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[BOG][B] Variational analysis and applications
BS Mordukhovich - 2018 - Springer
Boris S. Mordukhovich Page 1 Springer Monographs in Mathematics Boris S. Mordukhovich
Variational Analysis and Applications Page 2 Springer Monographs in Mathematics Editors-in-Chief …
Variational Analysis and Applications Page 2 Springer Monographs in Mathematics Editors-in-Chief …
C-mil: Continuation multiple instance learning for weakly supervised object detection
Weakly supervised object detection (WSOD) is a challenging task when provided with image
category supervision but required to simultaneously learn object locations and object …
category supervision but required to simultaneously learn object locations and object …
A globally convergent algorithm for nonconvex optimization based on block coordinate update
Nonconvex optimization arises in many areas of computational science and engineering.
However, most nonconvex optimization algorithms are only known to have local …
However, most nonconvex optimization algorithms are only known to have local …
Support points
This paper introduces a new way to compact a continuous probability distribution F into a set
of representative points called support points. These points are obtained by minimizing the …
of representative points called support points. These points are obtained by minimizing the …
Deterministic nonsmooth nonconvex optimization
We study the complexity of optimizing nonsmooth nonconvex Lipschitz functions by
producing $(\delta,\epsilon) $-Goldstein stationary points. Several recent works have …
producing $(\delta,\epsilon) $-Goldstein stationary points. Several recent works have …
A smoothing proximal gradient algorithm for nonsmooth convex regression with cardinality penalty
W Bian, X Chen - SIAM Journal on Numerical Analysis, 2020 - SIAM
In this paper, we focus on the constrained sparse regression problem, where the loss
function is convex but nonsmooth and the penalty term is defined by the cardinality function …
function is convex but nonsmooth and the penalty term is defined by the cardinality function …
[BOG][B] An optimization primer
The concerns with finding minima and maxima were for a long time mainly restricted to
mathematical expressions stemming from physical phenomena. The preeminence of being …
mathematical expressions stemming from physical phenomena. The preeminence of being …
MADMM: a generic algorithm for non-smooth optimization on manifolds
Numerous problems in computer vision, pattern recognition, and machine learning are
formulated as optimization with manifold constraints. In this paper, we propose the Manifold …
formulated as optimization with manifold constraints. In this paper, we propose the Manifold …
Group sparse optimization via lp, q regularization
In this paper, we investigate a group sparse optimization problem via lp, q regularization in
three aspects: theory, algorithm and application. In the theoretical aspect, by introducing a …
three aspects: theory, algorithm and application. In the theoretical aspect, by introducing a …
Risk-adaptive approaches to stochastic optimization: A survey
JO Royset - SIAM Review, 2025 - SIAM
Uncertainty is prevalent in engineering design and data-driven problems and, more broadly,
in decision making. Due to inherent risk-averseness and ambiguity about assumptions, it is …
in decision making. Due to inherent risk-averseness and ambiguity about assumptions, it is …