Publications

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Journal Articles

A unified differential equation solver approach for separable convex optimization: Splitting, acceleration and nonergodic rate

Mathematics of Compuation, 2025

This paper provides a self-contained ordinary differential equation solver approach for separable con- vex optimization problems. A novel primal-dual dynamical system with built-in time rescaling factors is introduced, and the exponential decay of a tailored Lyapunov function is established. Then several time dis- cretizations of the continuous model are considered and analyzed via a unified discrete Lyapunov function. Moreover, two families of accelerated proximal alternating direction methods of multipliers are obtained, and nonergodic optimal mixed-type convergence rates shall be proved for the primal objective residual, the feasi- bility violation and the Lagrangian gap. Finally, numerical experiments are provided to validate the practical performances.

Recommended citation:
Hao, Luo and Zihang, Zhang. (2025). "A unified differential equation solver approach for separable convex optimization: Splitting, acceleration and nonergodic rate" Math. Comp. 94(352).
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A universal accelerated primal–dual method for convex optimization problems

Journal of Optimization Theory and Applications, 2024

This work presents a universal accelerated primal–dual method for affinely constrained convex optimization problems. It can handle both Lipschitz and Hölder gradients but does not need to know the smoothness level of the objective function. In line search part, it uses dynamically decreasing parameters and produces approximate Lipschitz constant with moderate magnitude. In addition, based on a suitable discrete Lyapunov function and tight decay estimates of some differential/difference inequalities, a universal optimal mixed-type convergence rate is established. Some numerical tests are provided to confirm the efficiency of the proposed method.

Recommended citation:
Hao, Luo. (2024). "A universal accelerated primal–dual method for convex optimization problems" J. Optim. Theory Appl. 201(1).
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From differential equation solvers to accelerated first-order methods for convex optimization

Mathematical Programming, 2022

Convergence analysis of accelerated first-order methods for convex optimization problems are developed from the point of view of ordinary differential equation solvers. A new dynamical system, called Nesterov accelerated gradient (NAG) flow, is derived from the connection between acceleration mechanism and A-stability of ODE solvers, and the exponential decay of a tailored Lyapunov function along with the solution trajectory is proved. Numerical discretizations of NAG flow are then considered and convergence rates are established via a discrete Lyapunov function. The proposed differential equation solver approach can not only cover existing accelerated methods, such as FISTA, Güler’s proximal algorithm and Nesterov’s accelerated gradient method, but also produce new algorithms for composite convex optimization that possess accelerated convergence rates. Both the convex and the strongly convex cases are handled in a unified way in our approach.

Recommended citation:
Hao, Luo and Long, Chen. (2022). "From differential equation solvers to accelerated first-order methods for convex optimization" Math. Program. 195.
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A primal-dual flow for affine constrained convex optimization

ESAIM: Control, Optimisation and Calculus of Variations, 2022

We introduce a novel primal-dual flow for affine constrained convex optimization problems. As a modification of the standard saddle-point system, our flow model is proved to possess the exponential decay property, in terms of a tailored Lyapunov function. Then two primal-dual methods are obtained from numerical discretizations of the continuous problem, and global nonergodic linear convergence rate is established via a discrete Lyapunov function. Instead of solving the subproblem of the primal variable, we apply the semi-smooth Newton iteration to the inner problem with respect to the multiplier, provided that there are some additional properties such as semi-smoothness and sparsity. Finally, numerical tests on the linearly constrained l1-l2 minimization and the tot al-variation based image denoising model have been provided.

Recommended citation:
Hao, Luo. (2022). "A primal-dual flow for affine constrained convex optimization" ESAIM Control Optim. Calc. Var. 28.
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Optimal error estimates of a time-spectral method for fractional diffusion problems with low regularity data

Journal of Scientific Computing, 2022

This paper is devoted to the error analysis of a time-spectral algorithm for fractional diffusion problems of order α (0 <α<1). The solution regularity in the Sobolev space is revisited and new regularity results in the Besov space are established. A time-spectral algorithm is developed which adopts a standard spectral method and a conforming linear finite element method for temporal and spatial discretizations, respectively. Optimal error estimates are derived with nonsmooth data. Particularly, a sharp temporal convergence rate 1 + 2α is shown theoretically and numerically.

Recommended citation:
Hao, Luo and Xiaoping, Xie. (2022). "Optimal error estimates of a time-spectral method for fractional diffusion problems with low regularity data" J. Sci. Comput. 91(14).
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Hao Luo