Python API Reference

Anderson Acceleration

class quala._quala.AndersonAccel

C++ documentation: quala::AndersonAccel

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: quala._quala.AndersonAccel, params: quala._quala.AndersonAccelParams) -> None

2. __init__(self: quala._quala.AndersonAccel, params: dict) -> None

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

4. __init__(self: quala._quala.AndersonAccel, params: dict, n: int) -> 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]]

compute_inplace(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

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

property params

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

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

L-BFGS

class quala._quala.LBFGS

C++ documentation: quala::LBFGS

Negative = <Sign.Negative: 1>

Positive = <Sign.Positive: 0>

class Sign

C++ documentation quala::LBFGS::Sign

Members:

Positive

Negative

Negative = <Sign.Negative: 1>

Positive = <Sign.Positive: 0>

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

property name

property value

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: quala._quala.LBFGS, params: quala._quala.LBFGSParams) -> None

2. __init__(self: quala._quala.LBFGS, params: dict) -> None

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

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

apply(*args, **kwargs)

Overloaded function.

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

2. 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

property n

property params

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

All

Quala Quasi-Newton algorithms

class quala._quala.AndersonAccel

C++ documentation: quala::AndersonAccel

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: quala._quala.AndersonAccel, params: quala._quala.AndersonAccelParams) -> None

2. __init__(self: quala._quala.AndersonAccel, params: dict) -> None

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

4. __init__(self: quala._quala.AndersonAccel, params: dict, n: int) -> 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]]

compute_inplace(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

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

property params

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

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

class quala._quala.AndersonAccelParams

C++ documentation: quala::AndersonAccelParams

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: quala._quala.AndersonAccelParams) -> None

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

property memory

property min_div

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

class quala._quala.BroydenGood

C++ documentation: quala::BroydenGood

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: quala._quala.BroydenGood, params: quala._quala.BroydenGoodParams) -> None

2. __init__(self: quala._quala.BroydenGood, params: dict) -> None

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

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

apply(self: quala._quala.BroydenGood, q: numpy.ndarray[numpy.float64[m, 1], flags.writeable], γ: float = -1) → bool

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

property params

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

class quala._quala.BroydenGoodParams

C++ documentation: quala::BroydenGoodParams

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: quala._quala.BroydenGoodParams) -> None

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

property force_pos_def

property memory

property min_div_abs

property min_stepsize

property powell_damping_factor

property restarted

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

class quala._quala.LBFGS

C++ documentation: quala::LBFGS

Negative = <Sign.Negative: 1>

Positive = <Sign.Positive: 0>

class Sign

C++ documentation quala::LBFGS::Sign

Members:

Positive

Negative

Negative = <Sign.Negative: 1>

Positive = <Sign.Positive: 0>

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

property name

property value

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: quala._quala.LBFGS, params: quala._quala.LBFGSParams) -> None

2. __init__(self: quala._quala.LBFGS, params: dict) -> None

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

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

apply(*args, **kwargs)

Overloaded function.

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

2. 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

property n

property params

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

class quala._quala.LBFGSParams

C++ documentation: quala::LBFGSParams

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: quala._quala.LBFGSParams) -> None

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

property cbfgs

property force_pos_def

property memory

property min_abs_s

property min_div_fac

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

class quala._quala.LBFGSParamsCBFGS

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

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: quala._quala.LBFGSParamsCBFGS) -> None

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

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

property α

property ϵ

class quala._quala.LimitedMemoryQR

property Q

property R

__init__(*args, **kwargs)

Overloaded function.

1. __init__(self: quala._quala.LimitedMemoryQR) -> None

2. __init__(self: quala._quala.LimitedMemoryQR, n: int, m: int) -> None

add_column(self: quala._quala.LimitedMemoryQR, v: numpy.ndarray[numpy.float64[m, 1]]) → None

clear_reorth_count(self: quala._quala.LimitedMemoryQR) → None

property current_history

property history

property m

property max_eig

property min_eig

property n

property num_columns

remove_column(self: quala._quala.LimitedMemoryQR) → None

property reorth_count

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

resize(self: quala._quala.LimitedMemoryQR, arg0: int, arg1: int) → None

property size

solve(*args, **kwargs)

Overloaded function.

1. solve(self: quala._quala.LimitedMemoryQR, b: numpy.ndarray[numpy.float64[m, n], flags.f_contiguous], x: numpy.ndarray[numpy.float64[m, n], flags.writeable, flags.f_contiguous], tol: float = 0) -> None

2. solve(self: quala._quala.LimitedMemoryQR, b: numpy.ndarray[numpy.float64[m, n], flags.f_contiguous], tol: float = 0) -> numpy.ndarray[numpy.float64[m, n]]