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Yi-Bin Xiao

Publications and source records attributed to Yi-Bin Xiao.

5 recordsLinked to original sources

Trajectory convergence and $o(t^{-2})$ rates for Nesterov accelerated primal-dual dynamics without Lipschitz gradient assumption

We consider the Nesterov accelerated primal-dual dynamical system \[ \begin{cases} \ddot{x}(t)+\dfracα{t}\dot{x}(t) +\nabla f(x(t)) +A^\top\bigl(λ(t)+θt\dotλ(t)\bigr)+βA^\top(Ax(t)-b)=0,\\[0.6em] \ddotλ(t)+\dfracα{t}\dotλ(t) -\bigl(A(x(t)+θt\dot{x}(t))-b\bigr)=0, \end{cases} \] which is linked to the linearly constrained optimization problem $ \min_{x\in\mathbb{R}^n} f(x),\ s.t.\ Ax=b, $ where $α\ge 3$ and $f$ is convex and continuously differentiable. In a Hilbert framework, the weak convergence of its trajectory was established by Boţ and Nguyen (J. Differential Equations, 303:369--406, 2021) under $α>3$ and the Lipschitz continuity assumption on $\nabla f$. In this paper, we prove in finite-dimensional spaces that the trajectory converges to a primal-dual solution for $α\ge3$, without assuming Lipschitz continuity of $\nabla f$. Moreover, when $α>3$, we establish improved $o(t^{-2})$ convergence rates for both the objective residual and the feasibility violation. Our analysis relies on Bregman-distance arguments, instead of the Lipschitz continuity of $\nabla f$. The same strategy can also be extended to time-scaled primal-dual dynamics to obtain analogous convergence results. To the best of our knowledge, this is the first results in this topic without Lipschitz gradient assumption. Our result also present the first work on the convergence of the trajectory of the accelerated primal-dual dynamical system for the critical case $α=3$.

math.OC↗

Convergence of iterates and improved rates for accelerated augmented Lagrangian methods for linearly constrained convex optimization

Motivated by an inertial primal-dual dynamical system with vanishing damping, we propose a class of accelerated augmented Lagrangian methods with Nesterov extrapolation parameters for a linearly constrained convex optimization problem with a differentiable objective function. The framework contains two variants: an implicit-gradient scheme for convex continuously differentiable objectives and a partially explicit scheme for convex smooth objectives. Under suitable parameter conditions, we prove convergence of the primal-dual sequence to a primal-dual solution, together with accelerated estimates for the augmented Lagrangian gap, the feasibility violation, and the objective residual. In the noncritical parameter regime, these estimates are improved from $\mathcal{O}(1/k^2)$ to $o(1/k^2)$. Numerical experiments are also presented to illustrate the theoretical results. To the best of our knowledge, neither $o(1/k^2)$ rates for both feasibility violation and objective residual nor convergence of iterates under the critical parameter condition have been previously established for accelerated augmented Lagrangian-type methods in this setting.

math.OC↗

Locally Lipschitz vector optimization problems: second-order constraint qualifications, regularity condition and KKT necessary optimality conditions

In the present paper, we are concerned with a class of constrained vector optimization problems, where the objective functions and active constraint functions are locally Lipschitz at the referee point. Some second-order constraint qualifications of Zangwill type, Abadie type and Mangasarian~--~Fromovitz type as well as a regularity condition of Abadie type are proposed in a nonsmooth setting. The connections between these proposed conditions are established. They are applied to develop second-order Karush--Kuhn--Tucker necessary optimality conditions for local (weak, Geoffrion properly) efficient solutions to the considered problem. Examples are also given to illustrate the obtained results.

math.OC↗

On AKKT optimality conditions for cone-constrained vector optimization problems

In this paper, we introduce a kind of approximate Karush--Kuhn--Tucker condition (AKKT) for a smooth cone-constrained vector optimization problem. We show that, without any constraint qualification, the AKKT condition is a necessary for a local weak efficient solution of the considered problem. For convex problems, we prove that the AKKT condition is a necessary and sufficient optimality condition for a global weak efficient solution. We also introduce some strict constraint qualifications associated with the AKKT condition.

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