Clusters die hard: Time-correlated excitation in the Hamiltonian mean field model

The Hamiltonian mean field (HMF) model has a low-energy phase where N particles are trapped inside a cluster. Here, we investigate some properties of the trapping/untrapping mechanism of a single particle into/outside the cluster. Since the single particle dynamics of the HMF model resembles the one of a simple pendulum, each particle can be identified as a high-energy particle (HEP) or a low-energy particle (LEP), depending on whether its energy is above or below the separatrix energy. We then define the trapping ratio as the ratio of the number of LEP to the total number of particles and the “fully-clustered” and “excited” dynamical states as having either no HEP or at least one HEP. We analytically compute the phase-space average of the trapping ratio by using the Boltzmann–Gibbs stable stationary solution of the Vlasov equation associated with the N → ∞ limit of the HMF model. The same quantity, obtained numerically as a time average, is shown to be in very good agreement with the analytical calculation. Another important feature of the dynamical behavior of the system is that the dynamical state changes transitionally: the “fully-clustered” and “excited” states appear in turn. We find that the distribution of the lifetime of the “fully-clustered” state obeys a power law. This means that clusters die hard, and that the excitation of a particle from the cluster is not a Poisson process and might be controlled by some type of collective motion with long memory. Such behavior should not be specific of the HMF model and appear also in systems where itinerancy among different “quasi-stationary” states has been observed. It is also possible that it could mimick the behavior of transient motion in molecular clusters or some observed deterministic features of chemical reactions.     

Numerical analysis of generalised max-plus eigenvalue problems

This paper is concerned with the deterministic discrete-time infinite horizon optimisation problem on a compact metric space with an average cost criterion involving two functions K (the “cost”) and T (the “time”). Firstly, we collect the different characterisations of the value λ in terms of generalised max-plus eigenvalue problem and in terms of linear programming. Secondly, we prove an error bound on λ when the space is discretised.     

Optimal fleet replacement: A case study on a Spanish urban transport fleet

Optimizing the average annual cost of a bus fleet has become an increasing concern in transport companies management around the world. Nowadays, there are many tools available to assist managerial decisions, and one of the most used is the cost analysis of the life cycle of an asset, known as “life cycle cost”. Characterized by performing deterministic analysis of the situation, it allows the administration to evaluate the process of fleet replacement but is limited by not contemplating certain intrinsic variations related to vehicles and for disregarding variables related to exigencies of fleet use. The main purpose of this study is to develop a combined model of support to asset management based in the association of the life cycle cost tool and the mathematical model of Monte Carlo simulation, by performing a stochastic analysis considering both age and average annual mileage for optimum vehicle replacement. The utilized method was applied in a Spanish urban transport fleet, and the results indicate that the use of the stochastic model was more effective than the use of the deterministic model.     

A remark on multiobjective stochastic optimization via strongly convex functions

Many economic and financial applications lead (from the mathematical point of view) to deterministic optimization problems depending on a probability measure. These problems can be static (one stage), dynamic with finite (multistage) or infinite horizon, single objective or multiobjective. We focus on one-stage case in multiobjective setting. Evidently, well known results from the deterministic optimization theory can be employed in the case when the “underlying” probability measure is completely known. The assumption of a complete knowledge of the probability measure is fulfilled very seldom. Consequently, we have mostly to analyze the mathematical models on the data base to obtain a stochastic estimate of the corresponding “theoretical” characteristics. However, the investigation of these estimates has been done mostly in one-objective case. In this paper we focus on the investigation of the relationship between “characteristics” obtained on the base of complete knowledge of the probability measure and estimates obtained on the (above mentioned) data base, mostly in the multiobjective case. Consequently we obtain also the relationship between analysis (based on the data) of the economic process characteristics and “real” economic process. To this end the results of the deterministic multiobjective optimization theory and the results obtained for stochastic one objective problems will be employed.     

Bayesian nonparametrie inference and monte carlo optimization

As global or combinatorial optimization problems are not effectively tractable by means of deterministic techniques, Monte Carlo methods are used in practice for obtaining ”good“ approximations to the optimum. In order to test the accuracy achieved after a sample of finite size, the Bayesian nonparametric approach is proposed as a suitable context, and the theoretical as well as computational implications of prior distributions in the class of neutral to the right distributions are examined. The feasibility of the approach relatively to particular Monte Carlo procedures is finally illustrated both for the global optimization problem and the {0 - 1} programming problem.     

A note on inventory policies for products with residual-life-dependent demand

We study inventory ordering policies for products that attract demand at a decreasing rate as they approach the end of their usable lifetime, for example, perishable items nearing expiration. We consider the “product freshness’’, or equivalently, the time until expiration (“residual life”) as a factor influencing the customer demand. In a profit-maximizing framework, we build on the Economic Order Quantity (EOQ) replenishment model and formulate the inventory ordering problem using a deterministic demand function that is concave decreasing in the the age of the product. We provide analytical results on the optimal ordering policy, including an explicit characterization of the decisions in the linear-demand case, and we develop an easy-to-implement adaptive heuristic policy for the general case. Numerical examples show that the optimal policy generates significant profit gains compared to the traditional cost-based policies and the adaptive heuristic policy performs highly satisfactorily in the tested instances.     

Investigation of a problem of an elastic half space subjected to stochastic temperature distribution at the boundary

The present work investigates the responses of stochastic type temperature distribution applied at the boundary of an elastic medium in the context of thermoelasticity without energy dissipation. We consider an one dimensional problem of half space and assume that the bounding surface of the half space is traction free and is subjected to two types of time dependent temperature distributions which are of stochastic types. In order to compare the results predicted by stochastic temperature distributions with the results of deterministic type temperature distribution, the stochastic type temperature distributions applied at the boundary are taken in such a way that they reduce to the cases of deterministic types as special cases. Integral transform technique along with stochastic calculus is used to solve the problem. The approximated solutions for physical fields like, stress, temperature, displacement etc. are derived for very small values of time where stochastic type boundary conditions are taken to be of white noise type. The problem is further illustrated with graphical representation of numerical solutions of the problem for a particular case. A detailed comparison of the results of stochastic temperature, displacement and stress distributions inside the half space with the corresponding results of deterministic distributions is presented and special features of the effects of stochastic type boundary conditions are highlighted.     

Stochastic Material and Topology Design

In order to find a robust optimal topology or material design with respect to stochastic variations of the model parameters of a mechanical structure, the basic optimization problem under stochastic uncertainty must be replaced by an appropriate deterministic substitute problem. Starting from the equilibrium equation and the yield/strength conditions, the problem can be formulated as a stochastic (linear) program “with recourse”. Hence, by discretization the design space by finite elements, linearizing the yield conditions, in case of discrete probability distributions the resulting deterministic substitute problems are linear programs with a dual decomposition data structure.     

Scheduling for a processor sharing system with linear slowdown

We consider the problem of scheduling arrivals to a congestion system with a finite number of users having identical deterministic demand sizes. The congestion is of the processor sharing type in the sense that all users in the system at any given time are served simultaneously. However, in contrast to classical processor sharing congestion models, the processing slowdown is proportional to the number of users in the system at any time. That is, the rate of service experienced by all users is linearly decreasing with the number of users. For each user there is an ideal departure time (due date). A centralized scheduling goal is then to select arrival times so as to minimize the total penalty due to deviations from ideal times weighted with sojourn times. Each deviation penalty is assumed quadratic, or more generally convex. But due to the dynamics of the system, the scheduling objective function is non-convex. Specifically, the system objective function is a non-smooth piecewise convex function. Nevertheless, we are able to leverage the structure of the problem to derive an algorithm that finds the global optimum in a (large but) finite number of steps, each involving the solution of a constrained convex program. Further, we put forward several heuristics. The first is the traversal of neighbouring constrained convex programming problems, that is guaranteed to reach a local minimum of the centralized problem. This is a form of a “local search”, where we use the problem structure in a novel manner. The second is a one-coordinate “global search”, used in coordinate pivot iteration. We then merge these two heuristics into a unified “local–global” heuristic, and numerically illustrate the effectiveness of this heuristic.     

Thermofluiddynamics in liquid media during biotechnological processes under high pressure

In bio/ chemical engineering high pressure (HP) processing up to several GPa represents a novel technique to selectively influence (bio–)chemical reactions resulting from temperature and pressure sensitivities differing between the occuring reactions. Therefore, thermofluiddynamical effects leading to inhomogeneous process conditions (pressure, temperature) show an important influence on the process results. To predict and improve mass, momentum and energy transport within the HP vessel and the impact on desired chemical reactions spatiotemporal numerical simulations are a useful tool in process analysis and design. The present contribution deals with mathematical modelling of the enzyme inactivation in HP processing of liquid biotechnological media. For the first time a transport equation for the scalar quantity “enzyme activity” is deduced. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A deterministic‐control‐based approach motion by curvature

The level‐set formulation of motion by mean curvature is a degenerate parabolic equation. We show that its solution can be interpreted as the value function of a deterministic two‐person game. More precisely, we give a family of discrete‐time, two‐person games whose value functions converge in the continuous‐time limit to the solution of the motion‐by‐curvature PDE. For a convex domain, the boundary's “first arrival time” solves a degenerate elliptic equation; this corresponds, in our game‐theoretic setting, to a minimum‐exit‐time problem. For a nonconvex domain the two‐person game still makes sense; we draw a connection between its minimum exit time and the evolution of curves with velocity equal to the “positive part of the curvature.” These results are unexpected, because the value function of a deterministic control problem is normally the solution of a first‐order Hamilton‐Jacobi equation. Our situation is different because the usual first‐order calculation is singular. © 2005 Wiley Periodicals, Inc.     

A random bit-flipping method for seeking agreement

Recently Papadimitriou has proposed a randomized “bit-flipping” method for solving the 2-satisfiability problem, and the author has proposed a randomized recoloring method which, given a 3-colorable graph, finds a 2-coloring of the vertices so that no triangle is monochromatic. Both methods involve finding a “bad” configuration (unsatisfied clause, monochromatic triangle) and randomly changing one of the bits involved. In this paper we see how these problems and methods fit naturally in a more general geometrical context in which we seek a vector which “agrees” with a given collection of vectors; and we propose a simple “bit-flipping” method for the more general problem, which extends the solution methods for the two problems mentioned above. Further, we consider deterministic methods to handle such problems, and in particular we see how to solve the above “triangle problem” for 3-colorable graphs deterministically in polynomial time. © 1996 John Wiley & Sons, Inc.     

Computation of large shear deformations of a thermoplastic material

A specialized finite difference method with grid refinement and variable time steps is created to approximate the deformation velocity and the temperature in a simple model of the shearing of a thermoplastic material. A specific problem where the solution exhibits “blowup” in the adiabatic case is considered. The numerical method retains this property and is used to study the shape of the “blowup” function. The code is then used to investigate the solution in the closely related case where thermal conduction is included with a small conductivity coefficient. The computations indicate that the solution does not “blowup” in the nonadiabatic case. © 1996 John Wiley & Sons, Inc.     

Nonlocal Tug‐of‐War and the Infinity Fractional Laplacian

Motivated by the “tug‐of‐war” game studied by Peres et al. in 2009, we consider a nonlocal version of the game that goes as follows: at every step two players pick, respectively, a direction and then, instead of flipping a coin in order to decide which direction to choose and then moving a fixed amount ϵ > 0 (as is done in the classical case), it is an s‐stable Levy process that chooses at the same time both the direction and the distance to travel. Starting from this game, we heuristically derive a deterministic nonlocal integrodifferential equation that we call the “infinity fractional Laplacian.” We study existence, uniqueness, and regularity, both for the Dirichlet problem and for a double‐obstacle problem, both problems having a natural interpretation as tug‐of‐war games. © 2011 Wiley Periodicals, Inc.     

Path comparisons for a priori and time-adaptive decisions in stochastic,time-varying networks

Travel times in congested transportation networks are time-varying quantities that can at best be known a priori probabilistically. In such networks, the arc weights (travel times) are represented by random variables whose probability distribution functions vary with time. These networks are referred to herein as stochastic, time-varying, or STV, networks. The determination of “least time” routes in STV networks is more difficult than in deterministic networks, in part because, for a given departure time, more than one path may exist between an origin and destination, each with a positive probability of having the least travel time. In this paper, measures for comparing time-varying, random path travel times over a time period are given for both a priori optimization and time-adaptive choices (where a driver may react to revealed arrival times at intermediate nodes). The resulting measures are central to the development of methodologies for determining “optimal” paths in STV networks.     

About a generalized model of lymphoma

In this work we introduce and analyze a generalized model of precursor T-lymphoblastic lymphoma as a competition between two clonotypes of naïve T-cells, one “normal” and one tumorous. It is modeled as a continuous-time bivariate Markov process. Using an expansion of the master equation a deterministic approximation and the Fokker–Planck equation are derived. For a deterministic model we show existence and uniqueness of global solutions and positive invariance of the first quadrant of the phase space. Stability analysis of the model is performed, finding conditions guaranteeing existence of a unique, positive, steady state, which is proved to be globally stable. It is shown that expectations of fluctuations for both clonotypes tend to zero for large time. We also present numerical simulations in which two types of behavior of solutions are observed: either both clonotypes survive in the repertoire or the “normal” clonotype becomes extinct. Comparing this result with the rules of maintenance of naïve T-cell repertoire, which say that clonotypes with more specific set of receptors have longer life-span, it seems that “normal” clonotype follows them, whereas the tumorous one violates them and tends to the maximum possible expansion. The model supports the hypothesis of mutated precursor cells as an origin of cancer.     

Problems Involving Infrequent but Significant Contingencies

Problematic situations often arise in which it is required to provide a solution which will tend to avoid events, which, if they occur, would be very costly, or, if not directly costable, they would be highly undesirable. Although direct approaches to this sort of problem exist, they can be unmanageable. If, however, we take as a posit, that the frequency with which the undesirable events arise, in the optimum solution, is small, considerable simplifications can be made. Naturally we need to check the posit once the solution has been found. This paper considers three applications of this principle, viz. determination of how many chargers are needed for steel furnaces, where the undesirable event is “a furnace waits for service”; determination of the number of emergency beds to set aside in a hospital unit, where the undesirable event is “an emergency case arrives and no bed is immediately available”; determination of an inventory reorder rule where the undesirable event is “stock run-out”. The general principle is formalized.     

Inventory control under speculation: Myopic heuristics and exact procedures

We consider a periodic review inventory problem in which the purchasing cost exhibits a noticeable increase (deterministic or stochastic) in the second period and remains at the higher value for the remainder of the problem. (This simplification clarifies the nature of the myopic heuristic, but is not necessary for use of the heuristic in practice.) This results in a strategy that holds inventories due to speculation. We develop solution procedures to find the optimal inventory levels for both stationary and non-stationary demands. We establish that the problem with stochastic speculation behaves exactly like a problem with deterministic speculation with the same mean increase in price. We propose, based on the case of deterministic demands, simple myopic heuristics and study their effectiveness. We observe that these heuristics perform very well for exponential demands. However, for the case of uniform demands these heuristics are most effective when the increase in price is large compared to the holding cost.     

Automatic Optimal Regularization for Ill-Posed Problems with Stochastical Noise without Imposing Smoothness Assumptions

We consider the compact operator A : 𝒳 → 𝒴 for the separable Hilbert spaces 𝒳 and 𝒴. The problem Ax = y is called ill-posed when the singular values s_k , k = 1, 2, … of the operator A tend to zero. Classically one assumes that y is biased with “deterministic noise”; we will also consider “stochastic noise” where the noise element is a weak Gaussian random variable. There classical stopping rules (e.g. Morozov) do not work. We will show that both for the “deterministic noise” case as well for the “stochastical noise” case we can regularize in an (asymptotically almost) optimal way without knowledge of the smoothness of the solution using Lepskij's method. Furthermore the method also works for estimated error levels and error behavior. So we can assure regularization which is just dependent on measurements obtainable in reality, e.g. satellite problems. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

The impact of wind uncertainty on the strategic valuation of distributed electricity storage

The intermittent nature of wind energy generation has introduced a new degree of uncertainty to the tactical planning of energy systems. Short-term energy balancing decisions are no longer (fully) known, and it is this lack of knowledge that causes the need for strategic thinking. But despite this observation, strategic models are rarely set in an uncertain environment. And even if they are, the approach used is often inappropriate, based on some variant of scenario analysis—what-if analysis. In this paper we develop a deterministic strategic model for the valuation of electricity storage (a battery), and ask: “Though leaving out wind speed uncertainty clearly is a simplification, does it really matter for the valuation of storage?”. We answer this question by formulating a stochastic programming model, and compare its valuation to that of its deterministic counterpart. Both models capture the arbitrage value of storage, but only the stochastic model captures the battery value stemming from wind speed uncertainty. Is the difference important? The model is tested on a case from Lancaster University’s campus energy system where a wind turbine is installed. From our analysis, we conclude that considering wind speed uncertainty can increase the estimated value of storage with up to 50 % relative to a deterministic estimate. However, we also observe cases where wind speed uncertainty is insignificant for storage valuation.