arXiv subjects

Jiefeng Xu

Publications and source records attributed to Jiefeng Xu.

10 recordsLinked to original sources

arXiv2026

A smoothing extended sequential quadratic method for difference-of-convex optimization over a convex composite inequality constraint

We consider the problem of minimizing a difference-of-convex objective over a convex composite inequality constraint and a compact convex set constraint. To solve this problem, we extend the ESQM in [1] via incorporating a variable smoothing scheme. In essence, in each iteration of our algorithm, we apply one proximal gradient step to a smoothed penalty function, constructed based on a smooth approximation of the convex composite constraint function; and we design explicit rules to update the smoothing and penalty parameters. Under suitable constraint qualifications, we establish an iteration complexity of $O(\epsilon^{-3})$ for obtaining an $(\epsilon,\epsilon)$-KKT point. Moreover, in the convex setting, we show that the whole sequence generated by our algorithm is convergent and derive its local convergence rate under a standard H\"olderian growth condition.

math.OC↗

arXiv2025

A Variational Characterization and A Line Search Newton-Noda Method for the unifying spectral problem of nonnegative tensors

We study the general $(\boldsymbol{\sigma},\mathbf{p})$-eigenvalue problem of nonnegative tensors introduced by A. Gautier, F. Tudisco, and M. Hein [SIAM J. Matrix Anal. Appl., 40 (2019), pp. 1206--1231], which unifies several well-studied tensor eigenvalue and singular value problems. First, we propose an alternative min-max Collatz--Wielandt formula for the $(\boldsymbol{\sigma},\mathbf{p})$-spectral radius, which bypasses the auxiliary multihomogeneous mapping employed in that work. This variational characterization both recovers several classical results and admits a natural convex reformulation. It arises from an alternative approach that directly connects the $(\boldsymbol{\sigma},\mathbf{p})$-spectral problem to a class of convex programs. We then develop and analyze a line search Newton-Noda method (LS-NNM) for computing the positive $(\boldsymbol{\sigma},\mathbf{p})$-eigenpair of nonnegative tensors. The proposed method integrates Newton method with Noda iteration. The Newton equation is derived from an equivalent nonlinear system, while the eigenvalue sequence is updated by the strategy of the Noda iteration and its variants. To ensure global convergence, we introduce a positivity-preserving line search procedure based on an equivalent constrained optimization problem. The global and quadratic convergence of LS-NNM are established for the class of $(\boldsymbol{\sigma},\mathbf{p})$-spectral problem that admits a unique positive $(\boldsymbol{\sigma},\mathbf{p})$-eigenpair, as guaranteed by the Perron-Frobenius theorem. Finally, numerical experiments are conducted to illustrate the performance of LS-NNM.

math.OC↗

arXiv2025

A smoothing moving balls approximation method for a class of conic-constrained difference-of-convex optimization problems

In this paper, we consider the problem of minimizing a difference-of-convex objective over a nonlinear conic constraint, where the cone is closed, convex, pointed and has a nonempty interior. We assume that the support function of a compact base of the polar cone exhibits a majorizing smoothing approximation, a condition that is satisfied by widely studied cones such as $\mathbb{R}^m_-$ and ${\cal S}^m_-$. Leveraging this condition, we reformulate the conic constraint equivalently as a single constraint involving the aforementioned support function, and adapt the moving balls approximation (MBA) method for its solution. In essence, in each iteration of our algorithm, we approximate the support function by a smooth approximation function and apply one MBA step. The subproblems that arise in our algorithm always involve only one single inequality constraint, and can thus be solved efficiently via one-dimensional root-finding procedures. We design explicit rules to evolve the smooth approximation functions from iteration to iteration and establish the corresponding iteration complexity for obtaining an $(\epsilon_1, \epsilon_2, \epsilon_1 \sqrt{\epsilon_2})$-Karush-Kuhn-Tucker point. In addition, in the convex setting, we establish convergence of the sequence generated, and study its local convergence rate under a standard H\"olderian growth condition. Finally, we perform numerical experiments to illustrate the performance of our algorithm.

math.OC↗

arXiv2023

A Feasible Conjugate Gradient Method for Calculating $\mathcal B$-Eigenpairs of Symmetric Tensors

In this paper, we propose a feasible conjugate gradient (FCG) method for calculating ${\mathcal B}$-eigenpairs of a symmetric tensor ${\mathcal A}$. The method is an extension of the well-known conjugate gradient method for unconstrained optimization problems to some curve constrained optimization problems. The proposed FCG method can find a ${\mathcal B}$-eigenpair of a symmetric tensor ${\mathcal A}$ without the requirement that the orders of ${\mathcal A}$ and $\mathcal B$ are equal. We pay particular attention to the Polak-Ribíre-Polyak (PRP) type conjugate gradient method. We show that the FCG method with some Armijo-type line search is globally convergent. Our numerical experiments indicate the promising performance of the proposed method.

math.OC↗

arXiv2023

Finding the spectral radius of a nonnegative irreducible symmetric tensor via DC programming

The Perron-Frobenius theorem says that the spectral radius of an irreducible nonnegative tensor is the unique positive eigenvalue corresponding to a positive eigenvector. With this in mind, the purpose of this paper is to find the spectral radius and its corresponding positive eigenvector of an irreducible nonnegative symmetric tensor. By transferring the eigenvalue problem into an equivalent problem of minimizing a concave function on a closed convex set, which is typically a DC (difference of convex functions) programming, we derive a simpler and cheaper iterative method. The proposed method is well-defined. Furthermore, we show that both sequences of the eigenvalue estimates and the eigenvector evaluations generated by the method $Q$-linearly converge to the spectral radius and its corresponding eigenvector, respectively. To accelerate the method, we introduce a line search technique. The improved method retains the same convergence property as the original version. Preliminary numerical results show that the improved method performs quite well.

math.OC↗

arXiv2022

Feasible Newton's methods for symmetric tensor Z-eigenvalue problems

Finding a Z-eigenpair of a symmetric tensor is equivalent to finding a KKT point of a sphere constrained minimization problem. Based on this equivalency, in this paper, we first propose a class of iterative methods to get a Z-eigenpair of a symmetric tensor. Each method can generate a sequence of feasible points such that the sequence of function evaluations is decreasing. These methods can be regarded as extensions of the descent methods for unconstrained optimization problems. We pay particular attention to the Newton's method. We show that under appropriate conditions, the Newton's method is globally and quadratically convergent. Moreover, after finitely many iterations, the unit steplength will always be accepted. We also propose a nonlinear equations based Newton's method and establish its global and quadratic convergence. In the end, we do several numerical experiments to test the proposed Newton's methods. The results show that both Newton's methods are very efficient.

math.OC↗

arXiv2021

Retail Store Layout Optimization for Maximum Product Visibility

It is well-established that increased product visibility to shoppers leads to higher sales for retailers. In this study, we propose an optimization methodology which assigns product categories and subcategories to store locations and sublocations to maximize the overall visibility of products to shoppers. The methodology is hierarchically developed to meet strategic and tactical layout planning needs of brick-and-mortar retailers. Layouts in both levels of planning are optimized considering eligibility requirements and complete set of shopper paths, thus, they successfully capture the unique shopping behavior of consumers in a store's region. The resulting mathematical optimization problem is recognized as a special instance of the well-known Quadratic Assignment Problem, which is considered computationally as one of the hardest optimization problems. We adopt a linearization technique and demonstrate via a real-world numerical example that our linearized optimization models substantially improve the store layout, hence, can be used in practical applications as a vital decision support model for store layout planning.

math.OC↗

arXiv2021

Robust Price Optimization of Multiple Products under Interval Uncertainties

In this paper, we solve the multiple product price optimization problem under interval uncertainties of the price sensitivity parameters in the demand function. The objective of the price optimization problem is to maximize the overall revenue of the firm where the decision variables are the prices of the products supplied by the firm. We propose an approach that yields optimal solutions under different variations of the estimated price sensitivity parameters. We adopt a robust optimization approach by building a data-driven uncertainty set for the parameters, and then construct a deterministic counterpart for the robust optimization model. The numerical results show that two objectives are fulfilled: the method reflects the uncertainty embedded in parameter estimations, and also an interval is obtained for optimal prices. We also conducted a simulation study to which we compared the results of our approach. The comparisons show that although robust optimization is deemed to be conservative, the results of the proposed approach show little loss compared to those from the simulation.

math.OC↗

arXiv2021

Marketing Mix Optimization with Practical Constraints

In this paper, we address a variant of the marketing mix optimization (MMO) problem which is commonly encountered in many industries, e.g., retail and consumer packaged goods (CPG) industries. This problem requires the spend for each marketing activity, if adjusted, be changed by a non-negligible degree (minimum change) and also the total number of activities with spend change be limited (maximum number of changes). With these two additional practical requirements, the original resource allocation problem is formulated as a mixed integer nonlinear program (MINLP). Given the size of a realistic problem in the industrial setting, the state-of-the-art integer programming solvers may not be able to solve the problem to optimality in a straightforward way within a reasonable amount of time. Hence, we propose a systematic reformulation to ease the computational burden. Computational tests show significant improvements in the solution process.

math.OC↗

arXiv2020

Maximizing Store Revenues using Tabu Search for Floor Space Optimization

Floor space optimization is a critical revenue management problem commonly encountered by retailers. It maximizes store revenue by optimally allocating floor space to product categories which are assigned to their most appropriate planograms. We formulate the problem as a connected multi-choice knapsack problem with an additional global constraint and propose a tabu search based meta-heuristic that exploits the multiple special neighborhood structures. We also incorporate a mechanism to determine how to combine the multiple neighborhood moves. A candidate list strategy based on learning from prior search history is also employed to improve the search quality. The results of computational testing with a set of test problems show that our tabu search heuristic can solve all problems within a reasonable amount of time. Analyses of individual contributions of relevant components of the algorithm were conducted with computational experiments.

cs.AI↗