 1
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 |
|:------------------------------------------------|:--------|
| presolve | string |
| solver | string |
| parallel | string |
| run_crossover | string |
| time_limit | double |
| read_solution_file | string |
| read_basis_file | string |
| write_model_file | string |
| solution_file | string |
| write_basis_file | string |
| random_seed | integer |
| ranging | string |
| infinite_cost | double |
| infinite_bound | double |
| small_matrix_value | double |
| large_matrix_value | double |
| primal_feasibility_tolerance | double |
| dual_feasibility_tolerance | double |
| ipm_optimality_tolerance | double |
| primal_residual_tolerance | double |
| dual_residual_tolerance | double |
| objective_bound | double |
| objective_target | double |
| threads | integer |
| user_bound_scale | integer |
| user_cost_scale | integer |
| highs_debug_level | integer |
| highs_analysis_level | integer |
| simplex_strategy | integer |
| simplex_scale_strategy | integer |
| simplex_crash_strategy | integer |
| simplex_dual_edge_weight_strategy | integer |
| simplex_primal_edge_weight_strategy | integer |
| simplex_iteration_limit | integer |
| simplex_update_limit | integer |
| simplex_min_concurrency | integer |
| simplex_max_concurrency | integer |
| log_file | string |
| write_model_to_file | bool |
| write_presolved_model_to_file | bool |
| write_solution_to_file | bool |
| write_solution_style | integer |
| glpsol_cost_row_location | integer |
| write_presolved_model_file | string |
| output_flag | bool |
| log_to_console | bool |
| timeless_log | bool |
| ipm_iteration_limit | integer |
| pdlp_native_termination | bool |
| pdlp_scaling | bool |
| pdlp_iteration_limit | integer |
| pdlp_e_restart_method | integer |
| pdlp_d_gap_tol | double |
| qp_iteration_limit | integer |
| qp_nullspace_limit | integer |
| iis_strategy | integer |
| blend_multi_objectives | bool |
| log_dev_level | integer |
| log_githash | bool |
| solve_relaxation | bool |
| allow_unbounded_or_infeasible | bool |
| use_implied_bounds_from_presolve | bool |
| lp_presolve_requires_basis_postsolve | bool |
| mps_parser_type_free | bool |
| use_warm_start | bool |
| keep_n_rows | integer |
| cost_scale_factor | integer |
| allowed_matrix_scale_factor | integer |
| allowed_cost_scale_factor | integer |
| ipx_dualize_strategy | integer |
| simplex_dualize_strategy | integer |
| simplex_permute_strategy | integer |
| max_dual_simplex_cleanup_level | integer |
| max_dual_simplex_phase1_cleanup_level | integer |
| simplex_price_strategy | integer |
| simplex_unscaled_solution_strategy | integer |
| presolve_reduction_limit | integer |
| restart_presolve_reduction_limit | integer |
| presolve_substitution_maxfillin | integer |
| presolve_rule_off | integer |
| presolve_rule_logging | bool |
| presolve_remove_slacks | bool |
| simplex_initial_condition_check | bool |
| no_unnecessary_rebuild_refactor | bool |
| simplex_initial_condition_tolerance | double |
| rebuild_refactor_solution_error_tolerance | double |
| dual_steepest_edge_weight_error_tolerance | double |
| dual_steepest_edge_weight_log_error_threshold | double |
| dual_simplex_cost_perturbation_multiplier | double |
| primal_simplex_bound_perturbation_multiplier | double |
| dual_simplex_pivot_growth_tolerance | double |
| presolve_pivot_threshold | double |
| factor_pivot_threshold | double |
| factor_pivot_tolerance | double |
| start_crossover_tolerance | double |
| less_infeasible_DSE_check | bool |
| less_infeasible_DSE_choose_row | bool |
| use_original_HFactor_logic | bool |
| run_centring | bool |
| max_centring_steps | integer |
| centring_ratio_tolerance | double |
| icrash | bool |
| icrash_dualize | bool |
| icrash_strategy | string |
| icrash_starting_weight | double |
| icrash_iterations | integer |
| icrash_approx_iter | integer |
| icrash_exact | bool |
| icrash_breakpoints | bool |
| mip_detect_symmetry | bool |
| mip_allow_restart | bool |
| mip_max_nodes | integer |
| mip_max_stall_nodes | integer |
| mip_max_start_nodes | integer |
| mip_max_leaves | integer |
| mip_max_improving_sols | integer |
| mip_lp_age_limit | integer |
| mip_pool_age_limit | integer |
| mip_pool_soft_limit | integer |
| mip_pscost_minreliable | integer |
| mip_min_cliquetable_entries_for_parallelism | integer |
| mip_report_level | integer |
| mip_feasibility_tolerance | double |
| mip_rel_gap | double |
| mip_abs_gap | double |
| mip_heuristic_effort | double |
| mip_min_logging_interval | double |
| mip_heuristic_run_rins | bool |
| mip_heuristic_run_rens | bool |
| mip_heuristic_run_root_reduced_cost | bool |
| mip_heuristic_run_zi_round | bool |
| mip_heuristic_run_shifting | bool |
| mip_improving_solution_save | bool |
| mip_improving_solution_report_sparse | bool |
| mip_improving_solution_file | string |
| mip_root_presolve_only | bool |
| mip_lifting_for_probing | integer |
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" |
Try the highs package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.