56 search results - page 7 / 12 » Minimizing Convex Functions with Bounded Perturbations |
107
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CDC
2008
IEEE
14 years 9 months ago
2008
IEEE
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This c...
72
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CORR
2010
Springer
14 years 4 months ago
2010
Springer
In various applications, the effect of errors in gradient-based iterations is of particular importance when seeking saddle points of the Lagrangian function associated with constra...
84
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APPROX
2005
Springer
15 years 3 months ago
2005
Springer
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...
SDM
2008
SIAM
14 years 11 months ago
2008
SIAM
This paper presents a stagewise least square (SLS) loss function for classification. It uses a least square form within each stage to approximate a bounded monotonic nonconvex los...
150
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SIAMIS
2011
14 years 4 months ago
2011
The convergence rate is analyzed for the sparse reconstruction by separable approximation (SpaRSA) algorithm for minimizing a sum f(x) + ψ(x), where f is smooth and ψ is convex, ...