作者：
Shi, XiashengSu, LingfeiWang, QingAnhui Univ
Engn Res Ctr Autonomous Unmanned Syst Technol Minist Educ Hefei 230039 Peoples R China Anhui Univ
Anhui Prov Engn Res Ctr Unmanned Syst & Intelligen Hefei 230601 Peoples R China Beihang Univ
Sch Artificial Intelligence Beijing 100091 Peoples R China Beihang Univ
Sch Automat Sci & Elect Engn Beijing 100091 Peoples R China
The distributed nonsmooth constrained optimization problems over higher-order systems are investigated in this study. The challenges lies in the fact that the output of the agent is directly controlled by the state va...
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The distributed nonsmooth constrained optimization problems over higher-order systems are investigated in this study. The challenges lies in the fact that the output of the agent is directly controlled by the state variable rather than the control input. Compared to existing works, the local objectivefunction is merely assumed to be nonsmooth. Firstly, an initialization-free fully distributed derivative feedback control scheme is developed for the known objectivefunction over double-integrator systems. The local generic constraint is addressed by an adaptive nonnegative penalty factor. Secondly, an initialization-free fully distributed state feedback control scheme is proposed for the unknown objectivefunction over double-integrator systems. Addressing the local box constraint involves incorporating an adaptive penalty factor. Thirdly, the above two algorithms are extended to the general higher-order systems using the tracking control method. In addition, the above-developed methods are proved to be asymptotically convergent under certain conditions. Eventually, the efficiency of the above-produced methods is shown via four simulation cases.
In this paper, we consider Newton projection method for solving the quadratic programming problem that emerges in simulation of joining process for assembly with compliant parts. This particular class of problems has ...
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In this paper, we consider Newton projection method for solving the quadratic programming problem that emerges in simulation of joining process for assembly with compliant parts. This particular class of problems has specific features such as an ill-conditioned Hessian and a sparse matrix of constraints as well as a requirement for the large-scale computations. We use the projected Newton method with a quadratic rate of convergence and suggest some improvements to reduce the solving time: a method for solving the system of linear equations, so-called constraint recalculation method, and compare different approaches for step-size selection. We use the duality principle to formulate alternative forms of the minimization problem that, as a rule, can be solved faster. We describe how to solve the considered nonlinear minimization problem with the nonsmooth objective function by modifying Newton projection method and employing subgradients. In addition, we prove the convergence of the suggested algorithm. Finally, we compare Newton projection method with the other quadratic programming techniques on a number of assembly simulation problems.
This paper is concerned with a class of distributed nonsmooth convex constrained optimization problems with set constraints. The objectivefunction is a sum of local convex functions, which are not necessarily differe...
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ISBN:
(纸本)9781538626795
This paper is concerned with a class of distributed nonsmooth convex constrained optimization problems with set constraints. The objectivefunction is a sum of local convex functions, which are not necessarily differentiable. A new distributed continuous-time gradient-based algorithm using the decomposition design is explicitly constructed to solve the distributed optimization problem. Rigorous proofs using nonsmooth convex optimization theory and stability theory of differential inclusions are presented. A numerical simulation is conducted to show the efficacy of the proposed algorithm.
Iteratively reweighted algorithms are popular methods for solving nonconvex unconstrained minimization problems. Applications are notably mathematical models in image processing or signal processing. They often have a...
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Iteratively reweighted algorithms are popular methods for solving nonconvex unconstrained minimization problems. Applications are notably mathematical models in image processing or signal processing. They often have a convex subproblem and do not have closed form solutions in general. In this paper, we propose approximate versions of proximal iteratively reweighted algorithms for nonconvex and nonsmooth unconstrained minimization problems. Specifically, we can achieve an approximate solution for the subproblem by applying a computable inexact stopping rule. The convergence of our method is proved based on an inexact unified framework. Numerical applications for image deblurring or denoising problems validate the effectiveness of the proposed algorithms. (C) 2020 Elsevier B.V. All rights reserved.
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