Parameters¶

Solver settings are grouped by function. Each table lists the parameter name, default, type, and allowed values for the selected interface.

Usage¶

PythonJuliaCCommand line

Set parameters with setParams, setParam, or Params:

model.setParams(
    TimeLimit=300,
    OptimalityTol=1e-6,
    FeasibilityTol=1e-6,
    OutputFlag=True,
)

Python also accepts the leaf C field names, such as time_sec_limit and eps_optimal_relative.

Read and reset values with:

time_limit = model.getParam("TimeLimit")
same_value = model.Params.TimeLimit

for key, value in model.Params.items():
    print(key, value)

model.resetParams()

setParams(...) validates all updates before applying them. If any name or value is invalid, no parameters are changed.

CuPDLPx.jl exposes supported solver settings as JuMP attributes using the C leaf field names:

using JuMP, CuPDLPx

model = Model(CuPDLPx.Optimizer)
set_attribute(model, "time_sec_limit", 300.0)
set_attribute(model, "eps_optimal_relative", 1e-6)
set_attribute(model, "eps_feasible_relative", 1e-6)
set_attribute(model, "verbose", true)

See the CuPDLPx.jl documentation for the attributes exposed by the current Julia wrapper.

Initialize pdhg_parameters_t before overriding individual fields:

pdhg_parameters_t params;
set_default_parameters(&params);

params.termination_criteria.time_sec_limit = 300.0;
params.termination_criteria.eps_optimal_relative = 1e-6;
params.termination_criteria.eps_feasible_relative = 1e-6;
params.verbose = false;

Use values in the documented ranges; assigning C fields does not perform the Python interface's runtime parameter validation.

Pass CLI settings before the input and output paths:

cupdlpx \
  --time_limit 300 \
  --eps_opt 1e-6 \
  --eps_feas 1e-6 \
  --quiet \
  model.mps.gz output

Run cupdlpx --help for the full list of flags.

Limits and logging¶

PythonC and JuliaCommand line

Parameter	Default	Type and range	Description
`TimeLimit`	`3600.0`	float \([0,\infty)\)	Wall-clock limit in seconds.
`IterationLimit`	\(2^{31}-1\)	integer \([0,2^{31}-1]\)	Maximum number of iterations.
`OutputFlag`, `LogToConsole`	`true`	boolean `false`, `true`	Print progress and the solve summary.
`TermCheckFreq`	`200`	integer \([3,2^{31}-1]\)	Iterations between termination and restart checks.
`Debug`	`false`	boolean `false`, `true`	Print developer diagnostics: restart reasons, active-set step size boost events, and a primal-weight log column.

Parameter	Default	Type and range	Description
`time_sec_limit`	`3600.0`	float \([0,\infty)\)	Wall-clock limit in seconds.
`iteration_limit`	\(2^{31}-1\)	integer \([0,2^{31}-1]\)	Maximum number of iterations.
`verbose`	`true`	boolean `false`, `true`	Print progress and the solve summary.
`termination_evaluation_frequency`	`200`	integer \([3,2^{31}-1]\)	Iterations between termination and restart checks.
`debug`	`false`	boolean `false`, `true`	Print developer diagnostics: restart reasons, active-set step size boost events, and a primal-weight log column.

Parameter	Default	Type and range	Description
`--time_limit`	`3600.0`	float \([0,\infty)\)	Wall-clock limit in seconds.
`--iter_limit`	\(2^{31}-1\)	integer \([0,2^{31}-1]\)	Maximum number of iterations.
`--verbose`, `--quiet`	`true`	boolean `false`, `true`	Print progress and the solve summary.
`--eval_freq`	`200`	integer \([3,2^{31}-1]\)	Iterations between termination and restart checks.
`--debug`	`false`	boolean `false`, `true`	Print developer diagnostics: restart reasons, active-set step size boost events, and a primal-weight log column.

Accuracy and termination¶

PythonC and JuliaCommand line

Parameter	Default	Type and range	Description
`OptimalityTol`	`1e-4`	float \((0,\infty)\)	Relative objective-gap tolerance.
`FeasibilityTol`	`1e-4`	float \((0,\infty)\)	Relative primal and dual feasibility tolerance.
`OptimalityNorm`	`l2`	string `l2`, `linf`	Norm used by the termination criteria.

Parameter	Default	Type and range	Description
`eps_optimal_relative`	`1e-4`	float \((0,\infty)\)	Relative objective-gap tolerance.
`eps_feasible_relative`	`1e-4`	float \((0,\infty)\)	Relative primal and dual feasibility tolerance.
`optimality_norm`	`NORM_TYPE_L2`	`norm_type_t` `NORM_TYPE_L2`, `NORM_TYPE_L_INF`	Norm used by the termination criteria.

Parameter	Default	Type and range	Description
`--eps_opt`	`1e-4`	float \((0,\infty)\)	Relative objective-gap tolerance.
`--eps_feas`	`1e-4`	float \((0,\infty)\)	Relative primal and dual feasibility tolerance.
`--opt_norm`	`l2`	string `l2`, `linf`	Norm used by the termination criteria.

Residuals are evaluated in the unscaled model; when presolve is enabled, this is the presolved model.

Scaling and preprocessing¶

PythonC and JuliaCommand line

Parameter	Default	Type and range	Description
`GeoMeanIters`	`12`	integer \([0,2^{31}-1]\)	Number of geometric mean scaling passes; `0` disables this stage.
`RuizIters`	`10`	integer \([0,2^{31}-1]\)	Number of \(\ell_\infty\) Ruiz equilibration scaling passes.
`UsePCAlpha`	`true`	boolean `false`, `true`	Enable Pock–Chambolle scaling.
`PCAlpha`	`1.0`	float \(\mathbb R\)	Exponent used by Pock–Chambolle scaling.
`BoundObjRescaling`	`true`	boolean `false`, `true`	Enable objective and bound scaling.
`Presolve`	`true`	boolean `false`, `true`	Enable presolve.
`MatrixZeroTol`	`1e-9`	float \([0,\infty)\)	Remove entries of \(A\) at or below this absolute magnitude.
`InfiniteBound`	`1e20`	float \((0,\infty)\)	Replace lower bounds \(\le -t\) and upper bounds \(\ge t\) with infinities, where \(t\) is this threshold.

Parameter	Default	Type and range	Description
`geometric_mean_iterations`	`12`	integer \([0,2^{31}-1]\)	Number of geometric mean scaling passes; `0` disables this stage.
`l_inf_ruiz_iterations`	`10`	integer \([0,2^{31}-1]\)	Number of \(\ell_\infty\) Ruiz equilibration scaling passes.
`has_pock_chambolle_alpha`	`true`	boolean `false`, `true`	Enable Pock–Chambolle scaling.
`pock_chambolle_alpha`	`1.0`	float \(\mathbb R\)	Exponent used by Pock–Chambolle scaling.
`bound_objective_rescaling`	`true`	boolean `false`, `true`	Enable objective and bound scaling.
`presolve`	`true`	boolean `false`, `true`	Enable presolve.
`matrix_zero_tol`	`1e-9`	float \([0,\infty)\)	Remove entries of \(A\) at or below this absolute magnitude.
`infinite_bound`	`1e20`	float \((0,\infty)\)	Replace lower bounds \(\le -t\) and upper bounds \(\ge t\) with infinities, where \(t\) is this threshold.

Parameter	Default	Type and range	Description
`--geo_mean_iter`	`12`	integer \([0,2^{31}-1]\)	Number of geometric mean scaling passes; `0` disables this stage.
`--l_inf_ruiz_iter`	`10`	integer \([0,2^{31}-1]\)	Number of \(\ell_\infty\) Ruiz equilibration scaling passes.
`--no_pock_chambolle`	Not passed	flag	Disable Pock–Chambolle scaling (enabled by default).
`--pock_chambolle_alpha`	`1.0`	float \(\mathbb R\)	Exponent used by Pock–Chambolle scaling.
`--no_bound_obj_rescaling`	Not passed	flag	Disable objective and bound scaling (enabled by default).
`--no_presolve`	Not passed	flag	Disable presolve (enabled by default).
`--matrix_zero_tol`	`1e-9`	float \([0,\infty)\)	Remove entries of \(A\) at or below this absolute magnitude.
`--infinite_bound`	`1e20`	float \((0,\infty)\)	Replace lower bounds \(\le -t\) and upper bounds \(\ge t\) with infinities, where \(t\) is this threshold.

Step size and reflection¶

PythonC and JuliaCommand line

Parameter	Default	Type and range	Description
`SVMaxIter`	`5000`	integer \([1,2^{31}-1]\)	Maximum iterations for estimating \(\lVert A\rVert_2\).
`SVTol`	`1e-4`	float \((0,\infty)\)	Stopping tolerance for the norm estimate.
`ReflectionCoeff`	`1.0`	float \(\mathbb R\)	Weight \(\gamma\) on the reflected point in the Halpern update; `1.0` uses the fully reflected point \(2\widehat x-x\).

Parameter	Default	Type and range	Description
`sv_max_iter`	`5000`	integer \([1,2^{31}-1]\)	Maximum iterations for estimating \(\lVert A\rVert_2\).
`sv_tol`	`1e-4`	float \((0,\infty)\)	Stopping tolerance for the norm estimate.
`reflection_coefficient`	`1.0`	float \(\mathbb R\)	Weight \(\gamma\) on the reflected point in the Halpern update; `1.0` uses the fully reflected point \(2\widehat x-x\).

Parameter	Default	Type and range	Description
`--sv_max_iter`	`5000`	integer \([1,2^{31}-1]\)	Maximum iterations for estimating \(\lVert A\rVert_2\).
`--sv_tol`	`1e-4`	float \((0,\infty)\)	Stopping tolerance for the norm estimate.

The command-line interface does not expose the reflection coefficient.

Active-set step size boost¶

PythonC and JuliaCommand line

Parameter	Default	Type and range	Description
`ActiveSetBoost`	`true`	boolean `false`, `true`	Enable the active-set step size boost.
`ASBActivationTol`	`1e-4`	float \([0,\infty)\)	Threshold for the maximum relative primal residual, dual residual, and primal–dual gap; see activation.
`ASBWindowIter`	`10000`	integer \([1,2^{31}-1]\)	Length of the trailing window in iterations.
`ASBVariableTol`	`1e-8`	float \([0,\infty)\)	Dual-slack tolerance for identifying a variable at a bound.
`ASBConstraintTol`	`1e-8`	float \([0,\infty)\)	Margin for identifying an inactive constraint.
`ASBReestimateChangeRatio`	`0.01`	float \([0,\infty)\)	Threshold for accumulated additions and removals, as a fraction of the current active-set size, before norm re-estimation.
`ASBSafetyFactor`	`0.9`	float \((0,\infty)\)	Safety factor \(\alpha\) in the target step \(\alpha/\hat\sigma\).
`ASBMinRaiseRatio`	`1.1`	float \([1,\infty)\)	Minimum ratio of the target step to the current step for a step-size increase.
`ASBDivergenceMargin`	`0.05`	float \([0,\infty)\)	Allowed relative increase in fixed-point error within an epoch before rollback.
`ASBDivergenceCeilingRatio`	`0.7`	float \((0,1]\)	Limit on subsequent step-size increases, as a fraction of the rejected step size; the step never falls below its initial value.
`ASBMaxReverts`	`2`	integer \([0,2^{31}-1]\)	Number of rollbacks before ASB is disabled.

Parameter	Default	Type and range	Description
`active_set_boost`	`true`	boolean `false`, `true`	Enable the active-set step size boost.
`asb_activation_tol`	`1e-4`	float \([0,\infty)\)	Threshold for the maximum relative primal residual, dual residual, and primal–dual gap; see activation.
`asb_window_iter`	`10000`	integer \([1,2^{31}-1]\)	Length of the trailing window in iterations.
`asb_variable_tol`	`1e-8`	float \([0,\infty)\)	Dual-slack tolerance for identifying a variable at a bound.
`asb_constraint_tol`	`1e-8`	float \([0,\infty)\)	Margin for identifying an inactive constraint.
`asb_reestimate_change_ratio`	`0.01`	float \([0,\infty)\)	Threshold for accumulated additions and removals, as a fraction of the current active-set size, before norm re-estimation.
`asb_safety_factor`	`0.9`	float \((0,\infty)\)	Safety factor \(\alpha\) in the target step \(\alpha/\hat\sigma\).
`asb_min_raise_ratio`	`1.1`	float \([1,\infty)\)	Minimum ratio of the target step to the current step for a step-size increase.
`asb_divergence_margin`	`0.05`	float \([0,\infty)\)	Allowed relative increase in fixed-point error within an epoch before rollback.
`asb_divergence_ceiling_ratio`	`0.7`	float \((0,1]\)	Limit on subsequent step-size increases, as a fraction of the rejected step size; the step never falls below its initial value.
`asb_max_reverts`	`2`	integer \([0,2^{31}-1]\)	Number of rollbacks before ASB is disabled.

Parameter	Default	Type and range	Description
`--no_active_set_boost`	Not passed	flag	Disable the active-set step size boost (enabled by default).
`--asb_activation_tol`	`1e-4`	float \([0,\infty)\)	Threshold for the maximum relative primal residual, dual residual, and primal–dual gap; see activation.
`--asb_window_iter`	`10000`	integer \([1,2^{31}-1]\)	Length of the trailing window in iterations.
`--asb_variable_tol`	`1e-8`	float \([0,\infty)\)	Dual-slack tolerance for identifying a variable at a bound.
`--asb_constraint_tol`	`1e-8`	float \([0,\infty)\)	Margin for identifying an inactive constraint.
`--asb_reestimate_change_ratio`	`0.01`	float \([0,\infty)\)	Threshold for accumulated additions and removals, as a fraction of the current active-set size, before norm re-estimation.
`--asb_safety_factor`	`0.9`	float \((0,\infty)\)	Safety factor \(\alpha\) in the target step \(\alpha/\hat\sigma\).
`--asb_min_raise_ratio`	`1.1`	float \([1,\infty)\)	Minimum ratio of the target step to the current step for a step-size increase.
`--asb_divergence_margin`	`0.05`	float \([0,\infty)\)	Allowed relative increase in fixed-point error within an epoch before rollback.
`--asb_divergence_ceiling_ratio`	`0.7`	float \((0,1]\)	Limit on subsequent step-size increases, as a fraction of the rejected step size; the step never falls below its initial value.
`--asb_max_reverts`	`2`	integer \([0,2^{31}-1]\)	Number of rollbacks before ASB is disabled.

Adaptive restart¶

PythonC and JuliaCommand line

Parameter	Default	Type and range	Description
`RestartArtificialThresh`	`0.36`	float \(\mathbb R\)	Maximum epoch length relative to the total iteration count.
`RestartSufficientReduction`	`0.2`	float \(\mathbb R\)	Fixed-point-error ratio for a restart triggered by sufficient reduction.
`RestartNecessaryReduction`	`0.5`	float \(\mathbb R\)	Required reduction before a local-increase restart.
`RestartKp`	`0.99`	float \(\mathbb R\)	Proportional gain in the primal-weight controller.

The Python interface does not expose the integral gain, derivative gain, or integral decay. Their defaults are listed in the C and Julia tab.

Parameter	Default	Type and range	Description
`artificial_restart_threshold`	`0.36`	float \(\mathbb R\)	Maximum epoch length relative to the total iteration count.
`sufficient_reduction_for_restart`	`0.2`	float \(\mathbb R\)	Fixed-point-error ratio for a restart triggered by sufficient reduction.
`necessary_reduction_for_restart`	`0.5`	float \(\mathbb R\)	Required reduction before a local-increase restart.
`k_p`	`0.99`	float \(\mathbb R\)	Proportional gain in the primal-weight controller.
`k_i`	`0.01`	float \(\mathbb R\)	Integral gain in the primal-weight controller.
`k_d`	`0.0`	float \(\mathbb R\)	Derivative gain in the primal-weight controller.
`i_smooth`	`0.3`	float \(\mathbb R\)	Decay factor for the accumulated integral error.

These settings are not exposed by the command-line interface.

See Adaptive restart for the restart conditions and Primal weight for the controller update.

Feasibility polishing¶

PythonC and JuliaCommand line

Parameter	Default	Type and range	Description
`FeasibilityPolishing`	`false`	boolean `false`, `true`	Run the feasibility-polishing phases after the main solve.
`FeasibilityPolishingTol`	`1e-6`	float \((0,\infty)\)	Target relative feasibility residual.

Parameter	Default	Type and range	Description
`feasibility_polishing`	`false`	boolean `false`, `true`	Run the feasibility-polishing phases after the main solve.
`eps_feas_polish_relative`	`1e-6`	float \((0,\infty)\)	Target relative feasibility residual.

Parameter	Default	Type and range	Description
`-f`, `--feasibility_polishing`	`false`	boolean `false`, `true`	Run the feasibility-polishing phases after the main solve.
`--eps_feas_polish`	`1e-6`	float \((0,\infty)\)	Target relative feasibility residual.