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Table of Content

    30 September 2024, Volume 12 Issue 3
    An Accelerated Stochastic Mirror Descent Method
    Bo-Ou Jiang, Ya-Xiang Yuan
    2024, 12(3):  549-571.  doi:10.1007/s40305-023-00492-2
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    Driven by large-scale optimization problems arising from machine learning, the development of stochastic optimization methods has witnessed a huge growth. Numerous types of methods have been developed based on vanilla stochastic gradient descent method. However, for most algorithms, convergence rate in stochastic setting cannot simply match that in deterministic setting. Better understanding the gap between deterministic and stochastic optimization is the main goal of this paper. Specifically, we are interested in Nesterov acceleration of gradient-based approaches. In our study, we focus on acceleration of stochastic mirror descent method with implicit regularization property. Assuming that the problem objective is smooth and convex or strongly convex, our analysis prescribes the method parameters which ensure fast convergence of the estimation error and satisfied numerical performance.
    Trace Lasso Regularization for Adaptive Sparse Canonical Correlation Analysis via Manifold Optimization Approach
    Kang-Kang Deng, Zheng Peng
    2024, 12(3):  573-599.  doi:10.1007/s40305-022-00449-x
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    Canonical correlation analysis (CCA) describes the relationship between two sets of variables by finding a linear combination that maximizes the correlation coefficient. However, in high-dimensional settings where the number of variables exceeds sample size, or in the case that the variables are highly correlated, the traditional CCA is no longer appropriate. In this paper, a new matrix regularization is introduced, which is an extension of the trace Lasso in the vector case. Then we propose an adaptive sparse version of CCA (ASCCA) to overcome these disadvantages by utilizing the trace Lasso regularization. The adaptability of ASCCA is that the sparsity regularization of canonical vectors depends on the sample data, which is more realistic in practical applications. The ASCCA model is further reformulated to an optimization problem on the Riemannian manifold. Then we adopt a manifold inexact augmented Lagrangian method to solve the resulting optimization problem. The performance of the ASCCA model is compared with some existing sparse CCA techniques in different simulation settings and real datasets.
    The Perturbation Bound of the Extended Vertical Linear Complementarity Problem
    Shi-Liang Wu, Wen Li, He-Hui Wang
    2024, 12(3):  601-625.  doi:10.1007/s40305-023-00456-6
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    In this paper, we discuss the perturbation analysis of the extended vertical linear complementarity problem (EVLCP). Under the assumption of the row $\mathcal{W}$-property, we derive several absolute and relative perturbation bounds of EVLCP, which extend some existing results. Several numerical examples are given to show the proposed bounds.
    Greedy is Good: Constrained Non-submodular Function Maximization via Weak Submodularity
    Ma-Jun Shi, Wei Wang
    2024, 12(3):  627-648.  doi:10.1007/s40305-022-00444-2
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    The widely used greedy algorithm has been recently shown to achieve near-optimal theoretical guarantees for the problems of constrained monotone non-submodular function maximization, with competitive performances in practice. In this paper, we investigate the problems ofmaximizingmonotone non-submodular set functions under three classes of independent system constraints, including p-matroid intersection constraints, p-extendible system constraints and p-system constraints. We prove that the greedy algorithm yields an approximation ratio of $\frac{\gamma}{p+\gamma}$ for the former two problems, and $\frac{\xi \gamma}{p+\xi \gamma}$ for the last problem, which further has been improved to $\frac{\gamma}{p+\gamma}$, where γ, ξ denote the submodularity ratio and the diminishing returns ratio of set function respectively. In addition, we also show that the greedy guarantees have a further refinement of $\frac{\xi}{p+\alpha \xi}$ for all the problems mentioned above, where α is the generalized curvature of set function. Finally, we show that our greedy algorithm does yield competitive practical performances using a variety of experiments on synthetic data.
    Discrete Approximation and Convergence Analysis for a Class of Decision-Dependent Two-Stage Stochastic Linear Programs
    Jie Jiang, Zhi-Ping Chen
    2024, 12(3):  649-679.  doi:10.1007/s40305-022-00441-5
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    Customary stochastic programming with recourse assumes that the probability distribution of random parameters is independent of decision variables. Recent studies demonstrated that stochastic programming models with endogenous uncertainty can better reflect many real-world activities and applications accompanying with decisiondependent uncertainty. In this paper, we concentrate on a class of decision-dependent two-stage stochastic programs (DTSPs) and investigate their discrete approximation. To develop the discrete approximation methods for DTSPs, we first derive the quantitative stability results for DTSPs. Based on the stability conclusion, we examine two discretization schemes when the support set of random variables is bounded, and give the rates of convergence for the optimal value and optimal solution set of the discrete approximation problem to those of the original problem. Then we extend the proposed approaches to the general situation with an unbounded support set by using the truncating technique. As an illustration of our discretization schemes, we reformulate the discretization problems under specific structures of the decision-dependent distribution. Finally, an application and numerical results are presented to demonstrate our theoretical results.
    Minmax Common Due-Window Assignment Scheduling with Deteriorating Jobs
    Dan-Yang Lv, Jing Xue, Ji-Bo Wang
    2024, 12(3):  681-693.  doi:10.1007/s40305-023-00511-2
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    This paper investigates the common due-window assignment scheduling problem with deteriorating jobs on a single machine in which the processing time of a job is a proportional function of its starting time. The goal is to minimize the maximum value of earliness and tardiness penalties, as well as due-window location cost, and size cost. Depending on whether the location and size of due-window are known, this paper studies three relevant cases, all of which are solvable in polynomial time.
    Decision and Coordination in a Closed-Loop Supply Chain with Product Recycling Under Different Low-Carbon Regulations
    Qian-Dong Dong, Hai-Wen Xu
    2024, 12(3):  695-728.  doi:10.1007/s40305-022-00429-1
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    The implementation of recycling activity is of great significance to closed-loop supply chain operations. Consumers’ environmental awareness and governmental subsidy are considered in the two-echelon closed-loop supply chain. Two types of decision-making structures are proposed under different carbon emission regulations. The subsidy sharing mechanism is designed to resolve the channel members’ conflict caused by double marginalization. This paper provides insight into the effect of environmental awareness and government subsidy under different low-carbon regulations on the supply chain benefit and social welfare via the game theory. The analysis results display that the government subsidy and subsidy sharing mechanism are profitably beneficial to the whole supply chain and social welfare. Moreover, a noteworthy result reveals that social welfare is not always directly proportional to consumers’ environmental awareness. Social welfare decreases slightly with the increase in consumers’ environmental awareness when consumers’ environmental awareness is weak. However, social welfare will be significantly improved when consumers’ environmental awareness increases beyond a certain threshold.
    Approximation Algorithms for Solving the 1-Line Minimum Steiner Tree of Line Segments Problem
    Jian-Ping Li, Su-Ding Liu, Jun-Ran Lichen, Peng-Xiang Pan, Wen-Cheng Wang
    2024, 12(3):  729-755.  doi:10.1007/s40305-022-00437-1
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    We address the 1-line minimum Steiner tree of line segments (1L-MStT-LS) problem. Specifically, given a set S of n disjoint line segments in $\mathbb{R}^2$, we are asked to find the location of a line l and a set El of necessary line segments (i.e., edges) such that a graph consisting of all line segments in SEl plus this line l, denoted by Tl = (S, l, El), becomes a Steiner tree, the objective is to minimize total length of edges in El among all such Steiner trees. Similarly, we are asked to find a set E0 of necessary edges such that a graph consisting of all line segments in SE0, denoted by TS = (S, E0), becomes a Steiner tree, the objective is to minimize total length of edges in E0 among all such Steiner trees, we refer to this new problem as the minimum Steiner tree of line segments (MStT-LS) problem. In addition, when two endpoints of each edge in E0 need to be located on two different line segments in S, respectively, we refer to that problem as the minimum spanning tree of line segments (MST-LS) problem. We obtain three main results: (1) Using technique of Voronoi diagram of line segments, we design an exact algorithm in time O(nlogn) to solve the MST-LS problem; (2) we show that the algorithm designed in (1) is a 1.214-approximation algorithm to solve the MStT-LS problem; (3) using the combination of the algorithm designed in (1) as a subroutine for many times, a technique of finding linear facility location and a key lemma proved by techniques of computational geometry, we present a 1.214-approximation algorithm in time O(n3logn) to solve the 1L-MStT-LS problem.
    An Enhanced Conic Reformulation for Capacity-Constrained Assortment Optimization Under the Mixture of Multinomial Logit Model
    Shan Jiang, Ka-Meng Nip
    2024, 12(3):  757-771.  doi:10.1007/s40305-022-00438-0
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    In this work, we study the conic quadratic mixed-integer formulation for assortment optimization problem under the mixture of multinomial logit (MMNL) model. The MMNL model generalizes the widely studied multinomial logit choice model and can approximate any random utility model with an arbitrary additive error. An important operational decision problem in revenue management is assortment optimization problem, which aims to find a subset of products to make available to customers that maximizes the expected revenue of the retailer. It is known that assortment optimization problem under the MMNL model is NP-hard and inapproximable within any constant performance guarantee. Commonly used methods for solving such problem are heuristical approaches or customized combinatorial optimization approaches. In the meanwhile, studies related to global optimization approaches are relatively scarce. We propose an enhanced conic quadratic mixed-integer formulation for solving assortment optimization problem under the MMNL model with a higher computational efficiency. Furthermore, we conduct extensive numerical experiments to demonstrate that the proposed reformulation significantly outperforms the existing conic reformulations for assortment optimization under the MMNL model.
    Convergence, Scalarization and Continuity of Minimal Solutions in Set Optimization
    Karuna, C. S. Lalitha
    2024, 12(3):  773-793.  doi:10.1007/s40305-022-00440-6
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    The paper deals with the study of two different aspects of stability in the given space as well as the image space, where the solution concepts are based on a partial order relation on the family of bounded subsets of a real normed linear space. The first aspect of stability deals with the topological set convergence of families of solution sets of perturbed problems in the image space and Painlevé-Kuratowski set convergence of solution sets of the perturbed problems in the given space. The convergence in the given space is also established in terms of solution sets of scalarized perturbed problems. The second aspect of stability deals with semicontinuity of the solution set maps of parametric perturbed problems in both the spaces.
    Maximizing the Differences Between a Monotone DR-Submodular Function and a Linear Function on the Integer Lattice
    Zhen-Ning Zhang, Dong-Lei Du, Ran Ma, Dan Wu
    2024, 12(3):  795-807.  doi:10.1007/s40305-022-00393-w
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    In this paper, we investigate the maximization of the differences between a nonnegative monotone diminishing return submodular (DR-submodular) function and a nonnegative linear function on the integer lattice. As it is almost unapproximable for maximizing a submodular function without the condition of nonnegative, we provide weak (bifactor) approximation algorithms for this problem in two online settings, respectively. For the unconstrained online model, we combine the ideas of single threshold greedy, binary search and function scaling to give an efficient algorithm with a 1/2 weak approximation ratio. For the online streaming model subject to a cardinality constraint, we provide a one-pass ($3-\sqrt{5}$)/2 weak approximation ratio streaming algorithm. Its memory complexity is (klogk/ε), and the update time for per element is (log2k/ε).
    Cyclic Gradient Methods for Unconstrained Optimization
    Ya Zhang, Cong Sun
    2024, 12(3):  809-828.  doi:10.1007/s40305-022-00432-6
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    Gradient method is popular for solving large-scale problems. In this work, the cyclic gradient methods for quadratic function minimization are extended to general smooth unconstrained optimization problems. Combining with nonmonotonic line search, we prove its global convergence. Furthermore, the proposed algorithms have sublinear convergence rate for general convex functions, and R-linear convergence rate for strongly convex problems. Numerical experiments show that the proposed methods are effective compared to the state of the arts.
    An Upper Bound for the Transversal Number of Connected k-Uniform Hypergraphs
    Zi-An Chen, Bin Chen
    2024, 12(3):  829-835.  doi:10.1007/s40305-022-00445-1
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    Let H be a hypergraph with vertex set V(H) and hyperedge set E(H). We call a vertex set R $\subseteq$ V(H) a transversal if it has a nonempty intersection with every hyperedge of H. The transversal number, denoted by τ(H), is the minimum cardinality of transversals. In 2021, Diao verified that the upper bound of transversal number for any connected 3-uniform hypergraph H is at most $\frac{2 m+1}{3}$, that is, τ(H) $\frac{2 m+1}{3}$, where m is the size of H. Moreover, they gave the necessary and sufficient conditions to reach the upper bound, namely τ(H) = $\frac{2 m+1}{3}$, if and only if H is a hypertree with a perfect matching. In this paper, we investigate the transversal number of connected kuniform hypergraphs for k ≥ 3. We confirm that τ(H) $\frac{(k-1) m+1}{k}$ for any k-uniform hypergraph H with size m. Furthermore, we show that τ(H) = $\frac{(k-1) m+1}{k}$ if and only if H is a hypertree with a perfect matching, which generalizes the results of Diao.
    A Note on B-Subdifferential of Projection Operator on Polyhedral Set
    Hao-Yang Liu, Jia Wu, Li-Wei Zhang
    2024, 12(3):  837-845.  doi:10.1007/s40305-022-00433-5
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    This note characterizes the set of Fréchet-differentiable points of the projection operator on a polyhedral set and the B-subdifferential of this projection operator at any point.