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remove explicit solver settings in python #829

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fbb03ee
remove explicit solver settings in python
Iroy30 Feb 5, 2026
1d89016
Merge branch 'main' into update_python_solver_parameters
Iroy30 Feb 5, 2026
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1 change: 1 addition & 0 deletions cpp/include/cuopt/linear_programming/solver_settings.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -94,6 +94,7 @@ class solver_settings_t {
const std::vector<parameter_info_t<i_t>>& get_int_parameters() const;
const std::vector<parameter_info_t<bool>>& get_bool_parameters() const;
const std::vector<parameter_info_t<std::string>>& get_string_parameters() const;
const std::vector<std::string> get_parameter_names() const;

private:
pdlp_solver_settings_t<i_t, f_t> pdlp_settings;
Expand Down
19 changes: 19 additions & 0 deletions cpp/src/math_optimization/solver_settings.cu
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Expand Up @@ -440,6 +440,25 @@ solver_settings_t<i_t, f_t>::get_string_parameters() const
return string_parameters;
}

template <typename i_t, typename f_t>
const std::vector<std::string> solver_settings_t<i_t, f_t>::get_parameter_names() const
{
std::vector<std::string> parameter_names;
for (auto& param : int_parameters) {
parameter_names.push_back(param.param_name);
}
for (auto& param : float_parameters) {
parameter_names.push_back(param.param_name);
}
for (auto& param : bool_parameters) {
parameter_names.push_back(param.param_name);
}
for (auto& param : string_parameters) {
parameter_names.push_back(param.param_name);
}
return parameter_names;
}
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🛠️ Refactor suggestion | 🟠 Major

🧩 Analysis chain

🏁 Script executed:

#!/bin/bash
# First, check the parameter definitions to see if duplicates exist
head -n 200 cpp/src/math_optimization/solver_settings.cu | tail -n 140

Repository: NVIDIA/cuopt

Length of output: 7060

🏁 Script executed:

#!/bin/bash
# Search for get_parameter_names usage
rg -n "get_parameter_names" --type=cpp --type=py -B2 -A5

Repository: NVIDIA/cuopt

Length of output: 866

🏁 Script executed:

#!/bin/bash
# Look for similar iteration patterns in the file to assess code duplication
rg -n "for \(auto& param : .*_parameters\)" cpp/src/math_optimization/solver_settings.cu

Repository: NVIDIA/cuopt

Length of output: 961

🏁 Script executed:

#!/bin/bash
# Search for Python usage of get_parameter_names
find . -name "*.py" -type f | head -20

Repository: NVIDIA/cuopt

Length of output: 1222

🏁 Script executed:

#!/bin/bash
# Search for get_parameter_names in all Python files
rg "get_parameter_names" . --type=py -B3 -A10

Repository: NVIDIA/cuopt

Length of output: 38

🏁 Script executed:

#!/bin/bash
# Check if duplicates in returned vector would be problematic
# Look at how parameter names are used (e.g., in loops, sets, dicts)
rg "get_parameter_names\(\)" . -A15 --type=cpp --type=py | head -60

Repository: NVIDIA/cuopt

Length of output: 1267

Refactor repeated iteration patterns over parameter types into a shared helper function.

The returned vector contains duplicate parameter names (e.g., CUOPT_TIME_LIMIT, CUOPT_NUM_GPUS, CUOPT_LOG_TO_CONSOLE, CUOPT_PRESOLVE, CUOPT_LOG_FILE, CUOPT_SOLUTION_FILE, CUOPT_USER_PROBLEM_FILE appear across multiple parameter vectors). More importantly, the pattern of iterating over int_parameters, float_parameters, bool_parameters, and string_parameters is duplicated 5+ times throughout this file (lines 131–299 alone contain multiple occurrences). Per coding guidelines, 3+ code duplications in solver components should be refactored into shared utilities.

Create a helper template that accepts a callable or uses a generic aggregation pattern to avoid repeating the four-way loop. This improves maintainability and allows deduplication to be handled consistently across all parameter aggregation methods.

🤖 Prompt for AI Agents 
In `@cpp/src/math_optimization/solver_settings.cu` around lines 442 - 459, The
get_parameter_names method duplicates iteration over int_parameters,
float_parameters, bool_parameters, and string_parameters and collects duplicate
names; create a shared helper (e.g., a template function
aggregate_parameter_names or for_each_parameter in the solver_settings_t class)
that accepts a callable or output container and iterates the four parameter
vectors once to append names, using an unordered_set to filter duplicates;
replace solver_settings_t<i_t,f_t>::get_parameter_names and other similar
methods with calls to this helper (referencing int_parameters, float_parameters,
bool_parameters, string_parameters and the helper name) so all aggregation logic
and deduplication live in one place.


#if MIP_INSTANTIATE_FLOAT
template class solver_settings_t<int, float>;
template void solver_settings_t<int, float>::set_parameter(const std::string& name, int value);
Expand Down
2 changes: 2 additions & 0 deletions python/cuopt/cuopt/linear_programming/solver/solver.pxd
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Expand Up @@ -62,6 +62,8 @@ cdef extern from "cuopt/linear_programming/solver_settings.hpp" namespace "cuopt

string get_parameter_as_string(const string& name) except +

vector[string] get_parameter_names() except +

# LP settings
void set_initial_pdlp_primal_solution(
const f_t* initial_primal_solution,
Expand Down
104 changes: 19 additions & 85 deletions python/cuopt/cuopt/linear_programming/solver/solver_parameters.pyx
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@@ -1,4 +1,4 @@
# SPDX-FileCopyrightText: Copyright (c) 2023-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # noqa
# SPDX-FileCopyrightText: Copyright (c) 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. # noqa
# SPDX-License-Identifier: Apache-2.0


Expand All @@ -9,10 +9,10 @@

from libcpp.memory cimport unique_ptr
from libcpp.string cimport string
from libcpp.vector cimport vector

from cuopt.linear_programming.solver.solver cimport solver_settings_t


def get_solver_setting(name):
cdef unique_ptr[solver_settings_t[int, double]] unique_solver_settings

Expand All @@ -25,87 +25,21 @@ def get_solver_setting(name):
name.encode('utf-8')
).decode('utf-8')

# Get the C #defines from the constants.h file
cdef extern from "cuopt/linear_programming/constants.h": # noqa
cdef const char* c_CUOPT_ABSOLUTE_DUAL_TOLERANCE "CUOPT_ABSOLUTE_DUAL_TOLERANCE" # noqa
cdef const char* c_CUOPT_RELATIVE_DUAL_TOLERANCE "CUOPT_RELATIVE_DUAL_TOLERANCE" # noqa
cdef const char* c_CUOPT_ABSOLUTE_PRIMAL_TOLERANCE "CUOPT_ABSOLUTE_PRIMAL_TOLERANCE" # noqa
cdef const char* c_CUOPT_RELATIVE_PRIMAL_TOLERANCE "CUOPT_RELATIVE_PRIMAL_TOLERANCE" # noqa
cdef const char* c_CUOPT_ABSOLUTE_GAP_TOLERANCE "CUOPT_ABSOLUTE_GAP_TOLERANCE" # noqa
cdef const char* c_CUOPT_RELATIVE_GAP_TOLERANCE "CUOPT_RELATIVE_GAP_TOLERANCE" # noqa
cdef const char* c_CUOPT_INFEASIBILITY_DETECTION "CUOPT_INFEASIBILITY_DETECTION" # noqa
cdef const char* c_CUOPT_STRICT_INFEASIBILITY "CUOPT_STRICT_INFEASIBILITY" # noqa
cdef const char* c_CUOPT_PRIMAL_INFEASIBLE_TOLERANCE "CUOPT_PRIMAL_INFEASIBLE_TOLERANCE" # noqa
cdef const char* c_CUOPT_DUAL_INFEASIBLE_TOLERANCE "CUOPT_DUAL_INFEASIBLE_TOLERANCE" # noqa
cdef const char* c_CUOPT_ITERATION_LIMIT "CUOPT_ITERATION_LIMIT" # noqa
cdef const char* c_CUOPT_TIME_LIMIT "CUOPT_TIME_LIMIT" # noqa
cdef const char* c_CUOPT_PDLP_SOLVER_MODE "CUOPT_PDLP_SOLVER_MODE" # noqa
cdef const char* c_CUOPT_METHOD "CUOPT_METHOD" # noqa
cdef const char* c_CUOPT_PER_CONSTRAINT_RESIDUAL "CUOPT_PER_CONSTRAINT_RESIDUAL" # noqa
cdef const char* c_CUOPT_SAVE_BEST_PRIMAL_SO_FAR "CUOPT_SAVE_BEST_PRIMAL_SO_FAR" # noqa
cdef const char* c_CUOPT_FIRST_PRIMAL_FEASIBLE "CUOPT_FIRST_PRIMAL_FEASIBLE" # noqa
cdef const char* c_CUOPT_LOG_FILE "CUOPT_LOG_FILE" # noqa
cdef const char* c_CUOPT_LOG_TO_CONSOLE "CUOPT_LOG_TO_CONSOLE" # noqa
cdef const char* c_CUOPT_CROSSOVER "CUOPT_CROSSOVER" # noqa
cdef const char* c_CUOPT_PRESOLVE "CUOPT_PRESOLVE" # noqa
cdef const char* c_CUOPT_DUAL_POSTSOLVE "CUOPT_DUAL_POSTSOLVE" # noqa
cdef const char* c_CUOPT_NUM_GPUS "CUOPT_NUM_GPUS" # noqa
cdef const char* c_CUOPT_MIP_ABSOLUTE_TOLERANCE "CUOPT_MIP_ABSOLUTE_TOLERANCE" # noqa
cdef const char* c_CUOPT_MIP_RELATIVE_TOLERANCE "CUOPT_MIP_RELATIVE_TOLERANCE" # noqa
cdef const char* c_CUOPT_MIP_INTEGRALITY_TOLERANCE "CUOPT_MIP_INTEGRALITY_TOLERANCE" # noqa
cdef const char* c_CUOPT_MIP_ABSOLUTE_GAP "CUOPT_MIP_ABSOLUTE_GAP" # noqa
cdef const char* c_CUOPT_MIP_RELATIVE_GAP "CUOPT_MIP_RELATIVE_GAP" # noqa
cdef const char* c_CUOPT_MIP_HEURISTICS_ONLY "CUOPT_MIP_HEURISTICS_ONLY" # noqa
cdef const char* c_CUOPT_MIP_SCALING "CUOPT_MIP_SCALING" # noqa
cdef const char* c_CUOPT_SOLUTION_FILE "CUOPT_SOLUTION_FILE" # noqa
cdef const char* c_CUOPT_NUM_CPU_THREADS "CUOPT_NUM_CPU_THREADS" # noqa
cdef const char* c_CUOPT_USER_PROBLEM_FILE "CUOPT_USER_PROBLEM_FILE" # noqa
cdef const char* c_CUOPT_AUGMENTED "CUOPT_AUGMENTED"
cdef const char* c_CUOPT_FOLDING "CUOPT_FOLDING"
cdef const char* c_CUOPT_DUALIZE "CUOPT_DUALIZE"
cdef const char* c_CUOPT_ELIMINATE_DENSE_COLUMNS "CUOPT_ELIMINATE_DENSE_COLUMNS" # noqa
cdef const char* c_CUOPT_CUDSS_DETERMINISTIC "CUOPT_CUDSS_DETERMINISTIC" # noqa
cdef const char* c_CUOPT_ORDERING "CUOPT_ORDERING" # noqa
cdef const char* c_CUOPT_BARRIER_DUAL_INITIAL_POINT "CUOPT_BARRIER_DUAL_INITIAL_POINT" # noqa

# Create Python string constants from C string literals
CUOPT_ABSOLUTE_DUAL_TOLERANCE = c_CUOPT_ABSOLUTE_DUAL_TOLERANCE.decode('utf-8') # noqa
CUOPT_RELATIVE_DUAL_TOLERANCE = c_CUOPT_RELATIVE_DUAL_TOLERANCE.decode('utf-8') # noqa
CUOPT_ABSOLUTE_PRIMAL_TOLERANCE = c_CUOPT_ABSOLUTE_PRIMAL_TOLERANCE.decode('utf-8') # noqa
CUOPT_RELATIVE_PRIMAL_TOLERANCE = c_CUOPT_RELATIVE_PRIMAL_TOLERANCE.decode('utf-8') # noqa
CUOPT_ABSOLUTE_GAP_TOLERANCE = c_CUOPT_ABSOLUTE_GAP_TOLERANCE.decode('utf-8') # noqa
CUOPT_RELATIVE_GAP_TOLERANCE = c_CUOPT_RELATIVE_GAP_TOLERANCE.decode('utf-8') # noqa
CUOPT_INFEASIBILITY_DETECTION = c_CUOPT_INFEASIBILITY_DETECTION.decode('utf-8') # noqa
CUOPT_STRICT_INFEASIBILITY = c_CUOPT_STRICT_INFEASIBILITY.decode('utf-8') # noqa
CUOPT_PRIMAL_INFEASIBLE_TOLERANCE = c_CUOPT_PRIMAL_INFEASIBLE_TOLERANCE.decode('utf-8') # noqa
CUOPT_DUAL_INFEASIBLE_TOLERANCE = c_CUOPT_DUAL_INFEASIBLE_TOLERANCE.decode('utf-8') # noqa
CUOPT_ITERATION_LIMIT = c_CUOPT_ITERATION_LIMIT.decode('utf-8') # noqa
CUOPT_TIME_LIMIT = c_CUOPT_TIME_LIMIT.decode('utf-8') # noqa
CUOPT_PDLP_SOLVER_MODE = c_CUOPT_PDLP_SOLVER_MODE.decode('utf-8') # noqa
CUOPT_METHOD = c_CUOPT_METHOD.decode('utf-8') # noqa
CUOPT_PER_CONSTRAINT_RESIDUAL = c_CUOPT_PER_CONSTRAINT_RESIDUAL.decode('utf-8') # noqa
CUOPT_SAVE_BEST_PRIMAL_SO_FAR = c_CUOPT_SAVE_BEST_PRIMAL_SO_FAR.decode('utf-8') # noqa
CUOPT_FIRST_PRIMAL_FEASIBLE = c_CUOPT_FIRST_PRIMAL_FEASIBLE.decode('utf-8') # noqa
CUOPT_LOG_FILE = c_CUOPT_LOG_FILE.decode('utf-8') # noqa
CUOPT_LOG_TO_CONSOLE = c_CUOPT_LOG_TO_CONSOLE.decode('utf-8') # noqa
CUOPT_CROSSOVER = c_CUOPT_CROSSOVER.decode('utf-8') # noqa
CUOPT_PRESOLVE = c_CUOPT_PRESOLVE.decode('utf-8') # noqa
CUOPT_DUAL_POSTSOLVE = c_CUOPT_DUAL_POSTSOLVE.decode('utf-8') # noqa
CUOPT_NUM_GPUS = c_CUOPT_NUM_GPUS.decode('utf-8') # noqa
CUOPT_MIP_ABSOLUTE_TOLERANCE = c_CUOPT_MIP_ABSOLUTE_TOLERANCE.decode('utf-8') # noqa
CUOPT_MIP_RELATIVE_TOLERANCE = c_CUOPT_MIP_RELATIVE_TOLERANCE.decode('utf-8') # noqa
CUOPT_MIP_INTEGRALITY_TOLERANCE = c_CUOPT_MIP_INTEGRALITY_TOLERANCE.decode('utf-8') # noqa
CUOPT_MIP_ABSOLUTE_GAP = c_CUOPT_MIP_ABSOLUTE_GAP.decode('utf-8') # noqa
CUOPT_MIP_RELATIVE_GAP = c_CUOPT_MIP_RELATIVE_GAP.decode('utf-8') # noqa
CUOPT_MIP_HEURISTICS_ONLY = c_CUOPT_MIP_HEURISTICS_ONLY.decode('utf-8') # noqa
CUOPT_MIP_SCALING = c_CUOPT_MIP_SCALING.decode('utf-8') # noqa
CUOPT_SOLUTION_FILE = c_CUOPT_SOLUTION_FILE.decode('utf-8') # noqa
CUOPT_NUM_CPU_THREADS = c_CUOPT_NUM_CPU_THREADS.decode('utf-8') # noqa
CUOPT_USER_PROBLEM_FILE = c_CUOPT_USER_PROBLEM_FILE.decode('utf-8') # noqa
CUOPT_AUGMENTED = c_CUOPT_AUGMENTED.decode('utf-8') # noqa
CUOPT_FOLDING = c_CUOPT_FOLDING.decode('utf-8') # noqa
CUOPT_DUALIZE = c_CUOPT_DUALIZE.decode('utf-8') # noqa
CUOPT_ELIMINATE_DENSE_COLUMNS = c_CUOPT_ELIMINATE_DENSE_COLUMNS.decode('utf-8') # noqa
CUOPT_CUDSS_DETERMINISTIC = c_CUOPT_CUDSS_DETERMINISTIC.decode('utf-8') # noqa
CUOPT_ORDERING = c_CUOPT_ORDERING.decode('utf-8') # noqa
CUOPT_BARRIER_DUAL_INITIAL_POINT = c_CUOPT_BARRIER_DUAL_INITIAL_POINT.decode('utf-8') # noqa
cpdef get_solver_parameter_names():
cdef unique_ptr[solver_settings_t[int, double]] unique_solver_settings
unique_solver_settings.reset(new solver_settings_t[int, double]())
cdef solver_settings_t[int, double]* c_solver_settings = (
unique_solver_settings.get()
)
cdef vector[string] parameter_names = c_solver_settings.get_parameter_names()

cdef list py_parameter_names = []
cdef size_t i
for i in range(parameter_names.size()):
# std::string -> Python str
py_parameter_names.append(parameter_names[i].decode("utf-8"))
return py_parameter_names

solver_params = get_solver_parameter_names()
for param in solver_params: globals()["CUOPT_"+param.upper()] = param
153 changes: 18 additions & 135 deletions python/cuopt/cuopt/linear_programming/solver_settings/solver_settings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,46 +4,7 @@
from enum import IntEnum, auto

from cuopt.linear_programming.solver.solver_parameters import (
CUOPT_ABSOLUTE_DUAL_TOLERANCE,
CUOPT_ABSOLUTE_GAP_TOLERANCE,
CUOPT_ABSOLUTE_PRIMAL_TOLERANCE,
CUOPT_AUGMENTED,
CUOPT_BARRIER_DUAL_INITIAL_POINT,
CUOPT_CROSSOVER,
CUOPT_CUDSS_DETERMINISTIC,
CUOPT_DUAL_INFEASIBLE_TOLERANCE,
CUOPT_DUAL_POSTSOLVE,
CUOPT_DUALIZE,
CUOPT_ELIMINATE_DENSE_COLUMNS,
CUOPT_FIRST_PRIMAL_FEASIBLE,
CUOPT_FOLDING,
CUOPT_INFEASIBILITY_DETECTION,
CUOPT_ITERATION_LIMIT,
CUOPT_LOG_FILE,
CUOPT_LOG_TO_CONSOLE,
CUOPT_METHOD,
CUOPT_MIP_ABSOLUTE_GAP,
CUOPT_MIP_ABSOLUTE_TOLERANCE,
CUOPT_MIP_HEURISTICS_ONLY,
CUOPT_MIP_INTEGRALITY_TOLERANCE,
CUOPT_MIP_RELATIVE_GAP,
CUOPT_MIP_RELATIVE_TOLERANCE,
CUOPT_MIP_SCALING,
CUOPT_NUM_CPU_THREADS,
CUOPT_NUM_GPUS,
CUOPT_ORDERING,
CUOPT_PDLP_SOLVER_MODE,
CUOPT_PER_CONSTRAINT_RESIDUAL,
CUOPT_PRESOLVE,
CUOPT_PRIMAL_INFEASIBLE_TOLERANCE,
CUOPT_RELATIVE_DUAL_TOLERANCE,
CUOPT_RELATIVE_GAP_TOLERANCE,
CUOPT_RELATIVE_PRIMAL_TOLERANCE,
CUOPT_SAVE_BEST_PRIMAL_SO_FAR,
CUOPT_SOLUTION_FILE,
CUOPT_STRICT_INFEASIBILITY,
CUOPT_TIME_LIMIT,
CUOPT_USER_PROBLEM_FILE,
solver_params,
get_solver_setting,
)

Expand Down Expand Up @@ -156,6 +117,8 @@ def get_parameter(self, name):
For a list of availabe parameters, their descriptions, default values,
and acceptable ranges, see the cuOpt documentation `parameter.rst`.
"""
if name not in solver_params:
raise ValueError("Invalid parameter. Please check documentation")
if name in self.settings_dict:
if isinstance(self.settings_dict[name], str):
return self.to_base_type(self.settings_dict[name])
Expand All @@ -179,7 +142,8 @@ def set_parameter(self, name, value):
For a list of availabe parameters, their descriptions, default values,
and acceptable ranges, see the cuOpt documentation `parameter.rst`.
"""

if name not in solver_params:
raise ValueError("Invalid parameter. Please check documentation")
self.settings_dict[name] = value

def set_optimality_tolerance(self, eps_optimal):
Expand Down Expand Up @@ -211,12 +175,9 @@ def set_optimality_tolerance(self, eps_optimal):
Default value is 1e-4.
To set each absolute and relative tolerance, use the provided setters.
"""
self.settings_dict["absolute_dual_tolerance"] = eps_optimal
self.settings_dict["relative_dual_tolerance"] = eps_optimal
self.settings_dict["absolute_primal_tolerance"] = eps_optimal
self.settings_dict["relative_primal_tolerance"] = eps_optimal
self.settings_dict["absolute_gap_tolerance"] = eps_optimal
self.settings_dict["relative_gap_tolerance"] = eps_optimal
for param in solver_params:
if param.endswith("tolerance") and not param.startswith("mip"):
self.settings_dict[param] = eps_optimal

def set_pdlp_warm_start_data(self, pdlp_warm_start_data):
"""
Expand Down Expand Up @@ -339,93 +300,15 @@ def get_pdlp_warm_start_data(self):
return self.pdlp_warm_start_data

def toDict(self):
time_limit = self.get_parameter(CUOPT_TIME_LIMIT)
if time_limit == float("inf"):
time_limit = None

solver_config = {
"tolerances": {
"absolute_dual_tolerance": self.get_parameter(
CUOPT_ABSOLUTE_DUAL_TOLERANCE
),
"relative_dual_tolerance": self.get_parameter(
CUOPT_RELATIVE_DUAL_TOLERANCE
),
"absolute_primal_tolerance": self.get_parameter(
CUOPT_ABSOLUTE_PRIMAL_TOLERANCE
),
"relative_primal_tolerance": self.get_parameter(
CUOPT_RELATIVE_PRIMAL_TOLERANCE
),
"absolute_gap_tolerance": self.get_parameter(
CUOPT_ABSOLUTE_GAP_TOLERANCE
),
"relative_gap_tolerance": self.get_parameter(
CUOPT_RELATIVE_GAP_TOLERANCE
),
"primal_infeasible_tolerance": self.get_parameter(
CUOPT_PRIMAL_INFEASIBLE_TOLERANCE
),
"dual_infeasible_tolerance": self.get_parameter(
CUOPT_DUAL_INFEASIBLE_TOLERANCE
),
"mip_integrality_tolerance": self.get_parameter(
CUOPT_MIP_INTEGRALITY_TOLERANCE
),
"mip_absolute_gap": self.get_parameter(CUOPT_MIP_ABSOLUTE_GAP),
"mip_relative_gap": self.get_parameter(CUOPT_MIP_RELATIVE_GAP),
"mip_absolute_tolerance": self.get_parameter(
CUOPT_MIP_ABSOLUTE_TOLERANCE
),
"mip_relative_tolerance": self.get_parameter(
CUOPT_MIP_RELATIVE_TOLERANCE
),
},
"infeasibility_detection": self.get_parameter(
CUOPT_INFEASIBILITY_DETECTION
),
"time_limit": time_limit,
"iteration_limit": self.get_parameter(CUOPT_ITERATION_LIMIT),
"pdlp_solver_mode": self.get_parameter(CUOPT_PDLP_SOLVER_MODE),
"method": self.get_parameter(CUOPT_METHOD),
"presolve": self.get_parameter(CUOPT_PRESOLVE),
"dual_postsolve": self.get_parameter(CUOPT_DUAL_POSTSOLVE),
"mip_scaling": self.get_parameter(CUOPT_MIP_SCALING),
"mip_heuristics_only": self.get_parameter(
CUOPT_MIP_HEURISTICS_ONLY
),
"num_cpu_threads": self.get_parameter(CUOPT_NUM_CPU_THREADS),
"num_gpus": self.get_parameter(CUOPT_NUM_GPUS),
"augmented": self.get_parameter(CUOPT_AUGMENTED),
"folding": self.get_parameter(CUOPT_FOLDING),
"dualize": self.get_parameter(CUOPT_DUALIZE),
"ordering": self.get_parameter(CUOPT_ORDERING),
"barrier_dual_initial_point": self.get_parameter(
CUOPT_BARRIER_DUAL_INITIAL_POINT
),
"eliminate_dense_columns": self.get_parameter(
CUOPT_ELIMINATE_DENSE_COLUMNS
),
"cudss_deterministic": self.get_parameter(
CUOPT_CUDSS_DETERMINISTIC
),
"crossover": self.get_parameter(CUOPT_CROSSOVER),
"log_to_console": self.get_parameter(CUOPT_LOG_TO_CONSOLE),
"first_primal_feasible": self.get_parameter(
CUOPT_FIRST_PRIMAL_FEASIBLE
),
"log_file": self.get_parameter(CUOPT_LOG_FILE),
"per_constraint_residual": self.get_parameter(
CUOPT_PER_CONSTRAINT_RESIDUAL
),
"save_best_primal_so_far": self.get_parameter(
CUOPT_SAVE_BEST_PRIMAL_SO_FAR
),
"solution_file": self.get_parameter(CUOPT_SOLUTION_FILE),
"strict_infeasibility": self.get_parameter(
CUOPT_STRICT_INFEASIBILITY
),
"user_problem_file": self.get_parameter(CUOPT_USER_PROBLEM_FILE),
}
solver_config = {}
solver_config["tolerances"] = {}
for param in solver_params:
if param.endswith("tolerance"):
solver_config["tolerances"][param] = self.get_parameter(param)
else:
param_value = self.get_parameter(param)
if param_value == float("inf"):
param_value = None
solver_config[param] = param_value

return solver_config
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