Compressed Sensing Based on Trust Region Method
Sign Up to like & getrecommendations! Published in 2017 at "Circuits, Systems, and Signal Processing"
DOI: 10.1007/s00034-016-0299-2
Abstract: Developed in recent years, compressed sensing (CS) has saved considerable data storage and time in signal acquisition and processing, drawing the attention of many scholars in various fields. At present, a key issue is the… read more here.
Keywords: compressed sensing; trust region; region method; convex optimization ... See more keywords
An efficient neural network for solving convex optimization problems with a nonlinear complementarity problem function
Sign Up to like & getrecommendations! Published in 2020 at "Soft Computing"
DOI: 10.1007/s00500-019-04189-8
Abstract: In this paper, we present a one-layer recurrent neural network (NN) for solving convex optimization problems by using the Mangasarian and Solodov (MS) implicit Lagrangian function. In this paper by using Krush–Kuhn–Tucker conditions and MS… read more here.
Keywords: convex optimization; neural network; problem; optimization problems ... See more keywords
An Inertial Parallel and Asynchronous Forward–Backward Iteration for Distributed Convex Optimization
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Optimization Theory and Applications"
DOI: 10.1007/s10957-019-01542-7
Abstract: AbstractTwo characteristics that make convex decomposition algorithms attractive are simplicity of operations and generation of parallelizable structures. In principle, these schemes require that all coordinates update at the same time, i.e., they are synchronous by… read more here.
Keywords: iteration; convex optimization; asynchronous forward; optimization ... See more keywords
On quasi $$\epsilon $$ϵ-solution for robust convex optimization problems
Sign Up to like & getrecommendations! Published in 2017 at "Optimization Letters"
DOI: 10.1007/s11590-016-1067-8
Abstract: This paper devotes to the quasi $$\epsilon $$ϵ-solution (one sort of approximate solutions) for a robust convex optimization problem in the face of data uncertainty. Using robust optimization approach (worst-case approach), we establish approximate optimality… read more here.
Keywords: convex optimization; quasi epsilon; robust convex; optimization ... See more keywords
Fractional differential equation approach for convex optimization with convergence rate analysis
Sign Up to like & getrecommendations! Published in 2020 at "Optimization Letters"
DOI: 10.1007/s11590-019-01437-6
Abstract: A fractional differential equation (FDE) based algorithm for convex optimization is presented in this paper, which generalizes ordinary differential equation (ODE) based algorithm by providing an additional tunable parameter $$\alpha \in (0,1]$$ α ∈ (… read more here.
Keywords: convex optimization; fractional differential; convergence; differential equation ... See more keywords
Primal-dual subgradient method for constrained convex optimization problems
Sign Up to like & getrecommendations! Published in 2021 at "Optimization Letters"
DOI: 10.1007/s11590-021-01728-x
Abstract: This paper considers a general convex constrained problem setting where functions are not assumed to be differentiable nor Lipschitz continuous. Our motivation is in finding a simple first-order method for solving a wide range of… read more here.
Keywords: convex optimization; optimization; dual subgradient; primal dual ... See more keywords
A novel image segmentation approach for wood plate surface defect classification through convex optimization
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Forestry Research"
DOI: 10.1007/s11676-017-0572-7
Abstract: Detection of wood plate surface defects using image processing is a complicated problem in the forest industry as the image of the wood surface contains different kinds of defects. In order to obtain complete defect… read more here.
Keywords: image; plate surface; wood plate; convex optimization ... See more keywords
A new predictive motion control of a planar vehicle under uncertainty via convex optimization
Sign Up to like & getrecommendations! Published in 2017 at "International Journal of Control, Automation and Systems"
DOI: 10.1007/s12555-015-0129-5
Abstract: This paper proposes an optimization-based planning framework for target tracking of a planar vehicle in the presence of some moving obstacles and uncertainty resources. The framework is modeled based on Linear-Time Varying Model Predictive Control… read more here.
Keywords: convex optimization; uncertainty; control; vehicle ... See more keywords
A partially proximal S-ADMM for separable convex optimization with linear constraints
Sign Up to like & getrecommendations! Published in 2021 at "Applied Numerical Mathematics"
DOI: 10.1016/j.apnum.2020.09.016
Abstract: Abstract A classical approach to solving two-block separable convex optimization could be the symmetric alternating direction method of multipliers (S-ADMM). However, its convergence may not be guaranteed for a general multi-block case without additional assumptions.… read more here.
Keywords: convex optimization; admm; proximal admm; separable convex ... See more keywords
An inverse-free Zhang neural dynamic for time-varying convex optimization problems with equality and affine inequality constraints
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.06.051
Abstract: Abstract Time-varying convex optimization problems have attracted a great deal of attention in many fields due to its widespread application. Particularly, the approach to time-varying convex optimization problems with equality and affine inequality constraints simultaneously… read more here.
Keywords: varying convex; convex optimization; time varying; time ... See more keywords
A Dynamical Approach to Two-Block Separable Convex Optimization Problems with Linear Constraints
Sign Up to like & getrecommendations! Published in 2020 at "Numerical Functional Analysis and Optimization"
DOI: 10.1080/01630563.2020.1845730
Abstract: Abstract The aim of this manuscript is to approach by means of first order differential equations/inclusions convex programming problems with two-block separable linear constraints and objectives, whereby (at least) one of the components of the… read more here.
Keywords: block; convex optimization; linear constraints; optimization ... See more keywords