slsqp_jax.config

Nested configuration dataclasses consumed by SLSQP. Replaces the legacy 40+ flat kwargs.

slsqp_jax.config

Grouped configuration dataclasses for the SLSQP solver.

The SLSQP outer-loop solver previously exposed ~40 keyword arguments in a single flat namespace. This module groups them into small, semantically related eqx.Module dataclasses so the user-facing surface of SLSQP collapses to a single config: SLSQPConfig field plus the constraint structure (functions, counts, bounds), the optional derivative overrides, the optional pluggable inner solver, and the verbose printer.

Example:

from slsqp_jax import SLSQP, SLSQPConfig, ToleranceConfig, LBFGSConfig

solver = SLSQP(
    eq_constraint_fn=eq_fn,
    n_eq_constraints=1,
    config=SLSQPConfig(
        tolerance=ToleranceConfig(rtol=1e-8, atol=1e-8, max_steps=200),
        lbfgs=LBFGSConfig(memory=20),
    ),
)

The static / non-static distinction on each field mirrors the legacy field-by-field annotations so JAX retracing behaviour is unchanged.

class slsqp_jax.config.ToleranceConfig[source]

Bases: Module

Outer-loop tolerances and iteration / divergence budgets.

Attributes:
rtol: Relative tolerance for the stationarity convergence

check. The test is the filterSQP normalised KKT residual (Fletcher & Leyffer, User manual for filterSQP, eqs. 5 and 6): ||grad_L|| <= rtol * max(mu_max, 1) where mu_max = max_i {||grad_f||_2, |nu_i|, ||a_i||_2 |lambda_i|} is the largest single contributor to the Lagrangian gradient residual (objective-gradient norm, every bound multiplier, and every general-constraint ||row||_2 * |multiplier|). The same test is applied to the inexact projected-gradient numerator ||W_tilde g|| when AdaptiveCGConfig.use_inexact_stationarity is on. Replaces the legacy |L|-based denominator so the test is invariant to absolute objective magnitude and tracks multiplier blow-up under near-rank-deficient active sets. Default 1e-6.

atol: Absolute tolerance for primal feasibility and a number of

internal heuristic floors (steepest-descent fallback, adaptive CG tolerance floor, default proximal mu floor). Default 1e-6.

max_steps: Maximum number of outer SQP iterations. Default 100. min_steps: Minimum iterations before convergence is allowed.

Prevents premature termination at trivial starting points. Default 1.

stagnation_tol: Relative-improvement threshold for the

merit-based stagnation counter. Default 1e-12.

divergence_factor: Best-iterate divergence rollback fires when

the merit grows by more than divergence_factor * max(|best_merit|, 1) for divergence_patience consecutive steps. Default 10.0.

divergence_patience: Number of consecutive blow-up steps required

before the divergence rollback latches. Default 3.

rtol: float = 1e-06
atol: float = 1e-06
max_steps: int = 100
min_steps: int = 1
stagnation_tol: float = 1e-12
divergence_factor: float = 10.0
divergence_patience: int = 3
__init__(rtol=1e-06, atol=1e-06, max_steps=100, min_steps=1, stagnation_tol=1e-12, divergence_factor=10.0, divergence_patience=3)
Parameters:
Return type:

None

class slsqp_jax.config.LBFGSConfig[source]

Bases: Module

L-BFGS Hessian-approximation parameters.

Attributes:
memory: Number of curvature pairs (s, y) stored in the

ring buffer. Default 10.

damping_threshold: VARCHEN damping threshold applied to each

curvature pair before storage; set to 0.0 to disable. Default 0.2.

diag_floor: Lower clip for the per-variable secant diagonal

B_0 = diag(d). Default 1e-4.

diag_ceil: Upper clip for the per-variable secant diagonal.

Default 1e6.

memory: int = 10
damping_threshold: float = 0.2
diag_floor: float = 0.0001
diag_ceil: float = 1000000.0
__init__(memory=10, damping_threshold=0.2, diag_floor=0.0001, diag_ceil=1000000.0)
Parameters:
Return type:

None

class slsqp_jax.config.LineSearchConfig[source]

Bases: Module

Backtracking L1-merit line-search parameters.

Attributes:

max_steps: Maximum number of backtracking iterations. Default 20. armijo_c1: Armijo condition coefficient c_1. Default 1e-4. failure_patience: After this many consecutive line-search

failures, the L-BFGS history is hard-reset to identity. Default 3.

max_steps: int = 20
armijo_c1: float = 0.0001
failure_patience: int = 3
__init__(max_steps=20, armijo_c1=0.0001, failure_patience=3)
Parameters:

  • max_steps (int)

  • armijo_c1 (float)

  • failure_patience (int)

Return type:

None

class slsqp_jax.config.QPConfig[source]

Bases: Module

QP-subproblem parameters.

Attributes:
max_iter: Maximum number of active-set iterations per QP solve.

Default 100.

max_cg_iter: Maximum number of CG iterations per inner solve.

Default 50.

failure_patience: After this many consecutive QP failures, the

L-BFGS history is hard-reset to identity. Default 3.

zero_step_patience: After this many consecutive iterations where

the QP returns ||d|| < atol and primal feasibility holds, convergence is declared via the guarded qp_kkt_success disjunct. Default 3.

ping_pong_threshold: Threshold for the QP add/drop ping-pong

short-circuit. Default 2**31 - 1 (effectively disabled); opt in by setting to 3-8 on degenerate problems.

mult_drop_floor: Floor on the negative-multiplier drop test

inside the QP active-set loop. Default 1e-6.

cg_regularization: Minimum eigenvalue threshold delta**2 for

the CG curvature guard. Default 1e-6.

use_exact_hvp: When True, the QP inner CG uses the exact

Lagrangian HVP (via AD) instead of the L-BFGS approximation. Default False.

max_iter: int = 100
max_cg_iter: int = 50
failure_patience: int = 3
zero_step_patience: int = 3
ping_pong_threshold: int = 2147483647
mult_drop_floor: float = 1e-06
cg_regularization: float = 1e-06
use_exact_hvp: bool = False
__init__(max_iter=100, max_cg_iter=50, failure_patience=3, zero_step_patience=3, ping_pong_threshold=2147483647, mult_drop_floor=1e-06, cg_regularization=1e-06, use_exact_hvp=False)
Parameters:

  • max_iter (int)

  • max_cg_iter (int)

  • failure_patience (int)

  • zero_step_patience (int)

  • ping_pong_threshold (int)

  • mult_drop_floor (float)

  • cg_regularization (float)

  • use_exact_hvp (bool)

Return type:

None

class slsqp_jax.config.ProximalConfig[source]

Bases: Module

Adaptive proximal multiplier stabilization (sSQP, Wright 2002).

Attributes:
tau: Exponent in mu = clip(kkt_residual^tau, mu_min, mu_max).

Must lie in the half-open interval [0, 1). Set to 0.0 to disable sSQP entirely (equality constraints are then enforced via direct null-space projection). Default 0.5.

mu_min: Floor on the adaptive proximal mu. None resolves to

ToleranceConfig.atol at runtime. Default None.

mu_max: Ceiling on the adaptive proximal mu. Default 0.1.

tau: float = 0.5
mu_min: float | None = None
mu_max: float = 0.1
__init__(tau=0.5, mu_min=None, mu_max=0.1)
Parameters:
Return type:

None

class slsqp_jax.config.PreconditionerConfig[source]

Bases: Module

QP-inner-solver preconditioner configuration.

Attributes:

enabled: Whether to use a preconditioner at all. Default True. type: Either "lbfgs" (default) or "diagonal". The

diagonal estimator requires an exact HVP (set QPConfig.use_exact_hvp or provide obj_hvp_fn).

diagonal_n_probes: Number of Rademacher probes for the

stochastic diagonal estimator. Default 20.

enabled: bool = True
type: str = 'lbfgs'
diagonal_n_probes: int = 20
__init__(enabled=True, type='lbfgs', diagonal_n_probes=20)
Parameters:

  • enabled (bool)

  • type (str)

  • diagonal_n_probes (int)

Return type:

None

class slsqp_jax.config.LPECAConfig[source]

Bases: Module

LPEC-A active-set identification (Oberlin & Wright, 2005).

Attributes:
method: One of "expand" (default), "lpeca_init" or

"lpeca". "expand" disables LPEC-A entirely.

sigma: Threshold exponent (sigma_bar in the paper). Must

lie in the open interval (0, 1). Default 0.9.

beta: Threshold scaling factor. None resolves to

1 / (m_ineq + n + m_eq) at runtime. Default None.

use_lp: When True, solve the LPEC-A LP (via mpax.r2HPDHG)

for tighter multiplier estimates. Requires mpax. Default False.

trust_threshold: Trust gate on rho_bar. When rho_bar

exceeds this value the prediction is replaced with an empty set. Default 1.0.

warmup_steps: The first warmup_steps outer SQP iterations

bypass LPEC-A. Default 3.

predict_bounds: When True (default), extend the LPEC-A prediction

to box constraints (warm-start the bound-fixing loop).

method: str = 'expand'
sigma: float = 0.9
beta: float | None = None
use_lp: bool = False
trust_threshold: float = 1.0
warmup_steps: int = 3
predict_bounds: bool = True
__init__(method='expand', sigma=0.9, beta=None, use_lp=False, trust_threshold=1.0, warmup_steps=3, predict_bounds=True)
Parameters:
Return type:

None

class slsqp_jax.config.AdaptiveCGConfig[source]

Bases: Module

Adaptive CG / inexact stationarity configuration.

Attributes:
enabled: When True, the CG convergence tolerance is adapted from

the outer KKT residual (Eisenstat-Walker style). Default False to preserve baseline behaviour.

use_inexact_stationarity: When True, the projected-gradient norm

from a noise-aware inner solver (e.g. HRInexactSTCG) is added as a logical-OR disjunct to the classical stationarity test. Default False.

enabled: bool = False
use_inexact_stationarity: bool = False
__init__(enabled=False, use_inexact_stationarity=False)
Parameters:

  • enabled (bool)

  • use_inexact_stationarity (bool)

Return type:

None

class slsqp_jax.config.SLSQPConfig[source]

Bases: Module

Aggregate configuration for SLSQP.

Replaces the legacy 40+ flat keyword arguments with a small set of grouped sub-configs. Pass directly to SLSQP via the config= keyword. All sub-configs default to their dataclass defaults so SLSQPConfig() reproduces the legacy default settings.

tolerance: ToleranceConfig
lbfgs: LBFGSConfig
qp: QPConfig
proximal: ProximalConfig
preconditioner: PreconditionerConfig
lpeca: LPECAConfig
adaptive_cg: AdaptiveCGConfig
__init__(tolerance=<factory>, lbfgs=<factory>, line_search=<factory>, qp=<factory>, proximal=<factory>, preconditioner=<factory>, lpeca=<factory>, adaptive_cg=<factory>)
Parameters:
Return type:

None