diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle index 0f98d7e..57861dd 100644 Binary files a/docs/build/doctrees/environment.pickle and b/docs/build/doctrees/environment.pickle differ diff --git a/docs/build/doctrees/readme.doctree b/docs/build/doctrees/readme.doctree index 309d07e..71e48fe 100644 Binary files a/docs/build/doctrees/readme.doctree and b/docs/build/doctrees/readme.doctree differ diff --git a/docs/build/html/apidoc.html b/docs/build/html/apidoc.html index 86ce36f..b317174 100644 --- a/docs/build/html/apidoc.html +++ b/docs/build/html/apidoc.html @@ -309,7 +309,7 @@

5. API documentation
-torchimize.functions.lsq_gna(p: torch.Tensor, function: Callable, jac_function: Optional[Callable] = None, args: Union[Tuple, List] = (), ftol: float = 1e-08, ptol: float = 1e-08, gtol: float = 1e-08, l: float = 1.0, max_iter: int = 100) → List[torch.Tensor]
+torchimize.functions.lsq_gna(p: torch.Tensor, function: Callable, jac_function: Optional[Callable] = None, args: Union[Tuple, List] = (), ftol: float = 1e-08, ptol: float = 1e-08, gtol: float = 1e-08, l: float = 1.0, max_iter: int = 100) → List[torch.Tensor]

Gauss-Newton implementation for least-squares fitting of non-linear functions

Parameters:
@@ -333,7 +333,7 @@

5. API documentation
-torchimize.functions.lsq_lma(p: torch.Tensor, function: Callable, jac_function: Optional[Callable] = None, args: Union[Tuple, List] = (), ftol: float = 1e-08, ptol: float = 1e-08, gtol: float = 1e-08, tau: float = 0.001, meth: str = 'lev', rho1: float = 0.25, rho2: float = 0.75, bet: float = 2, gam: float = 3, max_iter: int = 100) → List[torch.Tensor]
+torchimize.functions.lsq_lma(p: torch.Tensor, function: Callable, jac_function: Optional[Callable] = None, args: Union[Tuple, List] = (), ftol: float = 1e-08, ptol: float = 1e-08, gtol: float = 1e-08, tau: float = 0.001, meth: str = 'lev', rho1: float = 0.25, rho2: float = 0.75, bet: float = 2, gam: float = 3, max_iter: int = 100) → List[torch.Tensor]

Levenberg-Marquardt implementation for least-squares fitting of non-linear functions

Parameters:
@@ -362,7 +362,7 @@

5. API documentation
-torchimize.functions.lsq_gna_parallel(p: torch.Tensor, function: Callable, jac_function: Optional[Callable] = None, args: Union[Tuple, List] = (), wvec: Optional[torch.Tensor] = None, ftol: float = 1e-08, ptol: float = 1e-08, gtol: float = 1e-08, l: float = 1.0, max_iter: int = 100) → List[torch.Tensor]
+torchimize.functions.lsq_gna_parallel(p: torch.Tensor, function: Callable, jac_function: Optional[Callable] = None, args: Union[Tuple, List] = (), wvec: Optional[torch.Tensor] = None, ftol: float = 1e-08, ptol: float = 1e-08, gtol: float = 1e-08, l: float = 1.0, max_iter: int = 100) → List[torch.Tensor]

Gauss-Newton implementation for parallel least-squares fitting of non-linear functions with conditions.

Parameters:
@@ -387,7 +387,7 @@

5. API documentation
-torchimize.functions.lsq_gna_parallel_plain(p: torch.Tensor, function: Callable, jac_function: Callable, wvec: torch.Tensor, l: float = 1.0, max_iter: int = 100) → torch.Tensor
+torchimize.functions.lsq_gna_parallel_plain(p: torch.Tensor, function: Callable, jac_function: Callable, wvec: torch.Tensor, l: float = 1.0, max_iter: int = 100) → torch.Tensor

Gauss-Newton implementation for parallel least-squares fitting of non-linear functions without conditions.

Parameters:
@@ -408,7 +408,7 @@

5. API documentation
-torchimize.functions.lsq_lma_parallel(p: torch.Tensor, function: Callable, jac_function: Optional[Callable] = None, args: Union[Tuple, List] = (), wvec: Optional[torch.Tensor] = None, ftol: float = 1e-08, ptol: float = 1e-08, gtol: float = 1e-08, tau: float = 0.001, meth: str = 'lev', rho1: float = 0.25, rho2: float = 0.75, beta: float = 2, gama: float = 3, max_iter: int = 100) → List[torch.Tensor]
+torchimize.functions.lsq_lma_parallel(p: torch.Tensor, function: Callable, jac_function: Optional[Callable] = None, args: Union[Tuple, List] = (), wvec: Optional[torch.Tensor] = None, ftol: float = 1e-08, ptol: float = 1e-08, gtol: float = 1e-08, tau: float = 0.001, meth: str = 'lev', rho1: float = 0.25, rho2: float = 0.75, beta: float = 2, gama: float = 3, max_iter: int = 100) → List[torch.Tensor]

Levenberg-Marquardt implementation for parallel least-squares fitting of non-linear functions

Parameters:
@@ -438,7 +438,7 @@

5. API documentation
-torchimize.functions.newton_step_parallel(p: torch.Tensor, function: Callable, jac_function: Callable, wvec: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
+torchimize.functions.newton_step_parallel(p: torch.Tensor, function: Callable, jac_function: Callable, wvec: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]

Newton second order step function for parallel least-squares fitting of non-linear functions

Parameters:
@@ -457,7 +457,7 @@

5. API documentation
-torchimize.functions.test_fun_dims_parallel(p: torch.Tensor, function: Callable, jac_function: Optional[Callable] = None, args: Union[Tuple, List] = (), wvec: Optional[torch.Tensor] = None) → bool
+torchimize.functions.test_fun_dims_parallel(p: torch.Tensor, function: Callable, jac_function: Optional[Callable] = None, args: Union[Tuple, List] = (), wvec: Optional[torch.Tensor] = None) → bool

Helper function that tests whether dimensionality of output tensors suits the herein provided optimization functions.

Parameters:
diff --git a/docs/build/html/readme.html b/docs/build/html/readme.html index 5134262..c483f86 100644 --- a/docs/build/html/readme.html +++ b/docs/build/html/readme.html @@ -308,7 +308,7 @@

1. Description

torchimize contains implementations of the Gradient Descent, Gauss-Newton and Levenberg-Marquardt optimization algorithms using the PyTorch library. The main motivation for this project is to enable convex optimization on GPUs based on the torch.Tensor class, which (as of 2022) is widely used in the deep learning field. This package features the capability to minimize several least-squares optimization problems at each loop iteration in parallel.

-

coverage tests_develop tests_master PyPI Downloads License

+

coverage tests_develop tests_master PyPI Downloads License

2. Installation