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Table of Contents

[ 1.6.0 ▼ ](http://pytorch.org/docs/versions.html)

Notes

  * [ Automatic Mixed Precision examples ](notes/amp_examples.html)
  * [ Autograd mechanics ](notes/autograd.html)
  * [ Broadcasting semantics ](notes/broadcasting.html)
  * [ CPU threading and TorchScript inference ](notes/cpu_threading_torchscript_inference.html)
  * [ CUDA semantics ](notes/cuda.html)
  * [ Distributed Data Parallel ](notes/ddp.html)
  * [ Extending PyTorch ](notes/extending.html)
  * [ Frequently Asked Questions ](notes/faq.html)
  * [ Features for large-scale deployments ](notes/large_scale_deployments.html)
  * [ Multiprocessing best practices ](notes/multiprocessing.html)
  * [ Reproducibility ](notes/randomness.html)
  * [ Serialization semantics ](notes/serialization.html)
  * [ Windows FAQ ](notes/windows.html)

Language Bindings

  * [ C++ ](cpp_index.html)
  * [ Javadoc ](https://pytorch.org/javadoc/)

Python API

  * [ torch ](torch.html)
  * [ torch.nn ](nn.html)
  * [ torch.nn.functional ](nn.functional.html)
  * [ torch.Tensor ](tensors.html)
  * [ Tensor Attributes ](tensor_attributes.html)
  * [ Tensor Views ](tensor_view.html)
  * [ torch.autograd ](autograd.html)
  * [ torch.cuda ](cuda.html)
  * [ torch.cuda.amp ](amp.html)
  * [ torch.distributed ](distributed.html)
  * [ torch.distributions ](distributions.html)
  * [ torch.futures ](futures.html)
  * [ torch.hub ](hub.html)
  * [ torch.jit ](jit.html)
  * [ torch.nn.init ](nn.init.html)
  * [ torch.onnx ](onnx.html)
  * torch.optim 
  * [ Complex Numbers ](complex_numbers.html)
  * [ Quantization ](quantization.html)
  * [ Distributed RPC Framework ](rpc.html)
  * [ torch.random ](random.html)
  * [ torch.sparse ](sparse.html)
  * [ torch.Storage ](storage.html)
  * [ torch.utils.bottleneck ](bottleneck.html)
  * [ torch.utils.checkpoint ](checkpoint.html)
  * [ torch.utils.cpp_extension ](cpp_extension.html)
  * [ torch.utils.data ](data.html)
  * [ torch.utils.dlpack ](dlpack.html)
  * [ torch.utils.mobile_optimizer ](mobile_optimizer.html)
  * [ torch.utils.model_zoo ](model_zoo.html)
  * [ torch.utils.tensorboard ](tensorboard.html)
  * [ Type Info ](type_info.html)
  * [ Named Tensors ](named_tensor.html)
  * [ Named Tensors operator coverage ](name_inference.html)
  * [ torch.__config__ ](__config__.html)

Libraries

  * [ torchaudio ](https://pytorch.org/audio)
  * [ torchtext ](https://pytorch.org/text)
  * [ torchvision ](https://pytorch.org/vision)
  * [ TorchElastic ](https://pytorch.org/elastic/)
  * [ TorchServe ](https://pytorch.org/serve)
  * [ PyTorch on XLA Devices ](http://pytorch.org/xla/)

Community

  * [ PyTorch Contribution Guide ](community/contribution_guide.html)
  * [ PyTorch Governance ](community/governance.html)
  * [ PyTorch Governance | Persons of Interest ](community/persons_of_interest.html)

  * [ Docs ](index.html) >
  * torch.optim 
  * [ ![](_static/images/view-page-source-icon.svg) ](_sources/optim.rst.txt)

Shortcuts

#  torch.optim  ¶

` torch.optim  ` is a package implementing various optimization algorithms.
Most commonly used methods are already supported, and the interface is general
enough, so that more sophisticated ones can be also easily integrated in the
future.

##  How to use an optimiz