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Coordinating the mobile production fleet in underground mines becomes increasingly important as the machines are more and more automated. We present a scheduling approach that applies to several of the most important production methods used in underground mines. Our algorithm combines constraint programming with a large neighborhood search strategy that dynamically adjusts the neighborhood size. The resulting algorithm is complete and able to rapidly improve constructed schedules in practice. In addition, it has important benefits when it comes to the acceptance of the approach in real-life operations. Our approach is evaluated on public and private industrial problem instances representing different mines and production methods. We find significant improvements over the current industrial practice., Part of proceedings ISBN: 978-3-030-78229-0QC 20220413

Motivated by large-scale optimization problems arising in the context of machine learning, there have been several advances in the study of asynchronous parallel and distributed optimization methods during the past decade. Asynchronous methods do not require all processors to maintain a consistent view of the optimization variables. Consequently, they generally can make more efficient use of computational resources than synchronous methods, and they are not sensitive to issues like stragglers (i.e., slow nodes) and unreliable communication links. Mathematical modeling of asynchronous methods involves proper accounting of information delays, which makes their analysis challenging. This article reviews recent developments in the design and analysis of asynchronous optimization methods, covering both centralized methods, where all processors update a master copy of the optimization variables, and decentralized methods, where each processor maintains a local copy of the variables. The analysis provides insights into how the degree of asynchrony impacts convergence rates, especially in stochastic optimization methods., QC 20210303

This study shows how to design an active suspension system for guidance of a rail vehicle wheelset in curve. The main focus of the study is on designing the controller and afterwards studying its effect on the wheel wear behaviour. The controller is designed based on the closed-loop transfer function shaping method and (Formula presented.) control strategy. The study discusses designing of the controller for both nominal and uncertain plants and considers both stability and performance. The designed controllers in Simulink are then applied to the vehicle model in Simpack to study the wheel wear behaviour in curve. The vehicle type selected for this study is a two-axle rail vehicle. This is because this type of vehicle is known to have very poor curving performance and high wheel wear. On the other hand, the relative simpler structure of this type of vehicle compared to bogie vehicles make it a more economic choice. Hence, equipping this type of vehicle with the active wheelset steering is believed to show high enough benefit to cost ratio to remain attractive to rail vehicle manufacturers and operators., QC 20201118

We analyze stability properties of monotone nonlinear systems via max-separable Lyapunov functions, motivated by the following observations: first, recent results have shown that asymptotic stability of a monotone nonlinear system implies the existence of a max-separable Lyapunov function on a compact set; second, for monotone linear systems, asymptotic stability implies the stronger properties of D-stability and insensitivity to time delays. This paper establishes that for monotone nonlinear systems, equivalence holds between asymptotic stability, the existence of a max-separable Lyapunov function, D-stability, and insensitivity to bounded and unbounded time-varying delays. In particular, a new and general notion of D-stability for monotone nonlinear systems is discussed, and a set of necessary and sufficient conditions for delay-independent stability are derived. Examples show how the results extend the state of the art., QC 20180403

The practical performance of stochastic gradient descent on large-scale machine learning tasks is often much better than what current theoretical tools can guarantee. This indicates that there is an inherent structure in these problems that could be exploited to strengthen the analysis. In this paper, we argue that data sparsity is such a property. We derive explicit expressions for how data sparsity affects the range of admissible step-sizes and the convergence factors of mini-batch gradient descent. Our theoretical results are validated by solving least-squares support vector machine problems on both synthetic and real-life data sets. The experimental results demonstrate improved performance of our update rules compared to the traditional mini-batch gradient descent algorithm., QC 20180306

Mini-batch optimization has proven to be a powerful paradigm for large-scale learning. However, the state of the art parallel mini–batch algorithms assume synchronous operation or cyclic update orders. When worker nodes are heterogeneous (due to different computational capabilities or different communication delays), synchronous and cyclic operations are inefficient since they will leave workers idle waiting for the slower nodes to complete their computations. In this paper, we propose an asynchronous mini-batch algorithm for regularized stochastic optimization problems with smooth loss functions that eliminates idle waiting and allows workers to run at their maximal update rates. We show that by suitably choosing the step-size values, the algorithm achieves a rate of the order O(1/ √ T) for general convex regularization functions, and the rate O(1/T ) for strongly convex regularization functions, where T is the number of iterations. In both cases, the impact of asynchrony on the convergence rate of our algorithm is asymptotically negligible, and a nearlinear speedup in the number of workers can be expected. Theoretical results are confirmed in real implementations on a distributed computing infrastructure., QC 20160311

Stability properties of monotone nonlinear systems with max-separable Lyapunov functions are considered in this paper, motivated by the following observations. First, recent results have shown that such Lyapunov functions are guaranteed to exist for asymptotically stable monotone systems on compact sets. Second, it is well-known that, for monotone linear systems, asymptotic stability implies the stronger properties of D-stability and robustness with respect to time-delays. This paper shows that similar properties hold for monotone nonlinear systems that admit max-separable Lyapunov functions. In particular, a notion of D-stability for monotone nonlinear systems and delay-independent stability will be discussed. The theoretical results are illustrated by means of examples., QC 20170615

Multi-agent optimization problems arise in a wide variety of networked systems, and are often required to be solved in an asynchronous and uncoordinated way. However, existing asynchronous algorithms for constrained multi-agent optimization do not have guaranteed convergence rates and, thus, lack performance guarantees in on-line applications. This paper addresses this shortcoming by developing randomized coordinate descent algorithms for solving the dual of a class of constrained multi-agent optimization problems. We show that the algorithms can be implemented asynchronously and distributively in multi-agent networks. Moreover, without relying on the standard assumption of boundedness of the dual optimal set, the proposed dual coordinate descent algorithms achieve sublinear convergence rates of both its primal and dual iterates in expectation. The competitive performance is demonstrated numerically on a constrained optimal rendezvous problem., QC 20160317

This paper establishes global convergence and provides global bounds of the rate of convergence for the Heavy-ball method for convex optimization. When the objective function has Lipschitz-continuous gradient, we show that the Cesáro average of the iterates converges to the optimum at a rate of O(1/k) where k is the number of iterations. When the objective function is also strongly convex, we prove that the Heavy-ball iterates converge linearly to the unique optimum. Numerical examples validate our theoretical findings., QC 20160314

Recent results in the literature have shown that particular classes of positive systems are insensitive to time-varying delays, giving the impression that the delay-insensitivity property stems from the fact that the system is positive. Nonetheless, it has been lately shown that a linear cone-invariant system is insensitive to time-varying delays, asserting that the property of delay-independence may stem from the fact that the system is cone-invariant rather than positive. In this paper, we provide additional evidence for this claim by analyzing the stability of cone-invariant monotone systems with bounded time-varying delays. We present a set of sufficient conditions for delay independent stability of discrete- and continuous-time cone-invariant monotone systems. For linear cone-invariant systems, we show that thestability conditions we have derived are also necessary., QC 20160314

This paper studies a flexible algorithm for minimizing a sum of component functions, each of which depends on a large number of decision variables. Such formulations appear naturally in “big data” applications, where each function describes the loss estimated using the data available at a specific machine, and the number of features under consideration is huge. In our algorithm, a coordinator updates a global iterate based on delayed partial gradients of the individual objective functions with respect to blocks of coordinates. Delayed incremental gradient and delayed coordinate descent algorithms are obtained as special cases. Under the assumption of strong convexity and block coordinate-wise Lipschitz continuous partial gradients, we show that the algorithm converges linearly to a ball around the optimal value. Contrary to related proposals in the literature, our algorithm is delay-insensitive: it converges for any bounded information delay, and its step-size parameter can be chosen independently of the maximum delay bound., QC 20150511

This paper presents a new incremental gradient algorithm for minimizing the average of a large number of smooth component functions based on delayed partial gradients. Even with a constant step size, which can be chosen independently of the maximum delay bound and the number of objective function components, the expected objective value is guaranteed to converge linearly to within some ball around the optimum. We derive an explicit expression that quantifies how the convergence rate depends on objective function properties and algorithm parameters such as step-size and the maximum delay. An associated upper bound on the asymptotic error reveals the trade-off between convergence speed and residual error. Numerical examples confirm the validity of our results., QC 20150511

This paper presents a unifying convergence result for asynchronous iterations involving pseudo-contractions in the block-maximum norm. Contrary to previous results which only established asymptotic convergence or studied simplified models of asynchronism, our result allows to bound the convergence rates for both partially and totally asynchronous implementations. Several examples are worked out to demonstrate that our theorem recovers and improves on existing results, and that it allows to characterize the solution times for several classes of asynchronous iterations that have not been addressed before., qc 20150223

This paper develops a comprehensive stability analysis framework for general classes of continuous-time power control algorithms under heterogeneous time-varying delays. Our first set of results establish global asymptotic stability of power control laws involving two-sided scalable interference functions, and include earlier work on standard interference functions as a special case. We then consider contractive interference functions and demonstrate that the associated continuous-time power control laws always have unique fixed points which are exponentially stable, even under bounded heterogeneous time-varying delays. For this class of interference functions, we present explicit bounds on the decay rate that allow us to quantify the impact of delays on the convergence time of the algorithm. When interference functions are linear, we also prove that contractivity is necessary and sufficient for exponential stability of continuous-time power control algorithms with time-varying delays. Finally, numerical simulations illustrate the validity of our theoretical results., QC 20140902

While the asymptotic stability of positive linear systems in the presence of bounded time delays has been thoroughly investigated, the theory for nonlinear positive systems is considerably less well-developed. This paper presents a set of conditions for establishing delay-independent stability and bounding the decay rate of a significant class of nonlinear positive systems which includes positive linear systems as a special case. Specifically, when the time delays have a known upper bound, we derive necessary and sufficient conditions for exponential stability of (a) continuous-time positive systems whose vector fields are homogeneous and cooperative, and (b) discrete-time positive systems whose vector fields are homogeneous and order-preserving. We then present explicit expressions that allow us to quantify the impact of delays on the decay rate and show that the best decay rate of positive linear systems that our bounds provide can be found via convex optimization. Finally, we extend the results to general linear systems with time-varying delays., QC 20140714

There are several results on the stability analysisof positive linear systems in the presence of constant or timevaryingdelays. However, most existing results assume thatthe delays are bounded. This paper studies the stability ofdiscrete-time positive linear systems with unbounded delays.We provide a set of easily verifiable necessary and sufficientconditions for delay-independent stability of positive linearsystems subject to a general class of heterogeneous timevaryingdelays. For two particular classes of unbounded delays,explicit expressions that bound the decay rate of the system arepresented. We demonstrate that the best bound on the decayrate that our results can guarantee can be found via convexoptimization. Finally, the validity of the results is demonstratedvia a numerical example., QC 20140103

This paper develops a comprehensive stabilityanalysis framework for continuous-time power control algorithmsin wireless networks under bounded time-varyingcommunication delays. Our first set of results establish globalasymptotic stability of power control laws involving two-sidedscalable interference functions, and include earlier work onstandard interference functions as a special case. We thenconsider contractive interference functions and demonstratethat the associated continuous-time power control laws alwayshave unique fixed points, which are exponentially stable evenin the presence of bounded heterogeneous time-varying delays.For this class of interference functions, we derive an explicitbound on the decay rate that allows us to quantify the impactof delays on the convergence time of the algorithm. Numericalsimulations illustrate our theoretical results., QC 20140103

In this work, a set of conditions are presented for establishing exponential stability and bounds on the convergence rates of both general and positive linear systems with heterogeneous time-varying delays. First, a sufficient condition for delay-independent exponential stability of general linear systems is derived. When the time delays have a known upper bound, we present an explicit expression that bounds the decay rate of the system. We demonstrate that the best decay rate that our bound can guarantee can be easily found via convex optimization techniques. Finally, for positive linear systems, we show that the stability condition that we have developed is also necessary. The validity of the results is demonstrated via numerical examples., QC 20140103

We consider the problem of optimal distributed control with delayed information sharing over chain structures motivated by applications of heavy duty vehicle platooning. We introduce a novel approach to find distributed controllers that take into account communication delays and correlation between the dynamic interconnection. The design decomposes the solution into separate optimization problems. The results show that a tight control is achieved, even in the presence of delays, and behaves well with respect to the imposed disturbances. The computed theoretical cost of the proposed optimal controller is significantly better than the theoretical cost for the centralized control with a global delay and close to the cost for a fully centralized control with full state information at all times., QC 20130313

This study shows how to design an active suspension system for guidance of a rail vehiclewheelset in curve. The main focus of the study is on designing the controller and afterwardsstudying its eect on the wheel wear behavior. The controller is designed based on theclosed-loop transfer function shaping method and H1 control strategy. The study discussesdesigning of the controller for both nominal and uncertain plants and considers both stabilityand performance. The designed controllers in Simulink are then applied to the vehicle modelin Simpack to study the wheel wear behavior in curve. The vehicle type selected for thisstudy is a two-axle rail vehicle. This is because this type of vehicle is known to have verypoor curving performance and high wheel wear. On the other hand, the relative simplerstructure of this type of vehicle compared to bogie vehicles make it a more economic choice.Hence, equipping this type of vehicle with the active wheelset steering is believed to show highenough benet to cost ratio to remain attractive to rail vehicle manufacturers and operators., QC 20170602