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Regularity Conditions in Differentiable Vector Optimization Revisited
(Springer/plenum PublishersNew YorkEUA, 2009)
A practical optimality condition without constraint qualifications for nonlinear programming
(Springer/plenum PublishersNew YorkEUA, 2003)
Worst-case Evaluation Complexity For Unconstrained Nonlinear Optimization Using High-order Regularized Models
(Springer HeidelbergHeidelberg, 2017)
An optimization problem with volume constraint for an inhomogeneous operator with nonstandard growth
(American Institute of Mathematical Sciences, 2021-06)
We consider an optimization problem with volume constraint for an energy functional associated to an inhomogeneous operator with nonstandard growth. By studying an auxiliary penalized problem, we prove existence and ...
Fine-Tuning Dropout Regularization in Energy-Based Deep Learning
(2021-01-01)
Deep Learning architectures have been extensively studied in the last years, mainly due to their discriminative power in Computer Vision. However, one problem related to such models concerns their number of parameters and ...
A new matrix-free algorithm for the large-scale trust-region subproblem
(Siam PublicationsPhiladelphiaEUA, 2001)
Regular Optimal Control Problems with Quadratic Final Penalties
(Unión Matemática Argentina, 2008-12)
Hamilton’s canonical equations (HCEs) have played a central role in Mechanicsafter (i) their equivalence with the principle of least action, and (ii) the variationalcalculus leading to the Euler-Lagrange equation, were ...
Algebraic Rules For Quadratic Regularization Of Newton's Method
(SPRINGERNEW YORK, 2015)
Stope optimization with vertical convexity constraints
(Springer, 2016)
A new algorithm for the optimal stope design problem is proposed. It is based on a previous methodology developed by Bai et al. (Comput Geosci 52:361-371, 2013a) where a cylindrical coordinate system is used to define ...
Proximal regularization for the saddle point gradient dynamics
(2021)
This paper concerns the solution of a convex optimization
problem through the saddle point gradient dynamics.
Instead of using the standard Lagrangian as is classical in this
method, we consider a regularized Lagrangian ...