Results 1 to 10 of about 481 (47)
Bregman iterative regularization using model functions for nonconvex nonsmooth optimization
Frontiers in Applied Mathematics and Statistics, 2022 In this paper, we propose a new algorithm called ModelBI by blending the Bregman iterative regularization method and the model function technique for solving a class of nonconvex nonsmooth optimization problems.
Haoxing Yang +3 more
doaj +1 more source
Axioms, 2022 The nonconvex and nonsmooth optimization problem has been attracting increasing attention in recent years in image processing and machine learning research. The algorithm-based reweighted step has been widely used in many applications.
Juyeb Yeo, Myeongmin Kang
doaj +1 more source
An ADMM-based SQP method for separably smooth nonconvex optimization
Journal of Inequalities and Applications, 2020 This work is about a splitting approach for solving separably smooth nonconvex linearly constrained optimization problems. Based on the ideas from two classical methods, namely the sequential quadratic programming (SQP) and the alternating direction ...
Meixing Liu, Jinbao Jian
doaj +1 more source
A regularized alternating direction method of multipliers for a class of nonconvex problems
Journal of Inequalities and Applications, 2019 In this paper, we propose a regularized alternating direction method of multipliers (RADMM) for a class of nonconvex optimization problems. The algorithm does not require the regular term to be strictly convex. Firstly, we prove the global convergence of
Jin Bao Jian, Ye Zhang, Mian Tao Chao
doaj +1 more source
Convergence of Linear Bregman ADMM for Nonconvex and Nonsmooth Problems with Nonseparable Structure
Complexity, 2020 The alternating direction method of multipliers (ADMM) is an effective method for solving two-block separable convex problems and its convergence is well understood.
Miantao Chao, Zhao Deng, Jinbao Jian
doaj +1 more source
IEEE Access, 2020 In this paper, we focus on the three-block nonconvex optimization problem of background/foreground extraction from a blurred and noisy surveillance video. The coefficient matrices of the equality constraints are nonidentity matrices.
Chun Zhang +3 more
doaj +1 more source
Inertial proximal alternating minimization for nonconvex and nonsmooth problems
Journal of Inequalities and Applications, 2017 In this paper, we study the minimization problem of the type L ( x , y ) = f ( x ) + R ( x , y ) + g ( y ) $L(x,y)=f(x)+R(x,y)+g(y)$ , where f and g are both nonconvex nonsmooth functions, and R is a smooth function we can choose.
Yaxuan Zhang, Songnian He
doaj +1 more source
, 2020 We introduce Bella, a locally superlinearly convergent Bregman forward backward splitting method for minimizing the sum of two nonconvex functions, one of which satisfying a relative smoothness condition and the other one possibly nonsmooth.
Ahookhosh, Masoud +2 more
core +1 more source
Continuous functions on the plane regular after one blowing-up [PDF]
, 2016 International audienceWe study rational functions admitting a continuous extension to the real affine space. First of all, we focus on the regularity of such functions exhibiting some nice properties of their partial derivatives.
Fichou, Goulwen +2 more
core +5 more sources
Alternating minimization and projection methods for structured nonconvex problems [PDF]
, 2010 International audienceWe study the convergence properties of an alternating proximal minimization algorithm for nonconvex structured functions of the type: L( x, y) = f( x )+Q( x , y) +g (y) , where f and g are proper lower semicontinuous functions ...
Attouch, Hedy +3 more
core +4 more sources