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Pengcheng Xie

Publications and source records attributed to Pengcheng Xie.

27 records · Page 2Linked to original sources
arXiv2025

A novel numerical method tailored for unconstrained optimization problems

Unconstrained optimization problems become more common in scientific computing and engineering applications with the rapid development of artificial intelligence, and numerical methods for solving them more quickly and efficiently have been getting more attention and research. Moreover, an efficient method to minimize all kinds of objective functions is urgently needed, especially the nonsmooth objective function. Therefore, in the current paper, we focus on proposing a novel numerical method tailored for unconstrained optimization problems whether the objective function is smooth or not. To be specific, based on the variational procedure to refine the gradient and Hessian matrix approximations, an efficient quadratic model with $2n$ constrained conditions is established. Moreover, to improve the computational efficiency, a simplified model with 2 constrained conditions is also proposed, where the gradient and Hessian matrix can be explicitly updated, and the corresponding boundedness of the remaining $2n-2$ constrained conditions is derived. On the other hand, the novel numerical method is summarized, and approximation results on derivative information are also analyzed and shown. Numerical experiments involving smooth, derivative blasting, and non-smooth problems are tested, demonstrating its feasibility and efficiency. Compared with existing methods, our proposed method can efficiently solve smooth and non-smooth unconstrained optimization problems for the first time, and it is very easy to program the code, indicating that our proposed method not also has great application prospects, but is also very meaningful to explore practical complex engineering and scientific problems.

math.OC↗
arXiv2025

ReMU: Regional Minimal Updating for Model-Based Derivative-Free Optimization

Derivative-free optimization (DFO) problems are optimization problems where derivative information is unavailable or extremely difficult to obtain. Model-based DFO solvers have been applied extensively in scientific computing. Powell's NEWUOA (2004) and Wild's POUNDerS (2014) explore the numerical power of the minimal norm Hessian (MNH) model for DFO and contributed to the open discussion on building better models with fewer data to achieve faster numerical convergence. Another decade later, we propose the regional minimal updating (ReMU) models, and extend the previous models into a broader class. This paper shows motivation behind ReMU models, computational details, theoretical and numerical results on particular extreme points and the barycenter of ReMU's weight coefficient region, and the associated KKT matrix error and distance. Novel metrics, such as the truncated Newton step error, are proposed to numerically understand the new models' properties. A new algorithmic strategy, based on iteratively adjusting the ReMU model type, is also proposed, and shows numerical advantages by combining and switching between the barycentric model and the classic least Frobenius norm model in an online fashion.

math.OC↗
arXiv2024

On the Relationship between $Λ$-poisedness in Derivative-Free Optimization and Outliers in Local Outlier Factor

Derivative-free optimization (DFO) is a method that does not require the calculation of gradients or higher-order derivatives of the objective function, making it suitable for cases where the objective function is non-differentiable or the computation of derivatives is expensive. This communication discusses the importance of \(Λ\)-poisedness in DFO and the outliers detected by the Local Outlier Factor (LOF) on the optimization process. We discuss the relationship between \(Λ\)-poisedness in derivative-free optimization and outliers in local outlier factor.

math.OC↗
arXiv2024

A New Two-dimensional Model-based Subspace Method for Large-scale Unconstrained Derivative-free Optimization: 2D-MoSub

This paper proposes the method 2D-MoSub (2-dimensional model-based subspace method), which is a novel derivative-free optimization (DFO) method based on the subspace method for general unconstrained optimization and especially aims to solve large-scale DFO problems. Our method combines 2-dimensional quadratic interpolation models and trust-region techniques to iteratively update the points and explore the 2-dimensional subspace. Its framework includes initialization, constructing the interpolation set, building the quadratic interpolation model, performing trust-region trial steps, and updating the trust-region radius and subspace. We introduce the framework and computational details of 2D-MoSub, and discuss the poisedness and quality of the interpolation set in the corresponding 2-dimensional subspace. We also analyze some properties of our method, including the model's approximation error with projection property and the algorithm's convergence. Numerical results demonstrate the effectiveness and efficiency of 2D-MoSub for solving a variety of unconstrained optimization problems.

math.OC↗
arXiv2023

Derivative-Free Optimization with Transformed Objective Functions (DFOTO) and the Algorithm Based on the Least Frobenius Norm Updating Quadratic Model

Derivative-free optimization problems are optimization problems where derivative information is unavailable. The least Frobenius norm updating quadratic interpolation model function is one of the essential under-determined model functions for model-based derivative-free trust-region methods. This article proposes derivative-free optimization with transformed objective functions and gives a trust-region method with the least Frobenius norm model. The model updating formula is based on Powell's formula. The method shares the same framework with those for problems without transformations, and its query scheme is given. We propose the definitions related to optimality-preserving transformations to understand the interpolation model in our method. We prove the existence of model optimality-preserving transformations beyond translation transformation. The necessary and sufficient condition for such transformations is given. The affine transformation with a positive multiplication coefficient is not model optimality-preserving. We also analyze the corresponding least Frobenius norm updating model and its interpolation error when the objective function is affinely transformed. Convergence property of a provable algorithmic framework containing our model is given. Numerical results of solving test problems and a real-world problem with the implementation NEWUOA-Trans show that our method can successfully solve most problems with objective optimality-preserving transformations, even though such transformations will change the optimality of the model function. To our best knowledge, this is the first work providing the model-based derivative-free algorithm and analysis for transformed problems with the function evaluation oracle (not the function-value comparison oracle). This article also proposes the "moving-target" optimization problem.

math.OC↗
arXiv2023

Least $H^2$ Norm Updating Quadratic Interpolation Model Function for Derivative-free Trust-region Algorithms

Derivative-free optimization methods are numerical methods for optimization problems in which no derivative information is used. Such optimization problems are widely seen in many real applications. One particular class of derivative-free optimization algorithms is trust-region algorithms based on quadratic models given by under-determined interpolation. Different techniques in updating the quadratic model from iteration to iteration will give different interpolation models. In this paper, we propose a new way to update the quadratic model by minimizing the $H^2$ norm of the difference between neighbouring quadratic models. Motivation for applying the $H^2$ norm is given, and theoretical properties of our new updating technique are also presented. Projection in the sense of $H^2$ norm and interpolation error analysis of our model function are proposed. We obtain the coefficients of the quadratic model function by using the KKT conditions. Numerical results show advantages of our model, and the derivative-free algorithms based on our least $H^2$ norm updating quadratic model functions can solve test problems with fewer function evaluations than algorithms based on least Frobenius norm updating.

math.OC↗
arXiv2021

Plasma response to resonant magnetic perturbations near rotation zero-crossing in low torque plasmas

Plasma response to resonant magnetic perturbations (RMPs) near the pedestal top is crucial for accessing edge localized modes (ELMs) suppression in tokamaks. Since radial location of rotation zero-crossing plays a key role in determining the threshold for field penetration of RMP, plasma response may be different in low input torque plasmas. In this work, the linear MHD code MARS-F is applied to reveal the dependence of plasma response to RMP on rotation zero-crossing by a scan of rotation profiles based on an EAST equilibrium. It is shown that the plasma response is enhanced when zero-crossing occurs near rational surfaces. The dependence of plasma response on the location of rotation zero-crossing is well fitted by a double Gaussian, indicating two effects in this enhancement. One is induced by rotation screening effect shown as a wide base (with a width around 10-20 krad/s), and the other is related to resistive singular layer effect characterized by a localized peak (with a width around 3-4 krad/s). The width of the peak scales with the resistive singular layer width. The plasma displacement suggests the response is tearing like when zero-crossing is within the singular layer, while it is kink like when zero-crossing is far from the layer. The enhancement of magnetic islands width at the peak is only around a factor of two, when the absolute value of local rotation is not larger than 10-20 krad/s. It is further confirmed in a modeling of plasma response in an EAST ELM suppression discharge. Though there is a zero-crossing in $E\times B$ rotation but not in electron perpendicular rotation, no significant difference in plasma response is obtained using these two rotation profiles. This suggests that the rotation near pedestal top should not be far away from zero but may not be necessary to have zero-crossing for accessing ELM suppression.

physics.plasm-ph↗
arXiv2018

SVM-Based Sea-Surface Small Target Detection: A False-Alarm-Rate-Controllable Approach

In this letter, we consider the varying detection environments to address the problem of detecting small targets within sea clutter. We first extract three simple yet practically discriminative features from the returned signals in the time and frequency domains and then fuse them into a 3-D feature space. Based on the constructed space, we then adopt and elegantly modify the support vector machine (SVM) to design a learning-based detector that enfolds the false alarm rate (FAR). Most importantly, our proposed detector can flexibly control the FAR by simply adjusting two introduced parameters, which facilitates to regulate detector's sensitivity to the outliers incurred by the sea spikes and to fairly evaluate the performance of different detection algorithms. Experimental results demonstrate that our proposed detector significantly improves the detection probability over several existing classical detectors in both low signal to clutter ratio (SCR) (up to 58%) and low FAR (up to 40%) cases.

cs.IT↗