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» On the steepest descent algorithm for quadratic functions
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ESANN
2007
13 years 6 months ago
Model Selection for Kernel Probit Regression
Abstract. The convex optimisation problem involved in fitting a kernel probit regression (KPR) model can be solved efficiently via an iteratively re-weighted least-squares (IRWLS)...
Gavin C. Cawley
SIAMCO
2002
121views more  SIAMCO 2002»
13 years 4 months ago
Consistent Approximations and Approximate Functions and Gradients in Optimal Control
As shown in [7], optimal control problems with either ODE or PDE dynamics can be solved efficiently using a setting of consistent approximations obtained by numerical discretizati...
Olivier Pironneau, Elijah Polak
EOR
2008
93views more  EOR 2008»
13 years 5 months ago
Approximate methods for convex minimization problems with series-parallel structure
Consider a problem of minimizing a separable, strictly convex, monotone and differentiable function on a convex polyhedron generated by a system of m linear inequalities. The probl...
Adi Ben-Israel, Genrikh Levin, Yuri Levin, Boris R...
DCC
2005
IEEE
14 years 4 months ago
When is Bit Allocation for Predictive Video Coding Easy?
This paper addresses the problem of bit allocation among frames in a predictively encoded video sequence. Finding optimal solutions to this problem potentially requires making an ...
Yegnaswamy Sermadevi, Jun Chen, Sheila S. Hemami, ...
CDC
2009
IEEE
132views Control Systems» more  CDC 2009»
13 years 10 months ago
Q-learning and Pontryagin's Minimum Principle
Abstract— Q-learning is a technique used to compute an optimal policy for a controlled Markov chain based on observations of the system controlled using a non-optimal policy. It ...
Prashant G. Mehta, Sean P. Meyn