Corpus ID: 11856692

On the saddle point problem for non-convex optimization

@article{Pascanu2014OnTS,
  title={On the saddle point problem for non-convex optimization},
  author={Razvan Pascanu and Yann Dauphin and S. Ganguli and Yoshua Bengio},
  journal={ArXiv},
  year={2014},
  volume={abs/1405.4604}
}

Razvan Pascanu, Yann Dauphin, +1 author Yoshua Bengio

Published 2014

Computer Science

ArXiv

A central challenge to many fields of science and engineering involves minimizing non-convex error functions over continuous, high dimensional spaces. Gradient descent or quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it is often thought that a main source of difficulty for the ability of these local methods to find the global minimum is the proliferation of local minima with much higher error than the global minimum. Here we argue, based on results from… CONTINUE READING

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