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ramp_proximal_weight

Ramp proximal-weight autotuner.

Ramps lam_prox up by a constant factor each iteration until it hits the configured maximum, then holds it constant.

RampProximalWeight

Bases: AutotuningBase

Ramp lam_prox toward lam_prox_max then hold.

update_weights is a pure functional update on the :class:AlgorithmState pytree; see the base-class contract.

Source code in openscvx/algorithms/autotuner/ramp_proximal_weight.py 
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class RampProximalWeight(AutotuningBase):
    """Ramp ``lam_prox`` toward ``lam_prox_max`` then hold.

    ``update_weights`` is a pure functional update on the
    :class:`AlgorithmState` pytree; see the base-class contract.
    """

    COMPUTES_ACCEPTANCE_METRICS = False

    def __init__(
        self,
        ramp_factor: float = 1.0,
        lam_prox_max: float = 1e3,
        lam_cost_drop: int = -1,
        lam_cost_relax: float = 1.0,
    ):
        self.hyper = RampProximalWeightHyper(
            ramp_factor=ramp_factor,
            lam_prox_max=lam_prox_max,
            lam_cost_drop=lam_cost_drop,
            lam_cost_relax=lam_cost_relax,
        )

    def update_weights(
        self,
        state: "AlgorithmState",
        candidate: "CandidateIterate",
        nodal_constraints: "LoweredJaxConstraints",
        settings: Config,
        params: dict,
    ) -> "AlgorithmState":
        """Return the next-iterate state.

        Pure functional update — see class docstring.
        """
        lam_cost_next = self._relaxed_lam_cost(state)

        was_at_max = jnp.all(state.lam_prox >= state.hyper.lam_prox_max)
        new_lam_prox = jnp.minimum(
            state.lam_prox * state.hyper.ramp_factor, state.hyper.lam_prox_max
        )

        code = jnp.where(
            was_at_max,
            jnp.int32(AdaptiveStateCode.ACCEPT_CONSTANT),
            jnp.int32(AdaptiveStateCode.ACCEPT_HIGHER),
        )

        return state.replace(
            x=candidate.x,
            u=candidate.u,
            x_prop=candidate.x_prop,
            x_prop_plus=candidate.x_prop_plus,
            lam_prox=new_lam_prox,
            lam_cost=lam_cost_next,
            adaptive_state_code=code,
        )
update_weights(state: AlgorithmState, candidate: CandidateIterate, nodal_constraints: LoweredJaxConstraints, settings: Config, params: dict) -> AlgorithmState

Return the next-iterate state.

Pure functional update — see class docstring.

Source code in openscvx/algorithms/autotuner/ramp_proximal_weight.py 
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def update_weights(
    self,
    state: "AlgorithmState",
    candidate: "CandidateIterate",
    nodal_constraints: "LoweredJaxConstraints",
    settings: Config,
    params: dict,
) -> "AlgorithmState":
    """Return the next-iterate state.

    Pure functional update — see class docstring.
    """
    lam_cost_next = self._relaxed_lam_cost(state)

    was_at_max = jnp.all(state.lam_prox >= state.hyper.lam_prox_max)
    new_lam_prox = jnp.minimum(
        state.lam_prox * state.hyper.ramp_factor, state.hyper.lam_prox_max
    )

    code = jnp.where(
        was_at_max,
        jnp.int32(AdaptiveStateCode.ACCEPT_CONSTANT),
        jnp.int32(AdaptiveStateCode.ACCEPT_HIGHER),
    )

    return state.replace(
        x=candidate.x,
        u=candidate.u,
        x_prop=candidate.x_prop,
        x_prop_plus=candidate.x_prop_plus,
        lam_prox=new_lam_prox,
        lam_cost=lam_cost_next,
        adaptive_state_code=code,
    )

RampProximalWeightHyper

Bases: LamCostRelaxHyper

Declared hyperparameters for :class:RampProximalWeight.

Extends the shared :class:LamCostRelaxHyper lam_cost relaxation knobs with the ramp factor and the lam_prox ceiling.

Source code in openscvx/algorithms/autotuner/ramp_proximal_weight.py 
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class RampProximalWeightHyper(LamCostRelaxHyper):
    """Declared hyperparameters for :class:`RampProximalWeight`.

    Extends the shared :class:`LamCostRelaxHyper` ``lam_cost`` relaxation knobs
    with the ramp factor and the ``lam_prox`` ceiling.
    """

    ramp_factor: float = 1.0
    lam_prox_max: float = 1e3

RampProximalWeightSpec

Bases: BaseModel

Validates RampProximalWeight configuration from dict/YAML input.

Source code in openscvx/algorithms/autotuner/ramp_proximal_weight.py 
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class RampProximalWeightSpec(BaseModel):
    """Validates RampProximalWeight configuration from dict/YAML input."""

    type: Literal["RampProximalWeight"] = "RampProximalWeight"
    ramp_factor: float = 1.0
    lam_prox_max: float = 1e3
    lam_cost_drop: int = -1
    lam_cost_relax: float = 1.0

    model_config = ConfigDict(extra="forbid")

    def build(self) -> RampProximalWeight:
        return RampProximalWeight(**self.model_dump(exclude={"type"}, exclude_unset=True))