56 search results - page 8 / 12 » Minimizing Convex Functions with Bounded Perturbations |
126
Voted
SAGA
2009
Springer
15 years 4 months ago
2009
Springer
We propose a generic method for obtaining quickly good upper bounds on the minimal value of a multistage stochastic program. The method is based on the simulation of a feasible dec...
78
Voted
ML
2007
ACM
14 years 9 months ago
2007
ACM
Abstract Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. A...
ICCV
2007
IEEE
15 years 11 months ago
2007
IEEE
We present a practical, stratified autocalibration algorithm with theoretical guarantees of global optimality. Given a projective reconstruction, the first stage of the algorithm ...
JMLR
2010
14 years 4 months ago
2010
We consider regularized stochastic learning and online optimization problems, where the objective function is the sum of two convex terms: one is the loss function of the learning...
75
Voted
SIAMJO
2010
14 years 4 months ago
2010
When liquidating a portfolio of large blocks of risky assets, an institutional investor wants to minimize the cost as well as the risk of execution. An optimal execution strategy ...