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Binary files a/_images/sampler-1_00_00.png and b/_images/sampler-1_00_00.png differ diff --git a/_images/sampler-1_01_00.png b/_images/sampler-1_01_00.png index d5c118bd..e52968f4 100644 Binary files a/_images/sampler-1_01_00.png and b/_images/sampler-1_01_00.png differ diff --git a/_parse_plugins/plugins.db b/_parse_plugins/plugins.db index d0a781e4..cc6f80a3 100644 Binary files a/_parse_plugins/plugins.db and b/_parse_plugins/plugins.db differ diff --git a/api/abc.html b/api/abc.html index b6cfa095..0bfdd9ea 100644 --- a/api/abc.html +++ b/api/abc.html @@ -304,7 +304,7 @@

pyxu.abc.arithmetic
class Rule[source]#
-

Bases: object

+

Bases: object

General arithmetic rule.

This class defines default arithmetic rules applicable unless re-defined by sub-classes.

@@ -374,7 +374,7 @@

pyxu.abc.arithmeticParameters:

@@ -433,7 +433,7 @@

pyxu.abc.arithmeticParameters:
@@ -683,7 +683,7 @@

pyxu.abc.operator
class Property(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
-

Bases: Enum

+

Bases: Enum

Mathematical property.

See also

@@ -747,7 +747,7 @@

pyxu.abc.operator

Instance methods affected by arithmetic operations.

Return type:
-

Set[str]

+

Set[str]

@@ -755,7 +755,7 @@

pyxu.abc.operator
class Operator(dim_shape, codim_shape)[source]#
-

Bases: object

+

Bases: object

Abstract Base Class for Pyxu operators.

Goals:

@@ -830,10 +830,10 @@

pyxu.abc.operator

Verify if operator possesses supplied properties.

Parameters:
-

prop (Property | Collection[Property])

+

prop (Property | Collection[Property])

Return type:
-

bool

+

bool

@@ -861,7 +861,7 @@

pyxu.abc.operator

  • The interface of cast_to is provided via encapsulation + forwarding.

  • If self does not implement all methods from cast_to, then unimplemented methods will raise -NotImplementedError when called.

    • +NotImplementedError when called.

    @@ -957,7 +957,7 @@

    pyxu.abc.operator__pow__(k)[source]#
    Parameters:
    -

    k (Integral)

    +

    k (Integral)

    Return type:

    OpT

    @@ -1035,12 +1035,12 @@

    pyxu.abc.operator
    Parameters:
    Return type:
    -

    str

    +

    str

    @@ -1054,7 +1054,7 @@

    pyxu.abc.operator
    Parameters:
    -

    axes (Integral | tuple[Integral, ...])

    +

    axes (Integral | tuple[Integral, ...])

    Return type:

    OpT

    @@ -1071,7 +1071,7 @@

    pyxu.abc.operator
    Parameters:
    -

    axes (Integral | tuple[Integral, ...])

    +

    axes (Integral | tuple[Integral, ...])

    Return type:

    OpT

    @@ -1088,7 +1088,7 @@

    pyxu.abc.operator
    Parameters:
    -

    codim_shape (Integral | tuple[Integral, ...])

    +

    codim_shape (Integral | tuple[Integral, ...])

    Return type:

    OpT

    @@ -1105,7 +1105,7 @@

    pyxu.abc.operator
    Parameters:
    -

    codim_shape (Integral | tuple[Integral, ...])

    +

    codim_shape (Integral | tuple[Integral, ...])

    Return type:

    OpT

    @@ -1136,7 +1136,7 @@

    pyxu.abc.operator
    Parameters:
    -

    chunks (dict)

    +

    chunks (dict)

    Return type:

    OpT

    @@ -1156,8 +1156,8 @@

    pyxu.abc.operator
    Parameters:

    @@ -1167,7 +1167,7 @@

    pyxu.abc.operator

    Mathematical properties of the operator.

    Return type:
    -

    Set[Property]

    +

    Set[Property]

    @@ -1202,7 +1202,7 @@

    pyxu.abc.operator

    -property lipschitz: Real#
    +property lipschitz: Real#

    Return the last computed Lipschitz constant of \(\mathbf{f}\).

    Notes

    @@ -1299,8 +1299,8 @@

    pyxu.abc.operator
    Parameters:

    @@ -1334,7 +1334,7 @@

    pyxu.abc.operator

    -property diff_lipschitz: Real#
    +property diff_lipschitz: Real#

    Return the last computed Lipschitz constant of \(\mathbf{J}_{\mathbf{f}}\).

    Notes

    @@ -1463,8 +1463,8 @@

    pyxu.abc.operator
    Parameters:
    @@ -1619,8 +1619,8 @@

    pyxu.abc.operator
    Parameters:
    @@ -1663,11 +1663,11 @@

    pyxu.abc.operator
    Parameters:
    @@ -1680,8 +1680,8 @@

    pyxu.abc.operator

    Q (PosDefOp) – Positive-definite operator. (Default: Identity)

  • c (LinFunc) – Linear functional. (Default: NullFunc)

  • t (Real) – Offset. (Default: 0)

    • -

  • dim_shape (Integral | tuple[Integral, ...])

    • -

  • codim_shape (Integral | tuple[Integral, ...])

    • +

  • dim_shape (Integral | tuple[Integral, ...])

    • +

  • codim_shape (Integral | tuple[Integral, ...])

    • @@ -1705,8 +1705,8 @@

    pyxu.abc.operator
    Parameters:
    @@ -1763,7 +1763,7 @@

    pyxu.abc.operator
    Return type:
    -

    Real

    +

    Real

    Notes

    @@ -1787,9 +1787,9 @@

    pyxu.abc.operatorParameters:
    Returns:
    @@ -1873,8 +1873,8 @@

    pyxu.abc.operator
    Returns:
    @@ -1921,8 +1921,8 @@

    pyxu.abc.operatorParameters:
    Returns:
    @@ -1942,7 +1942,7 @@

    pyxu.abc.operator
    Returns:
    @@ -1963,8 +1963,8 @@

    pyxu.abc.operator
    Parameters:
    @@ -1980,7 +1980,7 @@

    pyxu.abc.operator

    If hutchpp, compute an approximation. (Default)

    -

  • kwargs (Mapping) –

    Optional kwargs passed to:

    +

  • kwargs (Mapping) –

    Optional kwargs passed to:

    @@ -2259,7 +2259,7 @@

    pyxu.abc.solver<
    steps(n=None)[source]#

    Generator of logged variables after each iteration.

    -

    The i-th call to next() on this object returns the logged variables after the i-th solver iteration.

    +

    The i-th call to next() on this object returns the logged variables after the i-th solver iteration.

    This method is only usable after calling fit() with mode=MANUAL. See Solver for usage examples.

    There is no guarantee that a checkpoint on disk exists when the generator is exhausted. (Reason: potential @@ -2267,14 +2267,14 @@

    pyxu.abc.solver< afterwards if needed.

    Parameters:
    -

    n (Integer) –

    Maximum number of next() calls allowed before exhausting the generator. Defaults to infinity if +

    n (Integer) –

    Maximum number of next() calls allowed before exhausting the generator. Defaults to infinity if unspecified.

    -

    The generator will terminate prematurely if the solver naturally stops before n calls to next() +

    The generator will terminate prematurely if the solver naturally stops before n calls to next() are made.

    Return type:
    -

    Generator

    +

    Generator

    @@ -2285,13 +2285,13 @@

    pyxu.abc.solver<
    Returns:

      -

    • data (Mapping) – Value(s) of log_var (s) after last iteration.

      • -

    • history (numpy.ndarray, None) – (N_iter,) records of stopping-criteria values sampled every stop_rate iteration.

      • +

    • data (Mapping) – Value(s) of log_var (s) after last iteration.

      • +

    • history (numpy.ndarray, None) – (N_iter,) records of stopping-criteria values sampled every stop_rate iteration.

    Return type:
    -

    tuple[dict[str, Real | NDArray | None], ndarray | None]

    +

    tuple[dict[str, Real | NDArray | None], ndarray | None]

    Notes

    @@ -2299,7 +2299,7 @@

    pyxu.abc.solver<
    -property workdir: str | Path#
    +property workdir: str | Path#
    Returns:

    wd – Absolute path to the directory on disk where instance data is stored.

    @@ -2311,7 +2311,7 @@

    pyxu.abc.solver<

    -property logfile: str | Path#
    +property logfile: str | Path#
    Returns:

    lf – Absolute path to the log file on disk where stopping criteria statistics are logged.

    @@ -2323,7 +2323,7 @@

    pyxu.abc.solver<

    -property datafile: str | Path#
    +property datafile: str | Path#
    Returns:

    df – Absolute path to the file on disk where log_var (s) are stored during checkpointing or after solver has @@ -2345,7 +2345,7 @@

    pyxu.abc.solver<

    b – True if solver has stopped, False otherwise.

    Return type:
    -

    bool

    +

    bool

    @@ -2405,7 +2405,7 @@

    pyxu.abc.solver<
    class SolverMode(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
    -

    Bases: Enum

    +

    Bases: Enum

    Solver execution mode.

    @@ -2423,7 +2423,7 @@

    pyxu.abc.solver<
    class StoppingCriterion[source]#
    -

    Bases: object

    +

    Bases: object

    State machines (SM) which decide when to stop iterative solvers by examining their mathematical state.

    SM decisions are always accompanied by at least one numerical statistic. These stats may be queried by solvers via info() to provide diagnostic information to users.

    @@ -2434,7 +2434,7 @@

    pyxu.abc.solver<

    Compute a stop signal based on the current mathematical state.

    Parameters:
    -

    state (Mapping) –

    Full mathematical state of solver at some iteration, i.e. _mstate.

    +

    state (Mapping) –

    Full mathematical state of solver at some iteration, i.e. _mstate.

    Values from state may be cached inside the instance to form complex stopping conditions.

    @@ -2442,7 +2442,7 @@

    pyxu.abc.solver<

    s – True if no further iterations should be performed, False otherwise.

    Return type:
    -

    bool

    +

    bool

    @@ -2455,7 +2455,7 @@

    pyxu.abc.solver<

    data

    Return type:
    -

    Mapping

    +

    Mapping

    diff --git a/api/experimental/sampler.html b/api/experimental/sampler.html index 9597592d..85424f0b 100644 --- a/api/experimental/sampler.html +++ b/api/experimental/sampler.html @@ -337,8 +337,8 @@

    @@ -348,8 +348,8 @@
    Parameters:
      -

    • f (DiffFunc, None) – Differentiable functional.

      • -

    • g (ProxFunc, None) – Proximable functional.

      • +

    • f (DiffFunc, None) – Differentiable functional.

      • +

    • g (ProxFunc, None) – Proximable functional.

    • gamma (Real) – Euler-Maruyama discretization step of the Langevin equation (see Notes of ULA documentation).

    • lamb (Real) – Moreau-Yosida envelope parameter for g.

      • @@ -376,7 +376,7 @@ \sum_{k=1}^K (\mathbf{x}_k-\boldsymbol{\mu})^{i+2}\) for \(0 \leq i \leq d-2\).

      Parameters:
      -

      order (Real)

      +

      order (Real)

      @@ -415,7 +415,7 @@ k \leq K}\) is given by \(\frac{1}{K}\sum_{k=1}^K \mathbf{x}_k^d\).

      Parameters:
      -

      order (Real)

      +

      order (Real)

      @@ -644,7 +644,7 @@

      Remarks#< \(\mathcal{F}\).

      Return type:
      -

      Real

      +

      Real

    @@ -653,7 +653,7 @@

    Remarks#<
    Parameters:

    diff --git a/api/index.html b/api/index.html index c69c9b7e..f3396efb 100644 --- a/api/index.html +++ b/api/index.html @@ -762,7 +762,7 @@

    pyxu.info.ptype<

    NDArray dtype specifier.

    Integer

    -

    alias of Integral

    +

    alias of Integral

    NDArray

    Supported dense array types.

    diff --git a/api/info.deps.html b/api/info.deps.html index b1989450..1e0ecf9e 100644 --- a/api/info.deps.html +++ b/api/info.deps.html @@ -503,14 +503,14 @@

    pyxu.info.deps#

    -CUPY_ENABLED: bool#
    +CUPY_ENABLED: bool#

    Show if CuPy-based backends are available.

    class NDArrayInfo(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
    -

    Bases: Enum

    +

    Bases: Enum

    Supported dense array backends.

    @@ -544,7 +544,7 @@

    Array type associated to a backend.

    Return type:
    -

    type

    +

    type

    @@ -566,7 +566,7 @@

    Find array backend suitable for in-memory CPU/GPU computing.

    Parameters:
    -

    gpu (bool)

    +

    gpu (bool)

    Return type:

    NDArrayInfo

    @@ -580,10 +580,10 @@

    Python module associated to an array backend.

    Parameters:
    -

    linalg (bool) – Return the linear-algebra submodule with identical API to numpy.linalg.

    +

    linalg (bool) – Return the linear-algebra submodule with identical API to numpy.linalg.

    Return type:
    -

    ModuleType

    +

    ModuleType

    @@ -593,7 +593,7 @@
    class SparseArrayInfo(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
    -

    Bases: Enum

    +

    Bases: Enum

    Supported sparse array backends.

    @@ -622,7 +622,7 @@

    Array type associated to a backend.

    Return type:
    -

    type

    +

    type

    @@ -644,10 +644,10 @@

    Python module associated to an array backend.

    Parameters:
    -

    linalg (bool) – Return the linear-algebra submodule with identical API to scipy.sparse.linalg.

    +

    linalg (bool) – Return the linear-algebra submodule with identical API to scipy.sparse.linalg.

    Return type:
    -

    ModuleType

    +

    ModuleType

    @@ -660,7 +660,7 @@

    List of all supported dense array types in current Pyxu install.

    Return type:
    -

    Collection[type]

    +

    Collection[type]

    @@ -671,7 +671,7 @@

    List of all supported dense array modules in current Pyxu install.

    Return type:
    -

    Collection[ModuleType]

    +

    Collection[ModuleType]

    @@ -682,7 +682,7 @@

    List of all supported sparse array types in current Pyxu install.

    Return type:
    -

    Collection[type]

    +

    Collection[type]

    @@ -693,7 +693,7 @@

    List of all supported sparse array modules in current Pyxu install.

    Return type:
    -

    Collection[ModuleType]

    +

    Collection[ModuleType]

    diff --git a/api/info.ptype.html b/api/info.ptype.html index f8901704..d3dd0e5b 100644 --- a/api/info.ptype.html +++ b/api/info.ptype.html @@ -552,7 +552,7 @@

    pyxu.info.ptype
    class Real[source]#
    -

    Alias of numbers.Real.

    +

    Alias of numbers.Real.

    @@ -565,48 +565,48 @@

    pyxu.info.ptype OpC#

    Operator hierarchy class type.

    -

    alias of Type[OpT]

    +

    alias of Type[OpT]

    SolverC#

    Solver hierarchy class type.

    -

    alias of Type[SolverT]

    +

    alias of Type[SolverT]

    Integer#
    -

    alias of Integral

    +

    alias of Integral

    NDArrayAxis#

    Axis/Axes specifier.

    -

    alias of Integral | tuple[Integral, …]

    +

    alias of Integral | tuple[Integral, …]

    NDArrayShape#

    NDArray shape specifier.

    -

    alias of Integral | tuple[Integral, …]

    +

    alias of Integral | tuple[Integral, …]

    Path#

    Path-like object.

    -

    alias of str | Path

    +

    alias of str | Path

    VarName#

    Variable name(s).

    -

    alias of str | Collection[str]

    +

    alias of str | Collection[str]

    diff --git a/api/info.warning.html b/api/info.warning.html index c63ee83f..cc825049 100644 --- a/api/info.warning.html +++ b/api/info.warning.html @@ -504,7 +504,7 @@
    exception PyxuWarning[source]#
    -

    Bases: UserWarning

    +

    Bases: UserWarning

    Parent class of all warnings raised in Pyxu.

    @@ -529,7 +529,7 @@

    This method is aware of its context and prints the name of the enclosing function/method which invoked it.

    Parameters:
    -

    msg (str) – Custom warning message.

    +

    msg (str) – Custom warning message.

    diff --git a/api/operator.interop.html b/api/operator.interop.html index edee4d4c..15da1d13 100644 --- a/api/operator.interop.html +++ b/api/operator.interop.html @@ -524,11 +524,11 @@

    GeneralOpC) – Operator sub-class to instantiate.

  • dim_shape (NDArrayShape) – Operator domain shape (M1,…,MD).

  • codim_shape (NDArrayShape) – Operator co-domain shape (N1,…,NK).

    • -

  • embed (dict) –

    (k[str], v[value]) pairs to embed into the created operator.

    +

  • embed (dict) –

    (k[str], v[value]) pairs to embed into the created operator.

    embed is useful to attach extra information to synthesized Operator used by arithmetic methods.

    • -

  • kwargs (dict) –

    (k[str], v[callable]) pairs to use as arithmetic methods.

    +

  • kwargs (dict) –

    (k[str], v[callable]) pairs to use as arithmetic methods.

    Keys must be entries from arithmetic_methods().

    Omitted arithmetic attributes/methods default to those provided by cls.

    • @@ -609,7 +609,7 @@

    SciPyParameters:
    Returns:
    @@ -639,7 +639,7 @@

    JAXOpC) – Operator sub-class to instantiate.

  • dim_shape (NDArrayShape) – Operator domain shape (M1,…,MD).

  • codim_shape (NDArrayShape) – Operator co-domain shape (N1,…,NK).

    • -

  • kwargs (dict) –

    (k[str], v[callable]) pairs to use as arithmetic methods.

    +

  • kwargs (dict) –

    (k[str], v[callable]) pairs to use as arithmetic methods.

    Keys are restricted to the following arithmetic methods:

    apply(), grad(), prox(), pinv(), adjoint()
    @@ -649,8 +649,8 @@

    JAXVarName) –

    Arithmetic methods to vectorize.

    vectorize is useful if an arithmetic method provided to kwargs does not support stacking dimensions.

    • -

  • jit (bool) – If True, JIT-compile JAX-backed arithmetic methods for better performance.

    • -

  • enable_warnings (bool) – If True, emit warnings in case of precision/zero-copy issues.

    • +

  • jit (bool) – If True, JIT-compile JAX-backed arithmetic methods for better performance.

    • +

  • enable_warnings (bool) – If True, emit warnings in case of precision/zero-copy issues.

    • Returns:
    @@ -761,7 +761,7 @@

    JAXParameters:
    Return type:
    @@ -783,7 +783,7 @@

    PyTorchOpC) – Operator sub-class to instantiate.

  • dim_shape (NDArrayShape) – Operator domain shape (M1,…,MD).

  • codim_shape (NDArrayShape) – Operator co-domain shape (N1,…,NK).

    • -

  • kwargs (dict) –

    (k[str], v[callable]) pairs to use as arithmetic methods.

    +

  • kwargs (dict) –

    (k[str], v[callable]) pairs to use as arithmetic methods.

    Keys are restricted to the following arithmetic methods:

    apply(), grad(), prox(), pinv(), adjoint()
    @@ -793,9 +793,9 @@

    PyTorchVarName) –

    Arithmetic methods to vectorize.

    vectorize is useful if an arithmetic method provided to kwargs does not support stacking dimensions.

    • -

  • jit (bool) – Currently has no effect (for future-compatibility only). In the future, if True, then Torch-backed +

  • jit (bool) – Currently has no effect (for future-compatibility only). In the future, if True, then Torch-backed arithmetic methods will be JIT-compiled for better performance.

    • -

  • enable_warnings (bool) – If True, emit warnings in case of precision/zero-copy issues.

    • +

  • enable_warnings (bool) – If True, emit warnings in case of precision/zero-copy issues.

    • Returns:
    @@ -867,7 +867,7 @@

    PyTorchParameters:
    Returns:
    diff --git a/api/operator/blocks.html b/api/operator/blocks.html index df23526f..8f346577 100644 --- a/api/operator/blocks.html +++ b/api/operator/blocks.html @@ -527,7 +527,7 @@

    pyxu.operator.blocks
    Parameters:
    -

    ops (Sequence ( OpT )) – (Q,) identically-shaped operators to map over inputs.

    +

    ops (Sequence ( OpT )) – (Q,) identically-shaped operators to map over inputs.

    Returns:

    op – Stacked (M1,…,MD) -> (Q, N1,…,NK) operator.

    @@ -582,7 +582,7 @@

    pyxu.operator.blocksEach sub-operator \(O_{q}\) acts on the \(q\)-th slice of the inputs along the zero-th axis.

    Parameters:
    -

    ops (Sequence ( OpT )) – (Q,) identically-shaped operators to zip over inputs.

    +

    ops (Sequence ( OpT )) – (Q,) identically-shaped operators to zip over inputs.

    Returns:

    op – Block-diagonal (Q, M1,…,MD) -> (Q, N1,…,NK) operator.

    diff --git a/api/operator/func.html b/api/operator/func.html index 412a230a..326ae4b2 100644 --- a/api/operator/func.html +++ b/api/operator/func.html @@ -522,7 +522,7 @@

    Norms & Loss Func

    \(\ell_{1}\)-norm, \(\Vert\mathbf{x}\Vert_{1} := \sum_{i} |x_{i}|\).

    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -534,7 +534,7 @@

    Norms & Loss Func

    \(\ell_{2}\)-norm, \(\Vert\mathbf{x}\Vert_{2} := \sqrt{\sum_{i} |x_{i}|^{2}}\).

    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -546,7 +546,7 @@

    Norms & Loss Func

    \(\ell^{2}_{2}\)-norm, \(\Vert\mathbf{x}\Vert^{2}_{2} := \sum_{i} |x_{i}|^{2}\).

    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -565,7 +565,7 @@

    Norms & Loss Func
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -677,7 +677,7 @@

    Norms & Loss Func
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -690,8 +690,8 @@

    Norms & Loss Func
    Parameters:
    @@ -702,7 +702,7 @@

    Norms & Loss Func
    Parameters:
    @@ -729,7 +729,7 @@

    Norms & Loss Func
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -855,7 +855,7 @@

    Indicator Functions
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -884,8 +884,8 @@

    Indicator FunctionsParameters:
    diff --git a/api/operator/linop.html b/api/operator/linop.html index 509896bd..92275035 100644 --- a/api/operator/linop.html +++ b/api/operator/linop.html @@ -353,20 +353,20 @@

    Basic Operators<
    Parameters:
    IndexSpec#
    -

    alias of Integral | Sequence[Integral] | Sequence[bool] | slice

    +

    alias of Integral | Sequence[Integral] | Sequence[bool] | slice

    TrimSpec#
    -

    alias of Integral | Sequence[Integral] | Sequence[tuple[Integral, Integral]]

    +

    alias of Integral | Sequence[Integral] | Sequence[tuple[Integral, Integral]]

    @@ -478,8 +478,8 @@

    Basic Operators<
    Parameters:
    @@ -561,7 +561,7 @@

    Basic Operators<
    Return type:
    -

    Real

    +

    Real

    Notes

    @@ -585,7 +585,7 @@

    Basic Operators<

    Identity operator.

    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -598,8 +598,8 @@

    Basic Operators<
    Parameters:
    @@ -611,7 +611,7 @@

    Basic Operators<

    This functional maps any input vector on the null scalar.

    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    Return type:

    OpT

    @@ -626,7 +626,7 @@

    Basic Operators<
    Parameters:
    Returns:
    @@ -664,7 +664,7 @@

    Basic Operators<

  • dim_shape (NDArrayShape) – (M1,…,MD) shape of operator’s domain. Defaults to the shape of vec when omitted.

  • vec (NDArray) – Scale factors. If dim_shape is provided, then vec must be broadcastable with arrays of size dim_shape.

    • -

  • enable_warnings (bool) – If True, emit a warning in case of precision mis-match issues.

    • +

  • enable_warnings (bool) – If True, emit a warning in case of precision mis-match issues.

    • Return type:
    @@ -771,21 +771,21 @@

    Basic Operators<
    Parameters:
    WidthSpec#
    -

    alias of Integral | Sequence[Integral] | Sequence[tuple[Integral, Integral]]

    +

    alias of Integral | Sequence[Integral] | Sequence[tuple[Integral, Integral]]

    ModeSpec#
    -

    alias of str | Sequence[str]

    +

    alias of str | Sequence[str]

    @@ -803,7 +803,7 @@

    Basic Operators< pad_width[k][1] respectively.

    -

  • mode (str, list ( str )) –

    Padding mode. +

  • mode (str, list ( str )) –

    Padding mode. Multiple forms are accepted:

    • @@ -941,7 +941,7 @@

    Transforms

    @@ -1030,10 +1030,10 @@

    Transforms @@ -1079,7 +1079,7 @@

    Stencils & Convo
    class _Stencil(kernel, center)[source]#
    -

    Bases: object

    +

    Bases: object

    Multi-dimensional JIT-compiled stencil. (Low-level function.)

    This low-level class creates a gu-vectorized stencil applicable on multiple inputs simultaneously. Only NUMPY/CUPY arrays are accepted.

    @@ -1116,7 +1116,7 @@

    Stencils & Convo
    IndexSpec#
    -

    alias of Sequence[Integral]

    +

    alias of Sequence[Integral]

    @@ -1147,7 +1147,7 @@

    Stencils & Convo

    @@ -1559,7 +1559,7 @@

    Stencils & Convo

    -property kernel: NDArray | Sequence[NDArray]#
    +property kernel: NDArray | Sequence[NDArray]#

    Stencil kernel coefficients.

    Returns:
    @@ -1575,7 +1575,7 @@

    Stencils & Convo

    -property center: Sequence[Integral]#
    +property center: Sequence[Integral]#

    Stencil central position.

    Returns:
    @@ -1607,7 +1607,7 @@

    Stencils & Convo
    Return type:
    -

    str

    +

    str

    @@ -1689,11 +1689,11 @@

    Stencils & Convo
    Parameters:
    @@ -1704,11 +1704,11 @@

    Stencils & Convo
    Parameters:
    @@ -1742,11 +1742,11 @@

    Stencils & Convo
    Parameters:
    @@ -1771,7 +1771,7 @@

    Stencils & Convo

  • center (IndexSpec) –

    (i1,…,iD) index of the kernel’s center.

    center defines how a kernel is overlaid on inputs to produce outputs.

    • -

  • mode (str, list ( str )) –

    Boundary conditions. Multiple forms are accepted:

    +

  • mode (str, list ( str )) –

    Boundary conditions. Multiple forms are accepted:

    (See numpy.pad() for details.)

    • -

  • enable_warnings (bool) – If True, emit a warning in case of precision mis-match issues.

    • -

  • kwargs (dict) – Extra kwargs forwarded to FFT.

    • +

  • enable_warnings (bool) – If True, emit a warning in case of precision mis-match issues.

    • +

  • kwargs (dict) – Extra kwargs forwarded to FFT.

    • @@ -1836,11 +1836,11 @@

    Stencils & Convo
    Parameters:
    @@ -1851,11 +1851,11 @@

    Stencils & Convo
    Parameters:
    @@ -1881,7 +1881,7 @@

    FiltersParameters:
    @@ -1966,7 +1966,7 @@

    Filters
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1997,19 +1997,19 @@

    FiltersParameters:
    @@ -2075,11 +2075,11 @@

    Filters
    Parameters:
    @@ -2098,7 +2098,7 @@

    FiltersParameters:
    @@ -2171,11 +2171,11 @@

    FiltersParameters:
    @@ -2248,11 +2248,11 @@

    FiltersParameters:
    @@ -2325,11 +2325,11 @@

    FiltersParameters:
    @@ -2528,14 +2528,14 @@

    Remark#
    Parameters:
    @@ -2546,7 +2546,7 @@

    Derivatives
    class PartialDerivative[source]#
    -

    Bases: object

    +

    Bases: object

    Partial derivative operator based on Numba stencils.

    Notes

    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -708,7 +708,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -778,7 +778,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -848,7 +848,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -918,7 +918,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -988,7 +988,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1059,9 +1059,9 @@

    Element-wise Operator
    Parameters:
    @@ -1104,7 +1104,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1174,7 +1174,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1245,8 +1245,8 @@

    Element-wise Operator
    Parameters:
    @@ -1317,7 +1317,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1381,8 +1381,8 @@

    Element-wise Operator
    Parameters:
    @@ -1425,8 +1425,8 @@

    Element-wise Operator
    Parameters:
    @@ -1490,7 +1490,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1514,7 +1514,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1577,7 +1577,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1618,7 +1618,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1689,7 +1689,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1759,7 +1759,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1829,7 +1829,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1899,7 +1899,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -1969,7 +1969,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -2039,7 +2039,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -2110,7 +2110,7 @@

    Element-wise Operator
    Parameters:
    -

    dim_shape (Integral | tuple[Integral, ...])

    +

    dim_shape (Integral | tuple[Integral, ...])

    @@ -2172,9 +2172,9 @@

    Misc
    Parameters:
    Return type:
    @@ -2191,8 +2191,8 @@

    Misc
    Parameters:

    @@ -2206,7 +2206,7 @@

    MiscIntegral | tuple[Integral, ...])

    +

  • dim_shape (Integral | tuple[Integral, ...])

    • @@ -2222,8 +2222,8 @@

    Misc
    Parameters:

    @@ -2237,7 +2237,7 @@

    MiscIntegral | tuple[Integral, ...])

    +

  • dim_shape (Integral | tuple[Integral, ...])

    • @@ -2258,7 +2258,7 @@

    MiscParameters:

    @@ -2337,8 +2337,8 @@

    Misc
    Parameters:

    @@ -2363,7 +2363,7 @@

    MiscReturn type: -

    Real

    +

    Real

    Notes

    @@ -2388,9 +2388,9 @@

    MiscParameters:

    • k (Integer) – Number of singular values to compute.

      • -

    • gpu (bool) – If True the singular value decomposition is performed on the GPU.

      • +

    • gpu (bool) – If True the singular value decomposition is performed on the GPU.

    • dtype (DType) – Working precision of the linear operator.

      • -

    • kwargs (Mapping) – Additional kwargs accepted by svds().

      • +

    • kwargs (Mapping) – Additional kwargs accepted by svds().

    Returns:
    @@ -2432,8 +2432,8 @@

    Misc

    • arr (NDArray) – (…, N1,…,NK) input points.

    • damp (Real) – Positive dampening factor regularizing the pseudo-inverse.

      • -

    • kwargs_init (Mapping) – Optional kwargs to be passed to CG()’s __init__() method.

      • -

    • kwargs_fit (Mapping) – Optional kwargs to be passed to CG()’s fit() method.

      • +

    • kwargs_init (Mapping) – Optional kwargs to be passed to CG()’s __init__() method.

      • +

    • kwargs_fit (Mapping) – Optional kwargs to be passed to CG()’s fit() method.

    Returns:
    @@ -2481,8 +2481,8 @@

    MiscParameters:

    • damp (Real) – Positive dampening factor regularizing the pseudo-inverse.

      • -

    • kwargs_init (Mapping) – Optional kwargs to be passed to CG()’s __init__() method.

      • -

    • kwargs_fit (Mapping) – Optional kwargs to be passed to CG()’s fit() method.

      • +

    • kwargs_init (Mapping) – Optional kwargs to be passed to CG()’s __init__() method.

      • +

    • kwargs_fit (Mapping) – Optional kwargs to be passed to CG()’s fit() method.

    Returns:
    diff --git a/api/opt.solver.html b/api/opt.solver.html index b3fdff8c..49a6c158 100644 --- a/api/opt.solver.html +++ b/api/opt.solver.html @@ -333,13 +333,13 @@

    Remarks#<

    Parameters (__init__())#

    @@ -350,17 +350,17 @@

    Parameters (fit

  • x0 (NDArray) – (…, M1,…,MD) initial point(s).

    • -

  • tau (Real, None) +

  • tau (Real, None) – Gradient step size. Defaults to \(1 / \beta\) if unspecified.

    • -

  • acceleration (bool) +

  • acceleration (bool) – If True (default), then use Chambolle & Dossal acceleration scheme.

  • d (Real) – Chambolle & Dossal acceleration parameter \(d\). Should be greater than 2. Only meaningful if acceleration is True. Defaults to 75 in unspecified.

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.fit().

    • @@ -397,7 +397,7 @@

    Parameters (__i – Positive-definite operator \(\mathbf{A}: \mathbb{R}^{M_{1} \times\cdots\times M_{D}} \to \mathbb{R}^{M_{1} \times\cdots\times M_{D}}\).

    -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.__init__().

    • @@ -410,7 +410,7 @@

    Parameters (fit (…, M1,…,MD) \(\mathbf{b}\) terms in the CG cost function.

    All problems are solved in parallel.

    -

  • x0 (NDArray, None) +

  • x0 (NDArray, None) – (…, M1,…,MD) initial point(s).

    Must be broadcastable with b if provided. Defaults to 0.

    @@ -420,7 +420,7 @@

    Parameters (fit Number of iterations after which restart is applied.

    By default, a restart is done after ‘n’ iterations, where ‘n’ corresponds to the dimension of \(\mathbf{A}\).

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.fit().

    • @@ -479,7 +479,7 @@

    Parameters (__i

  • f (DiffFunc) – Differentiable function \(\mathcal{F}\).

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.__init__().

    • @@ -498,12 +498,12 @@

    Parameters (fit

  • “FR”: Fletcher-Reeves variant.

    • -

  • restart_rate (Integer, None) +

  • restart_rate (Integer, None) – Number of iterations after which restart is applied.

    By default, restart is done after \(N\) iterations.

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Optional parameters forwarded to backtracking_linesearch().

    If a0 is unspecified and \(\nabla f\) is \(\beta\)-Lipschitz continuous, then a0 is auto-chosen as @@ -625,7 +625,7 @@

    Parameters (__i

  • f (DiffFunc) – Differentiable function \(\mathcal{F}\).

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.__init__().

    • @@ -639,7 +639,7 @@

    Parameters (fit

  • variant (“adam”, “amsgrad”, “padam”) – Name of the Adam variant to use. Defaults to “adam”.

    • -

  • a (Real, None) +

  • a (Real, None) – Max normalized gradient step size. Defaults to \(1 / \beta\) if unspecified.

  • b1 (Real) @@ -648,11 +648,11 @@

    Parameters (fit

  • b2 (Real) – 2nd-order gradient exponential decay \(\beta_{2} \in [0, 1)\).

    • -

  • m0 (NDArray, None) +

  • m0 (NDArray, None) – (…, N) initial 1st-order gradient estimate corresponding to each initial point. Defaults to the null vector if unspecified.

    • -

  • v0 (NDArray, None) +

  • v0 (NDArray, None) – (…, N) initial 2nd-order gradient estimate corresponding to each initial point. Defaults to the null vector if unspecified.

    • @@ -665,7 +665,7 @@

    Parameters (fit

  • eps_var (Real) – Avoids division by zero if estimated gradient variance is too small. Defaults to 1e-6.

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.fit().

    • @@ -767,19 +767,19 @@

    Remarks#

    Parameters (__init__())#

    @@ -790,20 +790,20 @@

    Parameters (fit

  • x0 (NDArray) – (…, N) initial point(s) for the primal variable.

    • -

  • z0 (NDArray, None) +

  • z0 (NDArray, None) – (…, M) initial point(s) for the dual variable. If None (default), then use K(x0) as the initial point for the dual variable.

    • -

  • tau (Real, None) +

  • tau (Real, None) – Primal step size.

    • -

  • sigma (Real, None) +

  • sigma (Real, None) – Dual step size.

    • -

  • rho (Real, None) +

  • rho (Real, None) – Momentum parameter.

    • -

  • beta (Real, None) +

  • beta (Real, None) – Lipschitz constant \(\beta\) of \(\nabla\mathcal{F}\). If not provided, it will be automatically estimated.

    • @@ -811,7 +811,7 @@

    Parameters (fit – Strategy to be employed when setting the hyperparameters (default to 1). See section below for more details.

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.fit().

    • @@ -979,19 +979,19 @@

    Remarks#

    Parameters (__init__())#

    @@ -1002,20 +1002,20 @@

    Parameters (fit

  • x0 (NDArray) – (…, N) initial point(s) for the primal variable.

    • -

  • z0 (NDArray, None) +

  • z0 (NDArray, None) – (…, M) initial point(s) for the dual variable. If None (default), then use K(x0) as the initial point for the dual variable.

    • -

  • tau (Real, None) +

  • tau (Real, None) – Primal step size.

    • -

  • sigma (Real, None) +

  • sigma (Real, None) – Dual step size.

    • -

  • rho (Real, None) +

  • rho (Real, None) – Momentum parameter.

    • -

  • beta (Real, None) +

  • beta (Real, None) – Lipschitz constant \(\beta\) of \(\nabla\mathcal{F}\). If not provided, it will be automatically estimated.

    • @@ -1023,7 +1023,7 @@

    Parameters (fit – Strategy to be employed when setting the hyperparameters (default to 1). See section below for more details.

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.fit().

    • @@ -1129,12 +1129,12 @@

    Parameters (fit
    Parameters:
    @@ -1171,24 +1171,24 @@

    Parameters (fit

  • x0 (NDArray) – (…, N) initial point(s) for the primal variable.

    • -

  • z0 (NDArray, None) +

  • z0 (NDArray, None) – (…, M) initial point(s) for the dual variable. If None (default), then use K(x0) as the initial point for the dual variable.

    • -

  • tau (Real, None) +

  • tau (Real, None) – Primal step size.

    • -

  • sigma (Real, None) +

  • sigma (Real, None) – Dual step size.

    • -

  • rho (Real, None) +

  • rho (Real, None) – Momentum parameter.

  • tuning_strategy (1, 2, 3) – Strategy to be employed when setting the hyperparameters (default to 1). See base for more details.

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.fit().

    • @@ -1208,7 +1208,7 @@

    Parameters (fit

  • g (ProxFunc | None)

  • h (ProxFunc | None)

  • K (DiffMap | None)

    • -

  • base (Type[_PrimalDualSplitting])

    • +

  • base (Type[_PrimalDualSplitting])

    • @@ -1249,20 +1249,20 @@

    Remarks#

    Parameters (__init__())#

    @@ -1273,24 +1273,24 @@

    Parameters (fit

  • x0 (NDArray) – (…, N) initial point(s) for the primal variable.

    • -

  • z0 (NDArray, None) +

  • z0 (NDArray, None) – (…, M) initial point(s) for the dual variable. If None (default), then use K(x0) as the initial point for the dual variable.

    • -

  • tau (Real, None) +

  • tau (Real, None) – Primal step size.

    • -

  • sigma (Real, None) +

  • sigma (Real, None) – Dual step size.

    • -

  • rho (Real, None) +

  • rho (Real, None) – Momentum parameter.

  • tuning_strategy (1, 2, 3) – Strategy to be employed when setting the hyperparameters (default to 1). See PD3O for more details.

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.fit().

    • @@ -1346,17 +1346,17 @@

    Parameters (__i

  • f (DiffFunc) – Differentiable function \(\mathcal{F}\).

    • -

  • g (ProxFunc, None) +

  • g (ProxFunc, None) – Proximable function \(\mathcal{G}\).

    • -

  • h (ProxFunc, None) +

  • h (ProxFunc, None) – Proximable function \(\mathcal{H}\).

    • -

  • beta (Real, None) +

  • beta (Real, None) – Lipschitz constant \(\beta\) of \(\nabla\mathcal{F}\). If not provided, it will be automatically estimated.

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.__init__().

    • @@ -1367,24 +1367,24 @@

    Parameters (fit

  • x0 (NDArray) – (…, N) initial point(s) for the primal variable.

    • -

  • z0 (NDArray, None) +

  • z0 (NDArray, None) – (…, N) initial point(s) for the dual variable. If None (default), then use x0 as the initial point for the dual variable.

    • -

  • tau (Real, None) +

  • tau (Real, None) – Primal step size.

    • -

  • sigma (Real, None) +

  • sigma (Real, None) – Dual step size.

    • -

  • rho (Real, None) +

  • rho (Real, None) – Momentum parameter.

  • tuning_strategy (1, 2, 3) – Strategy to be employed when setting the hyperparameters (default to 1). See PD3O for more details.

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.fit().

    • @@ -1415,11 +1415,11 @@

    Parameters (fit
    Parameters:
    @@ -1448,14 +1448,14 @@

    Parameters (fit

  • x0 (NDArray) – (…, N) initial point(s) for the primal variable.

    • -

  • z0 (NDArray, None) +

  • z0 (NDArray, None) – (…, N) initial point(s) for the dual variable. If None (default), then use x0 as the initial point for the dual variable.

    • -

  • tau (Real, None) +

  • tau (Real, None) – Primal step size. Defaults to 1.

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.fit().

    • @@ -1474,7 +1474,7 @@

    Parameters (fit

    @@ -1510,17 +1510,17 @@

    Remarks#

    Parameters (__init__())#

    @@ -1531,21 +1531,21 @@

    Parameters (fit

  • x0 (NDArray) – (…, N) initial point(s) for the primal variable.

    • -

  • z0 (NDArray, None) +

  • z0 (NDArray, None) – (…, N) initial point(s) for the dual variable. If None (default), then use x0 as the initial point for the dual variable.

    • -

  • tau (Real, None) +

  • tau (Real, None) – Primal step size.

    • -

  • rho (Real, None) +

  • rho (Real, None) – Momentum parameter.

  • tuning_strategy (1, 2, 3) – Strategy to be employed when setting the hyperparameters (default to 1). See CondatVu for more details.

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.fit().

    • @@ -1578,7 +1578,7 @@

    Parameters (fit

  • g (ProxFunc) – Proximable function \(\mathcal{G}\).

  • base (CondatVu, PD3O) – Specifies the base primal-dual algorithm from which mathematical updates are inherited. (Default = CondatVu)

    • -

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.__init__().

    • +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.__init__().

    • @@ -1602,13 +1602,13 @@

    Parameters (fit

  • x0 (NDArray) – (…, N) initial point(s) for the primal variable.

    • -

  • tau (Real, None) +

  • tau (Real, None) – Primal step size.

    • -

  • rho (Real, None) +

  • rho (Real, None) – Momentum parameter.

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.fit().

    • @@ -1699,27 +1699,27 @@

    Remarks#

    Parameters (__init__())#

    @@ -1730,27 +1730,27 @@

    Parameters (fit

  • x0 (NDArray) – (…, N) initial point(s) for the primal variable.

    • -

  • z0 (NDArray, None) +

  • z0 (NDArray, None) – (…, M) initial point(s) for the dual variable. If None (default), then use K(x0) as the initial point for the dual variable.

    • -

  • tau (Real, None) +

  • tau (Real, None) – Primal step size.

    • -

  • rho (Real, None) +

  • rho (Real, None) – Momentum parameter for relaxation.

  • tuning_strategy (1, 2, 3) – Strategy to be employed when setting the hyperparameters (default to 1). See base class for more details.

    • -

  • solver_kwargs (Mapping) +

  • solver_kwargs (Mapping) – Keyword parameters passed to the fit() method of sub-iterative CG or NLCG solvers.

    solver_kwargs is ignored if solver was provided in __init__().

    • -

  • **kwargs (Mapping) +

  • **kwargs (Mapping) – Other keyword parameters passed on to pyxu.abc.Solver.fit().

    • @@ -1842,8 +1842,8 @@

    Parameters (fit

  • f (Func | None)

  • h (ProxFunc | None)

  • K (DiffMap | None)

    • -

  • solver (Callable[[NDArray, float], NDArray])

    • -

  • solver_kwargs (dict | None)

    • +

  • solver (Callable[[NDArray, float], NDArray])

    • +

  • solver_kwargs (dict | None)

    • diff --git a/api/opt.stop.html b/api/opt.stop.html index 53cc4133..0b011522 100644 --- a/api/opt.stop.html +++ b/api/opt.stop.html @@ -518,7 +518,7 @@

    pyxu.opt.stop
    Parameters:
    -

    n (Integral)

    +

    n (Integral)

    @@ -558,7 +558,7 @@

    pyxu.opt.stop
    Parameters:
    -

    t (timedelta)

    +

    t (timedelta)

    @@ -566,7 +566,7 @@

    pyxu.opt.stop__init__(t)[source]#
    Parameters:
    -

    t (timedelta) – Max runtime allowed.

    +

    t (timedelta) – Max runtime allowed.

    @@ -581,7 +581,7 @@

    pyxu.opt.stopSolver.)

    Parameters:
    -

    var (str | Collection[str])

    +

    var (str | Collection[str])

    @@ -604,12 +604,12 @@

    pyxu.opt.stop
    Parameters:

    @@ -623,14 +623,14 @@

    pyxu.opt.stopVarName) – Variable in pyxu.abc.Solver._mstate to query. Must hold an NDArray.

  • rank (Integer) – Array rank K of monitored variable after applying f. (See below.)

    • -

  • f (Callable) –

    Optional function to pre-apply to _mstate[var] before applying the norm. Defaults to the identity +

  • f (Callable) –

    Optional function to pre-apply to _mstate[var] before applying the norm. Defaults to the identity function. The callable should have the same semantics as apply():

    (…, M1,…,MD) -> (…, N1,…,NK)

  • norm (Integer, Real) – Ln norm to use >= 0. (Default: L2.)

    • -

  • satisfy_all (bool) – If True (default) and _mstate[var] is multi-dimensional, stop if all evaluation points lie below +

  • satisfy_all (bool) – If True (default) and _mstate[var] is multi-dimensional, stop if all evaluation points lie below threshold.

    • @@ -647,12 +647,12 @@

    pyxu.opt.stop
    Parameters:
    @@ -666,14 +666,14 @@

    pyxu.opt.stopVarName) – Variable in pyxu.abc.Solver._mstate to query. Must hold an NDArray

  • rank (Integer) – Array rank K of monitored variable after applying f. (See below.)

    • -

  • f (Callable) –

    Optional function to pre-apply to _mstate[var] before applying the norm. Defaults to the identity +

  • f (Callable) –

    Optional function to pre-apply to _mstate[var] before applying the norm. Defaults to the identity function. The callable should have the same semantics as apply():

    (…, M1,…,MD) -> (…, N1,…,NK)

  • norm (Integer, Real) – Ln norm to use >= 0. (Default: L2.)

    • -

  • satisfy_all (bool) – If True (default) and _mstate[var] is multi-dimensional, stop if all evaluation points lie below +

  • satisfy_all (bool) – If True (default) and _mstate[var] is multi-dimensional, stop if all evaluation points lie below threshold.

    • diff --git a/api/runtime.html b/api/runtime.html index dc4c62e9..8ad07397 100644 --- a/api/runtime.html +++ b/api/runtime.html @@ -504,7 +504,7 @@
    class Width(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
    -

    Bases: Enum

    +

    Bases: Enum

    Machine-dependent floating-point types.

    @@ -523,7 +523,7 @@

    Returns the difference between 1 and the next smallest representable float larger than 1.

    Return type:
    -

    Real

    +

    Real

    @@ -539,7 +539,7 @@
    class CWidth(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
    -

    Bases: Enum

    +

    Bases: Enum

    Machine-dependent complex-valued floating-point types.

    diff --git a/api/util.html b/api/util.html index 2ef7529d..b050f959 100644 --- a/api/util.html +++ b/api/util.html @@ -309,7 +309,7 @@

    pyxu.util.array_modul
    Parameters:
      -

    • x (object) – Any object compatible with the interface of NumPy arrays.

      • +

    • x (object) – Any object compatible with the interface of NumPy arrays.

    • fallback (ArrayModule) – Fallback module if x is not a NumPy-like array. Default behaviour: raise error if fallback used.

    @@ -336,15 +336,15 @@

    pyxu.util.array_modul
    Parameters:
    Return type:
    -

    Callable

    +

    Callable

    Notes

    @@ -376,18 +376,18 @@

    pyxu.util.array_modul
    Parameters:
      -

    • *args (object, list) – Any number of objects. If it is a dask object, it is evaluated and the result is returned. Non-dask arguments +

    • *args (object, list) – Any number of objects. If it is a dask object, it is evaluated and the result is returned. Non-dask arguments are passed through unchanged. Python collections are traversed to find/evaluate dask objects within. (Use traverse =False to disable this behavior.)

      • -

    • mode (str) – Dask evaluation strategy: compute or persist.

      • -

    • **kwargs (dict) – Extra keyword parameters forwarded to dask.compute() or dask.persist().

      • +

    • mode (str) – Dask evaluation strategy: compute or persist.

      • +

    • **kwargs (dict) – Extra keyword parameters forwarded to dask.compute() or dask.persist().

    Returns:

    *cargs – Evaluated objects. Non-dask arguments are passed through unchanged.

    Return type:
    -

    object, list

    +

    object, list

    @@ -571,12 +571,12 @@

    pyxu.util.misc
    Parameters:
    Return type:
    -

    ModuleType

    +

    ModuleType

    @@ -589,7 +589,7 @@

    pyxu.util.misc

    params – (key, value) params as seen in body of func when called via func(*args, **kwargs).

    Return type:
    -

    Mapping

    +

    Mapping

    @@ -698,11 +698,11 @@

    pyxu.util.operatorParameters:
    Return type:
    -

    Integral | tuple[Integral, …]

    +

    Integral | tuple[Integral, …]

    @@ -712,10 +712,10 @@

    pyxu.util.operator

    Transform a lone integer into a valid tuple-based shape specifier.

    Parameters:
    -

    x (Integral | tuple[Integral, ...])

    +

    x (Integral | tuple[Integral, ...])

    Return type:
    -

    Integral | tuple[Integral, …]

    +

    Integral | tuple[Integral, …]

    @@ -736,7 +736,7 @@

    pyxu.util.operator

    g – Function/Method with signature (..., M1,...,MD) -> (..., N1,...,NK) in parameter i.

    Return type:
    -

    Callable

    +

    Callable

    Example

    diff --git a/guide/diff_ops.html b/guide/diff_ops.html index 55ffba87..6285538a 100644 --- a/guide/diff_ops.html +++ b/guide/diff_ops.html @@ -292,7 +292,7 @@

    Partial Derivatives \[\mathbf{D} f [n] = f[n+1] - f[n]\]

    As we will see below, this is the forward finite difference approximation. This could be implemented in matrix-form, in which case it would look like this:

    -

    7a07057c1cd74670947d205c2dd0d440

    +

    c61ea19448fc40b3946a8ab1f65ee4d1

    Or, it could be instead implemented via a for loop, in which the case of large input signals, would not require storing a large matrix:

    [1]:
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