Python API Reference¶

Anderson Acceleration¶

class quala._quala.AndersonAccel

C++ documentation: quala::AndersonAccel

__init__(*args, **kwargs)

Overloaded function.

__init__(self: quala._quala.AndersonAccel, params: quala._quala.AndersonAccelParams) -> None
__init__(self: quala._quala.AndersonAccel, params: dict) -> None
__init__(self: quala._quala.AndersonAccel, params: quala._quala.AndersonAccelParams, n: int) -> None
__init__(self: quala._quala.AndersonAccel, params: dict, n: int) -> None

compute(*args, **kwargs)

Overloaded function.

compute(self: quala._quala.AndersonAccel, g_k: numpy.ndarray[numpy.float64[m, 1]], r_k: numpy.ndarray[numpy.float64[m, 1]], x_k_aa: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
compute(self: quala._quala.AndersonAccel, g_k: numpy.ndarray[numpy.float64[m, 1]], r_k: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

current_history(self: quala._quala.AndersonAccel) → int

initialize(self: quala._quala.AndersonAccel, g_0: numpy.ndarray[numpy.float64[m, 1]], r_0: numpy.ndarray[numpy.float64[m, 1]]) → None

reset(self: quala._quala.AndersonAccel) → None

resize(self: quala._quala.AndersonAccel, n: int) → None

property params

L-BFGS¶

class quala._quala.LBFGS

C++ documentation: quala::LBFGS

class Sign

C++ documentation quala::LBFGS::Sign

Members:

Positive

Negative

__init__(self: quala._quala.LBFGS.Sign, value: int) → None

Negative = <Sign.Negative: 1>

Positive = <Sign.Positive: 0>

property name

property value

__init__(*args, **kwargs)

Overloaded function.

__init__(self: quala._quala.LBFGS, params: quala._quala.LBFGSParams) -> None
__init__(self: quala._quala.LBFGS, params: dict) -> None
__init__(self: quala._quala.LBFGS, params: quala._quala.LBFGSParams, n: int) -> None
__init__(self: quala._quala.LBFGS, params: dict, n: int) -> None

apply(*args, **kwargs)

Overloaded function.

apply(self: quala._quala.LBFGS, q: numpy.ndarray[numpy.float64[m, 1], flags.writeable], γ: float) -> bool
apply(self: quala._quala.LBFGS, q: numpy.ndarray[numpy.float64[m, 1], flags.writeable], γ: float, J: List[int]) -> bool

current_history(self: quala._quala.LBFGS) → int

reset(self: quala._quala.LBFGS) → None

resize(self: quala._quala.LBFGS, n: int) → None

s(self: quala._quala.LBFGS, arg0: int) → numpy.ndarray[numpy.float64[m, 1], flags.writeable]

scale_y(self: quala._quala.LBFGS, factor: float) → None

update(self: quala._quala.LBFGS, xk: numpy.ndarray[numpy.float64[m, 1]], xkp1: numpy.ndarray[numpy.float64[m, 1]], pk: numpy.ndarray[numpy.float64[m, 1]], pkp1: numpy.ndarray[numpy.float64[m, 1]], sign: quala._quala.LBFGS.Sign = <Sign.Positive: 0>, forced: bool = False) → bool

update_sy(self: quala._quala.LBFGS, sk: numpy.ndarray[numpy.float64[m, 1]], yk: numpy.ndarray[numpy.float64[m, 1]], pkp1Tpkp1: float, forced: bool = False) → bool

static update_valid(params: quala._quala.LBFGSParams, yTs: float, sTs: float, pTp: float) → bool

y(self: quala._quala.LBFGS, arg0: int) → numpy.ndarray[numpy.float64[m, 1], flags.writeable]

α(self: quala._quala.LBFGS, arg0: int) → float

ρ(self: quala._quala.LBFGS, arg0: int) → float

Negative = <Sign.Negative: 1>

Positive = <Sign.Positive: 0>

property n

property params

All¶

Quala Quasi-Newton algorithms

class quala._quala.AndersonAccel¶

C++ documentation: quala::AndersonAccel

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: quala._quala.AndersonAccel, params: quala._quala.AndersonAccelParams) -> None
__init__(self: quala._quala.AndersonAccel, params: dict) -> None
__init__(self: quala._quala.AndersonAccel, params: quala._quala.AndersonAccelParams, n: int) -> None
__init__(self: quala._quala.AndersonAccel, params: dict, n: int) -> None

compute(*args, **kwargs)¶

Overloaded function.

compute(self: quala._quala.AndersonAccel, g_k: numpy.ndarray[numpy.float64[m, 1]], r_k: numpy.ndarray[numpy.float64[m, 1]], x_k_aa: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) -> None
compute(self: quala._quala.AndersonAccel, g_k: numpy.ndarray[numpy.float64[m, 1]], r_k: numpy.ndarray[numpy.float64[m, 1]]) -> numpy.ndarray[numpy.float64[m, 1]]

current_history(self: quala._quala.AndersonAccel) → int¶

initialize(self: quala._quala.AndersonAccel, g_0: numpy.ndarray[numpy.float64[m, 1]], r_0: numpy.ndarray[numpy.float64[m, 1]]) → None¶

reset(self: quala._quala.AndersonAccel) → None¶

resize(self: quala._quala.AndersonAccel, n: int) → None¶

property params¶

class quala._quala.AndersonAccelParams¶

C++ documentation: quala::AndersonAccelParams

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: quala._quala.AndersonAccelParams) -> None
__init__(self: quala._quala.AndersonAccelParams, **kwargs) -> None

to_dict(self: quala._quala.AndersonAccelParams) → dict¶

property memory¶

class quala._quala.BroydenGood¶

C++ documentation: quala::BroydenGood

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: quala._quala.BroydenGood, params: quala._quala.BroydenGoodParams) -> None
__init__(self: quala._quala.BroydenGood, params: dict) -> None
__init__(self: quala._quala.BroydenGood, params: quala._quala.BroydenGoodParams, n: int) -> None
__init__(self: quala._quala.BroydenGood, params: dict, n: int) -> None

apply(self: quala._quala.BroydenGood, arg0: numpy.ndarray[numpy.float64[m, 1], flags.writeable]) → bool¶: q

current_history(self: quala._quala.BroydenGood) → int¶

reset(self: quala._quala.BroydenGood) → None¶

resize(self: quala._quala.BroydenGood, n: int) → None¶

update(self: quala._quala.BroydenGood, xk: numpy.ndarray[numpy.float64[m, 1]], xkp1: numpy.ndarray[numpy.float64[m, 1]], pk: numpy.ndarray[numpy.float64[m, 1]], pkp1: numpy.ndarray[numpy.float64[m, 1]], forced: bool = False) → bool¶

update_sy(self: quala._quala.BroydenGood, sk: numpy.ndarray[numpy.float64[m, 1]], yk: numpy.ndarray[numpy.float64[m, 1]], forced: bool = False) → bool¶

property params¶

class quala._quala.BroydenGoodParams¶

C++ documentation: quala::BroydenGoodParams

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: quala._quala.BroydenGoodParams) -> None
__init__(self: quala._quala.BroydenGoodParams, **kwargs) -> None

to_dict(self: quala._quala.BroydenGoodParams) → dict¶

property force_pos_def¶

property memory¶

property min_div_abs¶

property restarted¶

class quala._quala.LBFGS¶

C++ documentation: quala::LBFGS

class Sign¶

C++ documentation quala::LBFGS::Sign

Members:

Positive

Negative

__init__(self: quala._quala.LBFGS.Sign, value: int) → None¶

Negative = <Sign.Negative: 1>¶

Positive = <Sign.Positive: 0>¶

property name¶

property value¶

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: quala._quala.LBFGS, params: quala._quala.LBFGSParams) -> None
__init__(self: quala._quala.LBFGS, params: dict) -> None
__init__(self: quala._quala.LBFGS, params: quala._quala.LBFGSParams, n: int) -> None
__init__(self: quala._quala.LBFGS, params: dict, n: int) -> None

apply(*args, **kwargs)¶

Overloaded function.

apply(self: quala._quala.LBFGS, q: numpy.ndarray[numpy.float64[m, 1], flags.writeable], γ: float) -> bool
apply(self: quala._quala.LBFGS, q: numpy.ndarray[numpy.float64[m, 1], flags.writeable], γ: float, J: List[int]) -> bool

current_history(self: quala._quala.LBFGS) → int¶

reset(self: quala._quala.LBFGS) → None¶

resize(self: quala._quala.LBFGS, n: int) → None¶

s(self: quala._quala.LBFGS, arg0: int) → numpy.ndarray[numpy.float64[m, 1], flags.writeable]¶

scale_y(self: quala._quala.LBFGS, factor: float) → None¶

update(self: quala._quala.LBFGS, xk: numpy.ndarray[numpy.float64[m, 1]], xkp1: numpy.ndarray[numpy.float64[m, 1]], pk: numpy.ndarray[numpy.float64[m, 1]], pkp1: numpy.ndarray[numpy.float64[m, 1]], sign: quala._quala.LBFGS.Sign = <Sign.Positive: 0>, forced: bool = False) → bool¶

update_sy(self: quala._quala.LBFGS, sk: numpy.ndarray[numpy.float64[m, 1]], yk: numpy.ndarray[numpy.float64[m, 1]], pkp1Tpkp1: float, forced: bool = False) → bool¶

static update_valid(params: quala._quala.LBFGSParams, yTs: float, sTs: float, pTp: float) → bool¶

y(self: quala._quala.LBFGS, arg0: int) → numpy.ndarray[numpy.float64[m, 1], flags.writeable]¶

α(self: quala._quala.LBFGS, arg0: int) → float¶

ρ(self: quala._quala.LBFGS, arg0: int) → float¶

Negative = <Sign.Negative: 1>¶

Positive = <Sign.Positive: 0>¶

property n¶

property params¶

class quala._quala.LBFGSParams¶

C++ documentation: quala::LBFGSParams

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: quala._quala.LBFGSParams) -> None
__init__(self: quala._quala.LBFGSParams, **kwargs) -> None

to_dict(self: quala._quala.LBFGSParams) → dict¶

property cbfgs¶

property force_pos_def¶

property memory¶

property min_abs_s¶

property min_div_fac¶

class quala._quala.LBFGSParamsCBFGS¶

C++ documentation: :cpp:member:`quala::LBFGSParams::CBFGSParams `

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: quala._quala.LBFGSParamsCBFGS) -> None
__init__(self: quala._quala.LBFGSParamsCBFGS, **kwargs) -> None

to_dict(self: quala._quala.LBFGSParamsCBFGS) → dict¶

property α¶

property ϵ¶