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SIAMJO
2008
114views more  SIAMJO 2008»
13 years 9 months ago
An Inexact SQP Method for Equality Constrained Optimization
We present an algorithm for large-scale equality constrained optimization. The method is based on a characterization of inexact sequential quadratic programming (SQP) steps that ca...
Richard H. Byrd, Frank E. Curtis, Jorge Nocedal
SIAMJO
2010
87views more  SIAMJO 2010»
13 years 7 months ago
A Second Derivative SQP Method: Global Convergence
Abstract. Sequential quadratic programming (SQP) methods form a class of highly efficient algorithms for solving nonlinearly constrained optimization problems. Although second deri...
Nicholas I. M. Gould, Daniel P. Robinson
ML
2002
ACM
220views Machine Learning» more  ML 2002»
13 years 8 months ago
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
Peter Sollich
VALUETOOLS
2006
ACM
125views Hardware» more  VALUETOOLS 2006»
14 years 2 months ago
An approximative method for calculating performance measures of Markov processes
We present a new approximation method called value extrapolation for Markov processes with large or infinite state spaces. The method can be applied for calculating any performan...
Juha Leino, Jorma T. Virtamo
MP
2010
135views more  MP 2010»
13 years 7 months ago
An inexact Newton method for nonconvex equality constrained optimization
Abstract We present a matrix-free line search algorithm for large-scale equality constrained optimization that allows for inexact step computations. For sufficiently convex problem...
Richard H. Byrd, Frank E. Curtis, Jorge Nocedal