A quasi-Newton approach to nonsmooth convex optimization

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 1216-1223

  Yu, Jin ^a   Vishwanathau, S V N ^a   Günter, Simon ^a   Schraudolph, Nicol N ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAMMING; CONVEX OPTIMIZATION; NEWTON-RAPHSON METHOD; LEARNING SYSTEMS; OPTIMIZATION; ROBOT LEARNING;

DESCENT DIRECTIONS; DIRECTION FINDING; GENERIC ALGORITHM; NON-SMOOTH CONVEX OPTIMIZATIONS; QUADRATIC MODELING; QUASI-NEWTON METHODS; RISK MINIMIZATION; WOLFE LINE SEARCHES;

MACHINE LEARNING; CONVEX OPTIMIZATION;

CONVEX OBJECTIVES; DIRECTION FINDINGS; GENERIC ALGORITHMS; LINE SEARCHES; NON SMOOTHES; QUADRATIC MODELS; QUASI-NEWTON METHODS; RISK MINIMIZATIONS; SUBDIFFERENTIALS; THREE COMPONENTS;

EID: 56449097975     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390309     Document Type: Conference Paper

References (11)

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11. J. Yu, S. V. N. Vishwanathan, S. Günter, and N. N. Schraudolph. A quasi-Newton approach to nonsmooth convex optimization. Technical Report arXiv:0804.3835, April 2008. http://arxiv.org/pdf/0804.3835.