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A draft of a paper by Mandelbaum, "The dynamic complementarity problem", was circulated in 1987, but has never been published. We give an exposition of two important results from that paper which are not readily accessible in the literature. The first is an example of a Skorokhod problem in two dimensions in the quadrant for which there is not uniqueness. The second is a proof of uniqueness for the Skorokhod problem in two dimensions in the quadrant in a critical case.

In this note, we give the possible revised version of the unique solvability conditions for the two incorrect results that appeared in the published paper by Wu et al. (Appl Math Lett 76:195-200, 2018).

Borsuk-Ulam's theorem is a useful tool of algebraic topology. It states that for any continuous mapping $f$ from the $n$-sphere to the $n$-dimensional Euclidean space, there exists a pair of antipodal points such that $f(x)=f(-x)$. As for its applications, ham-sandwich theorem, necklace theorem and coloring of Kneser graph by Lov\'{a}sz are well-known. This paper attempts to apply Borsuk-Ulam's theorem to nonlinear programming., Comment: 9 pages, 1 figure

This erratum highlights a very serious error in a paper published by SIAM Review in 2011. The error is in Section 6.2 of [1]. It is very important to notify this error because of the following two reasons: (i) [1] is one of the most cited contributions in the field of identifiability of viral dynamics models, and (ii)the error is relevant because, as a result of it, a very popular viral model (perhaps the most popular in the field of HIV dynamics) has been classified as identifiable. In contrast, three of its parameters are not identifiable, even locally. This erratum first proves the non uniqueness of the three unidentifiable parameters by exhibiting infinitely many distinct but indistinguishable values of them. The non uniqueness is even local. Then, this erratum details the error made by the authors of [1] which produced the claimed (but false) local identifiability of all the model parameters., Comment: SIAM review refuses to publish this erratum. This is in my opinion a very wrong decision which significantly deteriorates the rank of this journal. Or, even worse, this decision highlights an unfortunately low rank of this journal

This note presents a summary and review of various conditions and characterizations for matrix stability (in particular diagonal matrix stability) and matrix stabilizability.

I consider a setting where reviewers offer very noisy scores for several items for the selection of high-quality ones (e.g., peer review of large conference proceedings), whereas the owner of these items knows the true underlying scores but prefers not to provide this information. To address this withholding of information, in this paper, I introduce the Isotonic Mechanism, a simple and efficient approach to improving imprecise raw scores by leveraging certain information that the owner is incentivized to provide. This mechanism takes the ranking of the items from best to worst provided by the owner as input, in addition to the raw scores provided by the reviewers. It reports the adjusted scores for the items by solving a convex optimization problem. Under certain conditions, I show that the owner's optimal strategy is to honestly report the true ranking of the items to her best knowledge in order to maximize the expected utility. Moreover, I prove that the adjusted scores provided by this owner-assisted mechanism are significantly more accurate than the raw scores provided by the reviewers. This paper concludes with several extensions of the Isotonic Mechanism and some refinements of the mechanism for practical consideration., Comment: Corrected typos and added a reference

Many scientific conferences employ a two-phase paper review process, where some papers are assigned additional reviewers after the initial reviews are submitted. Many conferences also design and run experiments on their paper review process, where some papers are assigned reviewers who provide reviews under an experimental condition. In this paper, we consider the question: how should reviewers be divided between phases or conditions in order to maximize total assignment similarity? We make several contributions towards answering this question. First, we prove that when the set of papers requiring additional review is unknown, a simplified variant of this problem is NP-hard. Second, we empirically show that across several datasets pertaining to real conference data, dividing reviewers between phases/conditions uniformly at random allows an assignment that is nearly as good as the oracle optimal assignment. This uniformly random choice is practical for both the two-phase and conference experiment design settings. Third, we provide explanations of this phenomenon by providing theoretical bounds on the suboptimality of this random strategy under certain natural conditions. From these easily-interpretable conditions, we provide actionable insights to conference program chairs about whether a random reviewer split is suitable for their conference.

An important problem in decision theory concerns the aggregation of individual rankings/ratings into a collective evaluation. We illustrate a new aggregation method in the context of the 2007 MSOM's student paper competition. The aggregation problem in this competition poses two challenges. Firstly, each paper was reviewed only by a very small fraction of the judges; thus the aggregate evaluation is highly sensitive to the subjective scales chosen by the judges. Secondly, the judges provided both cardinal and ordinal evaluations (ratings and rankings) of the papers they reviewed. The contribution here is a new robust methodology that jointly aggregates ordinal and cardinal evaluations into a collective evaluation. This methodology is particularly suitable in cases of incomplete evaluations -- i.e., when the individuals evaluate only a strict subset of the objects. This approach is potentially useful in managerial decision making problems by a committee selecting projects from a large set or capital budgeting involving multiple priorities.

The production of corrugated paper boxes accounts for roughly one third of the world's total paper production and, as a result of both COVID-19 and the rise of e-commerce, is a growing market. We provide a fresh approach to determining near-optimal stock policies for integrated paper companies. The new approach shows that existing policies can be improved by a significant margin. In a case study we saw a reduction in total waste by 9%, with a simultaneous decrease in logistics costs., Comment: New version corrects a couple of typos

The dissipativity concept sits at the intersection of physics, systems theory, and control engineering, as a natural generalisation of passive systems that dissipate energy. It relates the external behavior of systems to their internal state, and connects the subjects of optimal control, algebraic Riccati equations, linear matrix inequalities, complex functions, and spectral factorization. Within control, its applications include the analysis and design of interconnected systems (such as cyber-physical systems), robustness, and the absolute stability problem, and network synthesis (of electrical, mechanical, and multi-physics systems). This article details recent developments in the treatment of dissipativity and the related concept of reciprocity for systems that are not necessarily controllable and need not lend themselves naturally to an input-state-output perspective, as is the case for many physical and passive systems. We illustrate these concepts using simple electric circuit and mechanical network examples., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

We consider some critical claims concerning our above paper, and reply to these claims.

The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision-free paths for a number of agents, from a set of start locations to a set of goal positions in a known 2D environment. MAPF has been studied in theoretical computer science, robotics, and artificial intelligence over several decades, due to its importance for robot navigation. It is currently experiencing significant scientific progress due to its relevance in automated warehousing (such as those operated by Amazon) and in other contemporary application areas. In this paper, we demonstrate that many recently developed MAPF algorithms apply more broadly than currently believed in the MAPF research community. In particular, we describe the 3D Pipe Routing (PR) problem, which aims at placing collision-free pipes from given start locations to given goal locations in a known 3D environment. The MAPF and PR problems are similar: a solution to a MAPF instance is a set of blocked cells in x-y-t space, while a solution to the corresponding PR instance is a set of blocked cells in x-y-z space. We show how to use this similarity to apply several recently developed MAPF algorithms to the PR problem, and discuss their performance on abstract PR instances. We also discuss further research necessary to tackle real-world pipe-routing instances of interest to industry today. This opens up a new direction of industrial relevance for the MAPF research community., Comment: 6 pages, 5 figures

The study of the movement of flocks, whether biological or technological is motivated by the desire to understand the capability of coherent motion of a large number of agents that only receive very limited information. In a biological flock a large group of animals seek their course while moving in a more or less fixed formation. It seems reasonable that the immediate course is determined by leaders at the boundary of the flock. The others follow: what is their algorithm? The most popular technological application consists of cars on a one-lane road. The light turns green and the lead car accelerates. What is the efficient algorithm for the others to closely follow without accidents? In this position paper we present some general questions from a more fundamental point of view. We believe that the time is right to solve many of these questions: they are within our reach., Comment: 7 pages, 10 figures

We investigate stability of a solution of a hybrid system in the sense that the graphs of solutions from nearby initial conditions remain close and tend towards the graph of the given solution. In this manner, a small continuous-time mismatch is allowed between the jump times of neighbouring solutions and the `peaking phenomenon' is avoided. We provide conditions such that this stability notion is implied by stability with respect to a specifically designed distance-like function. Hence, stability of solutions in the graphical sense can be analysed with existing Lyapunov techniques.

This short paper aims to investigate some of the historical developments of one classic, well-cited and highly esteemed scientific journal in the domain of quantitative operations research - namely the INFORMS journal Operations Research - over a period of 25 years between 1981 and 2006. As such this paper, and the journal in question, represents one representative attempt to analyze - for the purpose of possible future generalization - how research production has evolved, and evolves, over time. Among the general developments that we think we can trace are that (a) the historical overviews (i.e., literature surveys) in the articles, as well as the list of references, somewhat counter-intuitively shrink over time, while (b) the motivating and modelling parts grow. We also attempt to characterize - in some detail - the appearance and character, over time, of the most cited, as well as the least cited, papers over the years studied. In particular, we find that many of the least cited papers are quite imbalanced. For example, some of them include one main section only, and the least cited papers also have shorter reference lists. We also analyse the articles' utilization of important buzz words representing the constitutive parts of an OR journal paper, based on Subben's checklist (Larsson and Patriksson, 2014, 2016). Based on a word count of these buzz words we conclude through a citation study, utilizing a collection of particularly highly or little cited papers, that there is a quite strong positive correlation between a journal paper being highly cited and its degree of utilization of this checklist.

The aim of this paper is to discuss the main result in the paper by D.Y. Gao and X. Lu [On the extrema of a nonconvex functional with double-well potential in 1D, Z. Angew. Math. Phys. (2016) 67:62]. More precisely we provide a detailed study of the problem considered in that paper, pointing out the importance of the norm on the space $C^{1}[a,b]$; because no norm (topology) is mentioned on $C^{1}[a,b]$ we look at it as being a subspace of $W^{1,p}(a,b)$ for $p\in [1,\infty]$ endowed with its usual norm. We show that the objective function has not local extrema with the mentioned constraints for $p\in [1,4)$, and has (up to an additive constant) only a local maximizer for $p=\infty$, unlike the conclusion of the main result of the discussed paper where it is mentioned that there are (up to additive constants) two local minimizers and a local maximizer. We also show that the same conclusions are valid for the similar problem treated in the preprint by X. Lu and D.Y. Gao [On the extrema of a nonconvex functional with double-well potential in higher dimensions, arXiv:1607.03995]., Comment: 12 pages; in this version we added the forgotten condition $F(x) \ne 0$ for $x\in (a,b)$ on page 3

In this work, we use a multiobjective approach to address the lot sizing problem integrated with the cutting stock problem in a paper industry. We analyze the trade-offs and correlations which exist among the costs and their decision variables. Considering some of our computational results, if we decrease the production costs, then we increase the waste of material of the cutting process and vice versa. Thereby we show the importance of the multiobjective approach in allowing multiple answers to the decision maker, using Pareto optimal solutions set. Several tests were performed to check the quality of our approach., Comment: 13 pages, 5 figures, 3 tables

This working paper is divided into two parts. Firstly, we develop a new combined equilibrium model of business land-use, which puts travelers' traffic equilibrium and business companies' competitive location equilibrium into a unified framework. A variational inequality is presented for the combined equilibrium and the properties of equilibrium solution are investigated. Secondly, the congestion pricing principles associated with the combined equilibrium are studied. From the mathematical point of view, we prove that there exists an optimal road pricing scheme that can minimize the social cost of travelers. This road pricing scheme generalizes the traditional link-based optimal road pricing scheme. Furthermore, when simultaneously imposing charges on travelers and companies is allowed, we prove that there exists an optimal congestion pricing scheme that can derive a combined equilibrium toward an overall system optimum. The economic meaning of every pricing scheme proposed in this paper is discussed in detail. At last, a simple numerical example is used to demonstrate that the optimal congestion pricing scheme may indeed reduce the social cost.

In this short note we prove by a counter-example that Theorem 3.2 in the paper "A study on concave optimization via canonical dual function" by J. Zhu, S. Tao, D. Gao is false; moreover, we give a very short proof for Theorem 3.1 in the same paper., Comment: This paper has been submitted to JCAM (Journal of Computational and Applied Mathematics) on April 8, 2010 and was rejected on October 13, 2011. There was not given any reason for the rejection. For more details see http://www.math.uaic.ro/~zalinesc/

In this note we prove that a recent result stated by D.Y. Gao and R.W. Ogden on global minimizers and local extrema in a phase transition problem is false. Our goal is achieved by providing a thorough analysis of the context and result in question and counter-examples., Comment: This paper has been submitted to QJMAM (The Quarterly Journal of Mechanics and Applied Mathematics) on February 19, 2010 and was rejected on October 19, 2010. The reason for the rejection was that "the paper is more like a pure math paper" and this decision was taken mainly after the editor investigated our web-pages. For more details see http://www.math.uaic.ro/~zalinesc/

Synchronization problem for linear coupled networks is a hot topic in recent decade. However, until now, some confused concepts and results still puzzle people. To avoid further misleading researchers, it is necessary to point out these misunderstandings, correct these mistakes and give precise results.

In this paper, we consider the 1D wave equation where the spatial domain is a bounded interval. Assuming the initial conditions to be known, we are here interested in identifying an unknown source term, while we take the Neumann derivative of the solution on one of the boundaries as the measurement output. Applying a back-and-forth iterative scheme and constructing well-chosen observers, we retrieve the source term from the measurement output in the minimal observation time. We further provide an extension of the method to the case of wave equations with N dimensional spatial domain., Comment: 26 pages, 2 figures

In their recent SIAM J. Control Optim. paper from 2009, J. Eckstein and B.F. Svaiter proposed a very general and flexible splitting framework for finding a zero of the sum of finitely many maximal monotone operators. In this short note, we provide a technical result that allows for the removal of Eckstein and Svaiter's assumption that the sum of the operators be maximal monotone or that the underlying Hilbert space be finite-dimensional.

The paper develops a singularity-free backstepping-based adaptive control for stabilizing and tracking the trajectory of a bicopter system. In the bicopter system, the inertial parameters parameterize the input map. Since the classical adaptive backstepping technique requires the inversion of the input map, which contains the estimate of parameter estimates, the stability of the closed-loop system cannot be guaranteed due to the inversion of parameter estimates. This paper proposes a novel technique to circumvent the inversion of parameter estimates in the control law. The resulting controller requires only the sign of the unknown parameters. The proposed controller is validated in simulation for a smooth and nonsmooth trajectory-tracking problem., Comment: arXiv admin note: text overlap with arXiv:2402.03709

This paper proposes a model predictive controller for discrete-time linear systems with additive, possibly unbounded, stochastic disturbances and subject to chance constraints. By computing a polytopic probabilistic positively invariant set for constraint tightening with the help of the computation of the minimal robust positively invariant set, the chance constraints are guaranteed, assuming only the mean and covariance of the disturbance distribution are given. The resulting online optimization problem is a standard strictly quadratic programming, just like in conventional model predictive control with recursive feasibility and stability guarantees and is simple to implement. A numerical example is provided to illustrate the proposed method., Comment: This paper was accepted for publication in CDC 2024

Reconfigurable Intelligent Surfaces (RISs) can be designed in various ways. A previously proposed semidefinite relaxation-based optimization method for maximizing power transfer efficiency showed promise, but earlier results were only theoretical. This paper evaluates a small RIS at 3.55GHz, the center of the 5G band "n78", for practical verification of this method. The presented results not only empirically confirm the desired performance of the optimized RIS, but also affirm the optimality of the resulting reactance values. Additionally, this paper discusses several practical aspects of RIS design and measurement, such as the operation of varactor diodes and time gating to omit the direct line-of-sight (LOS) path., Comment: 5 pages, conference

This paper addresses the challenges of high-dimensional non-convex optimization, particularly the inefficiencies caused by saddle points. The authors propose several techniques for detecting, evading, and optimizing in the presence of these saddle points. We begin by analyzing saddle point detection through the Hessian spectrum, showing that the likelihood of encountering saddle points increases with dimensionality. We introduce stochastic gradient perturbation, which adds noise to escape saddle points and avoid premature convergence, and emphasize the importance of gradient flow dynamics and adaptive learning rates in ensuring convergence to local minima. The paper validates these methods within constrained optimization problems and explores randomized subspace optimization, reducing search space dimensionality while maintaining global convergence efficiency. These findings offer a comprehensive framework for enhancing the reliability and efficiency of high-dimensional non-convex optimization.

This paper considers the problem of minimizing a differentiable function with locally Lipschitz continuous gradient on the algebraic variety of all $m$-by-$n$ real matrices of rank at most $r$. Several definitions of stationarity exist for this nonconvex problem. Among them, Bouligand stationarity is the strongest necessary condition for local optimality. Only a handful of algorithms generate a sequence in the variety whose accumulation points are provably Bouligand stationary. Among them, the most parsimonious with (truncated) singular value decompositions (SVDs) or eigenvalue decompositions can still require a truncated SVD of a matrix whose rank can be as large as $\min\{m, n\}-r+1$ if the gradient does not have low rank, which is computationally prohibitive in the typical case where $r \ll \min\{m, n\}$. This paper proposes a first-order algorithm that generates a sequence in the variety whose accumulation points are Bouligand stationary while requiring SVDs of matrices whose smaller dimension is always at most $r$. A standard measure of Bouligand stationarity converges to zero along the bounded subsequences at a rate at least $O(1/\sqrt{i+1})$, where $i$ is the iteration counter. Furthermore, a rank-increasing scheme based on the proposed algorithm is presented, which can be of interest if the parameter $r$ is potentially overestimated.

This paper presents a new technique for norm-convergent dynamic mode decomposition of deterministic systems. The developed method utilizes recent results on singular dynamic mode decomposition where it is shown that by appropriate selection of domain and range Hilbert spaces, the Liouville operator (also known as the Koopman generator) can be made to be compact. In this paper, it is shown that by selecting appropriate collections of finite basis functions in the domain and the range, a novel finite-rank representation of the Liouville operator may be obtained. It is also shown that the model resulting from dynamic mode decomposition of the finite-rank representation is closely related to regularized regression using the so-called occupation kernels as basis functions.

This paper deals with the black-box optimization problem. In this setup, we do not have access to the gradient of the objective function, therefore, we need to estimate it somehow. We propose a new type of approximation JAGUAR, that memorizes information from previous iterations and requires $\mathcal{O}(1)$ oracle calls. We implement this approximation in the Frank-Wolfe and Gradient Descent algorithms and prove the convergence of these methods with different types of zero-order oracle. Our theoretical analysis covers scenarios of non-convex, convex and PL-condition cases. Also in this paper, we consider the stochastic minimization problem on the set $Q$ with noise in the zero-order oracle; this setup is quite unpopular in the literature, but we prove that the JAGUAR approximation is robust not only in deterministic minimization problems, but also in the stochastic case. We perform experiments to compare our gradient estimator with those already known in the literature and confirm the dominance of our methods., Comment: 29 pages, 5 algorithms, 3 figures, 1 table

In this paper, motion planning for a vehicle moving on a unit sphere with unit speed is considered, wherein the desired terminal location is fixed, but the terminal orientation is free. The motion of the vehicle is modeled to be constrained by a maximum geodesic curvature $U_{max},$ which controls the rate of change of heading of the vehicle such that the maximum heading change occurs when the vehicle travels on a tight circular arc of radius $r = \frac{1}{\sqrt{1 + U_{max}^2}}$. Using Pontryagin's Minimum Principle, the main result of this paper shows that for $r \leq \frac{1}{2}$, the optimal path connecting a given initial configuration and a final location on the sphere belongs to a set of at most seven paths. The candidate paths are of type $CG, CC,$ and degenerate paths of the same, where $C \in \{L, R\}$ denotes a tight left or right turn, respectively, and $G$ denotes a great circular arc., Comment: \c{opyright} 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

This paper focuses on designing completely decentralized load frequency control (LFC) for multi-area power systems to achieve global optimized performance. To this end, a new concept of integrated design is introduced for designing the decentralized LFC observers and controllers simultaneously off-line, by taking into account of the interactions between areas and the bidirectional effects between the local observer and controller in each area. The integrated design in this paper is realized via $H_\infty$ optimization with a single-step linear matrix inequality (LMI) formulation. The LMI regional eigenvalue assignment technique is further incorporated with $H_\infty$ optimization to improve the closed-loop system transient performance. A three-area power system is simulated to validate the superiority of the proposed integrated design over the conventional decentralized designs., Comment: 23 pages, 8 figures

This paper is mainly concerned with the observability inequalities for heat equations with time-dependent Lipschtiz potentials. The observability inequality for heat equations asserts that the total energy of a solution is bounded above by the energy localized in a subdomain with an observability constant. For a bounded measurable potential $V = V(x,t)$, the factor in the observability constant arising from the Carleman estimate is best known to be $\exp(C\|V\|_{\infty}^{2/3})$ (even for time-independent potentials). In this paper, we show that, for Lipschtiz potentials, this factor can be replaced by $\exp(C(\|\nabla V\|_{\infty}^{1/2} +\|\partial_tV\|_{\infty}^{1/3} ))$, which improves the previous bound $\exp(C\|V\|_{\infty}^{2/3})$ in some typical scenarios. As a consequence, with such a Lipschitz potential, we obtain a quantitative regular control in a null controllability problem. In addition, for the one-dimensional heat equation with some time-independent bounded measurable potential $V = V(x)$, we obtain the optimal observability constant., Comment: 34 pages

The performance of mini-batch stochastic gradient descent (SGD) strongly depends on setting the batch size and learning rate to minimize the empirical loss in training the deep neural network. In this paper, we present theoretical analyses of mini-batch SGD with four schedulers: (i) constant batch size and decaying learning rate scheduler, (ii) increasing batch size and decaying learning rate scheduler, (iii) increasing batch size and increasing learning rate scheduler, and (iv) increasing batch size and warm-up decaying learning rate scheduler. We show that mini-batch SGD using scheduler (i) does not always minimize the expectation of the full gradient norm of the empirical loss, whereas it does using any of schedulers (ii), (iii), and (iv). Furthermore, schedulers (iii) and (iv) accelerate mini-batch SGD. The paper also provides numerical results of supporting analyses showing that using scheduler (iii) or (iv) minimizes the full gradient norm of the empirical loss faster than using scheduler (i) or (ii)., Comment: 23 pages, 5 figures

As the global focus on combating environmental pollution intensifies, the transition to sustainable energy sources, particularly in the form of electric vehicles (EVs), has become paramount. This paper addresses the pressing need for Smart Charging for EVs by developing a comprehensive mathematical model aimed at optimizing charging station management. The model aims to efficiently allocate the power from charging sockets to EVs, prioritizing cost minimization and avoiding energy waste. Computational simulations demonstrate the efficacy of the mathematical optimization model, which can unleash its full potential when the number of EVs at the charging station is high., Comment: Paper submitted and accepted to ESCC 2024 - "11th International Conference on Energy, Sustainability and Climate Crisis August 26 - 30, 2024, Corfu, Greece"

In this paper, the study of nonsmooth optimal control problems (P) involving a controlled sweeping process with three main characteristics is launched. First, the sweeping sets C(t) are nonsmooth, unbounded, time-dependent, uniformly prox-regular, and satisfy minimal assumptions. Second, the sweeping process is coupled with a controlled differential equation. Third, joint-state endpoints constraint set S, including periodic conditions, is present. The existence and uniqueness of a Lipschitz solution for our dynamic is established, the existence of an optimal solution for our general form of optimal control is obtained, and the full form of the nonsmooth Pontryagin maximum principle for strong local minimizers in (P) is derived under minimal hypotheses. One of the novelties of this paper is the idea to work with a well-constructed problem corresponding to truncated sweeping sets and joint endpoint constraints that shares the same strong local minimizer as (P) and for which the exponential-penalty approximation technique can be developed using only the assumptions on (P).

The paper studies a large-scale order fulfillment problem for a leading e-commerce company in the United States. The challenge involves selecting fulfillment centers and shipping carriers with observational data only to efficiently process orders from a vast network of physical stores and warehouses. The company's current practice relies on heuristic rules that choose the cheapest fulfillment and shipping options for each unit, without considering opportunities for batching items or the reliability of carriers in meeting expected delivery dates. The paper develops a data-driven Contextual Stochastic Optimization (CSO) framework that integrates distributional forecasts of delivery time deviations with stochastic and robust order fulfillment optimization models. The framework optimizes the selection of fulfillment centers and carriers, accounting for item consolidation and delivery time uncertainty. Validated on a real-world data set containing tens of thousands of products, each with hundreds of fulfillment options, the proposed CSO framework significantly enhances the accuracy of meeting customer-expected delivery dates compared to current practices. It provides a flexible balance between reducing fulfillment costs and managing delivery time deviation risks, emphasizing the importance of contextual information and distributional forecasts in order fulfillment. This is the first paper that studies the omnichannel multi-courier order fulfillment problem with delivery time uncertainty through the lens of contextual optimization, fusing machine learning and optimization.

The paper addresses a problem of sequential bilateral bargaining with incomplete information. We proposed a decision model that helps agents to successfully bargain by performing indirect negotiation and learning the opponent's model. Methodologically the paper casts heuristically-motivated bargaining of a self-interested independent player into a framework of Bayesian learning and Markov decision processes. The special form of the reward implicitly motivates the players to negotiate indirectly, via closed-loop interaction. We illustrate the approach by applying our model to the Nash demand game, which is an abstract model of bargaining. The results indicate that the established negotiation: i) leads to coordinating players' actions; ii) results in maximising success rate of the game and iii) brings more individual profit to the players., Comment: Appears in IEEE Access

This paper proposes an approach for controlling surgical robotic systems, while complying with the Remote Center of Motion (RCM) constraint in Robot-Assisted Minimally Invasive Surgery (RA-MIS). In this approach, the RCM-constraint is upheld algorithmically, providing flexibility in the positioning of the insertion point and enabling compatibility with a wide range of general-purpose robots. The paper further investigates the impact of the tool's insertion ratio on the RCM-error, and introduces a manipulability index of the robot which considers the RCM-error that it is used to find a starting configuration. To accurately evaluate the proposed method's trajectory tracking within an RCM-constrained environment, an electromagnetic tracking system is employed. The results demonstrate the effectiveness of the proposed method in addressing the RCM constraint problem in RA-MIS., Comment: 6 pages, 7 figures. Published in the 21st International Conference on Advanced Robotics (ICAR 2023)

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node's local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through $3$-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only $3$-bits). Our proposed algorithm respects the network's bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm's operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm's unique characteristics., Comment: arXiv admin note: text overlap with arXiv:2309.04588

In this paper, we consider a class of generalized difference-of-convex functions (DC) programming, whose objective is the difference of two convex (not necessarily smooth) functions plus a decomposable (possibly nonconvex) function with Lipschitz gradient. By employing the Fenchel-Young inequality and Moreau decomposition theorem, we introduce an inertial Bregman proximal DC algorithm to solve the problem under consideration. Our algorithmic framework is able to fully exploit the decomposable structure of the generalized DC programming such that each subproblem of the algorithm is enough easy in many cases. Theoretically, we show that the sequence generated by the proposed algorithm globally converges to a critical point under the Kurdyka-{\L}ojasiewicz condition. A series of numerical results demonstrate that our algorithm runs efficiently on matrix and tensor completion problems., Comment: This paper has been accepted by Communications in Optimization Theory

In this brief paper, we present a naive aggregation algorithm for a typical learning problem with expert advice setting, in which the task of improving generalization, i.e., model validation, is embedded in the learning process as a sequential decision-making problem. In particular, we consider a class of learning problem of point estimations for modeling high-dimensional nonlinear functions, where a group of experts update their parameter estimates using the discrete-time version of gradient systems, with small additive noise term, guided by the corresponding subsample datasets obtained from the original dataset. Here, our main objective is to provide conditions under which such an algorithm will sequentially determine a set of mixing distribution strategies used for aggregating the experts' estimates that ultimately leading to an optimal parameter estimate, i.e., as a consensus solution for all experts, which is better than any individual expert's estimate in terms of improved generalization or learning performances. Finally, as part of this work, we present some numerical results for a typical case of nonlinear regression problem., Comment: Brief paper, with 7 pages, 1 figure

Evaluating performance across optimization algorithms on many problems presents a complex challenge due to the diversity of numerical scales involved. Traditional data processing methods, such as hypothesis testing and Bayesian inference, often employ ranking-based methods to normalize performance values across these varying scales. However, a significant issue emerges with this ranking-based approach: the introduction of new algorithms can potentially disrupt the original rankings. This paper extensively explores the problem, making a compelling case to underscore the issue and conducting a thorough analysis of its root causes. These efforts pave the way for a comprehensive examination of potential solutions. Building on this research, this paper introduces a new mathematical model called "absolute ranking" and a sampling-based computational method. These contributions come with practical implementation recommendations, aimed at providing a more robust framework for addressing the challenge of numerical scale variation in the assessment of performance across multiple algorithms and problems.

This paper studies asynchronous stochastic approximation (SA) algorithms and their application to reinforcement learning in semi-Markov decision processes (SMDPs) with an average-reward criterion. We first extend Borkar and Meyn's stability proof method to accommodate more general noise conditions, leading to broader convergence guarantees for asynchronous SA algorithms. Leveraging these results, we establish the convergence of an asynchronous SA analogue of Schweitzer's classical relative value iteration algorithm, RVI Q-learning, for finite-space, weakly communicating SMDPs. Furthermore, to fully utilize the SA results in this application, we introduce new monotonicity conditions for estimating the optimal reward rate in RVI Q-learning. These conditions substantially expand the previously considered algorithmic framework, and we address them with novel proof arguments in the stability and convergence analysis of RVI Q-learning., Comment: The materials in this paper extend the authors' results from 2023, reported in arXiv:2408.16262 and arXiv:2312.15091. This paper incorporates and subsumes the results of arXiv:2312.15091 and serves as Part II of arXiv:2408.16262

The rapid increase in resident space objects, including satellites and orbital debris, threatens the safety and sustainability of space missions. This paper explores orbital debris remediation using laser ablation with a network of collaborative space-based lasers. A novel delta-v vector analysis framework quantifies the effects of multiple simultaneous laser-to-debris (L2D) engagements by leveraging a vector composition of imparted delta-v vectors. The paper introduces the Concurrent Location-Scheduling Problem (CLSP), which optimizes the placement of laser platforms and schedules L2D engagements to maximize debris remediation capacity. Due to the computational complexity of CLSP, it is decomposed into two sequential subproblems: (1) optimal laser platform locations are determined using the Maximal Covering Location Problem, and (2) a novel integer linear programming-based approach schedules L2D engagements within the network configuration to maximize remediation capacity. Computational experiments are conducted to evaluate the proposed framework's effectiveness under various mission scenarios, demonstrating key network functions such as collaborative nudging, deorbiting, and just-in-time collision avoidance. A cost-benefit analysis further explores how varying the number and distribution of laser platforms affects debris remediation capacity, providing insights into optimizing the performance of space-based laser networks., Comment: 41 pages, 13 figures, submitted to the Journal of Spacecraft and Rockets

This paper investigates a zero-sum stochastic linear quadratic (SLQ, for short) differential games with Markovian jumps. Both open-loop and closed-loop solvabilities are studied by employing a new "decomposition method", which can decompose the open-loop and closed-loop solvability problems of zero-sum SLQ differential game into two coupled SLQ control problems for solving. Moreover, we construct the open-loop saddle point and its closed-loop representation under the uniform convexity-concavity condition based on the solution of a system of constrained coupled differential Riccati equations (CDREs, for short), whose solvability is also provided by adopting the dimension extension technique and the continuation method. At the end of this paper, we provide a concrete example and give its open-loop saddle based on the obtained results.

The Deffuant-Weisbuch (DW) model is a well-known bounded-confidence opinion dynamics that has attracted wide interest. Although the heterogeneous DW model has been studied by simulations over $20$ years, its convergence proof is open. Our previous paper \cite{GC-WS-WM-FB:20} solves the problem for the case of uniform weighting factors greater than or equal to $1/2$, but the general case remains unresolved. This paper considers the DW model with heterogeneous confidence bounds and heterogeneous (unconstrained) weighting factors and shows that, with probability one, the opinion of each agent converges to a fixed vector. In other words, this paper resolves the convergence conjecture for the heterogeneous DW model. Our analysis also clarifies how the convergence speed may be arbitrarily slow under certain parameter conditions.

This paper investigates a distributed optimization problem under edge agreements, where each agent in the network is also subject to local convex constraints. Generalized from the concept of consensus, a group of edge agreements represents the constraints defined for neighboring agents, with each pair of neighboring agents required to satisfy one edge agreement constraint. Edge agreements are defined locally to allow more flexibility than a global consensus, enabling heterogeneous coordination within the network. This paper proposes a discrete-time algorithm to solve such problems, providing a theoretical analysis to prove its convergence. Additionally, this paper illustrates the connection between the theory of distributed optimization under edge agreements and distributed model predictive control through a distributed battery network energy management problem. This approach enables a new perspective to formulate and solve network control and optimization problems.

Renewable prosumers face the complex challenge of balancing self-sufficiency with seamless grid and market integration. This paper introduces a novel prosumers network framework aimed at mitigating peak loads in Iran, particularly under the uncertainties inherent in renewable energy generation and demand. A cost-oriented integrated prediction and optimization approach is proposed, empowering prosumers to make informed decisions within a distributed contextual stochastic optimization (DCSO) framework. The problem is formulated as a bi-level two-stage multi-time scale optimization to determine optimal operation and interaction strategies under various scenarios, considering flexible resources. To facilitate grid integration, a novel consensus-based contextual information sharing mechanism is proposed. This approach enables coordinated collective behaviors and leverages contextual data more effectively. The overall problem is recast as a mixed-integer linear program (MILP) by incorporating optimality conditions and linearizing complementarity constraints. Additionally, a distributed algorithm using the consensus alternating direction method of multipliers (ADMM) is presented for computational tractability and privacy preservation. Numerical results highlights that integrating prediction with optimization and implementing a contextual information-sharing network among prosumers significantly reduces peak loads as well as total costs., Comment: 10 pages, 26 figure, journal paper

This paper investigates a zero-sum stochastic linear-quadratic (SLQ, for short) Stackelberg differential game problem, where the coefficients of the state equation and the weighting matrices in the performance functional are regulated by a Markov chain. By utilizing the findings in \citet{Zhang.X.2021_ILQM}, we directly present the feedback representation to the rational reaction of the follower. For the leader's problem, we derive the optimality system through the variational method and study its unique solvability from the Hilbert space point of view. We construct the explicit optimal control for the leader based on the solution to coupled differential Riccati equations (CDREs, for short) and obtain the solvability of CDREs under the one-dimensional framework. Finally, we provide two concrete examples to illustrate the results developed in this paper.