Efficient Computing Budget Allocation for Simulation-based Optimization with Stochastic Simulation Time

The dynamics of many systems nowadays follow not only physical laws but also man-made rules. These systems are known as discrete event dynamic systems and their performances can be accurately evaluated only through simulations. Existing studies on simulation-based optimization (SBO) usually assume deterministic simulation time for each replication. However, in many applications such as evacuation, smoke detection, and territory exploration, the simulation time is stochastic due to the randomness in the system behavior. We consider the computing budget allocation for SBO's with stochastic simulation time in this paper, which has not been addressed in existing literatures to the author's best knowledge. We make the following major contribution. The relationship between simulation time and performance estimation accuracy is quantified. It is shown that when the asymptotic performance is of interest only the mean value of individual simulation time matters. Then based on the existing optimal computing budget allocation (OCBA) method for deterministic simulation time we develop OCBA for stochastic simulation time (OCBAS), and show that OCBAS is asymptotically optimal. Numerical experiments are used to discuss the impact of the variance of simulation time, the impact of correlated simulation time and performance estimation, and to demonstrate the performance of OCBAS on a smoke detection problem in wireless sensor network. The numerical results also show that OCBA for deterministic simulation time is robust even when the simulation time is stochastic.Comment: 7 pages, 5 figures, technical repor

Photoproduction of dileptons and photons in p-p collisions at Large Hadron Collider energies

The production of large $p_{T}$ dileptons and photons originating from photoproduction processes in p-p collisions at Large Hadron Collider energies is calculated. The comparisons between the exact treatment results and the ones of the equivalent photon approximation approach are expressed as the $Q^{2}$ (the virtuality of photon) and $p_{T}$ distributions. The method developed by Martin and Ryskin is used for avoiding double counting when the coherent and incoherent contributions are considered simultaneously. The numerical results indicate that, the equivalent photon approximation is only effective in small $Q^{2}$ region and can be used for coherent photoproduction processes with proper choice of $Q^{2}_{\textrm{max}}$ ( the choices $Q^{2}_{\textrm{max}}\sim \hat{s}$ or $\infty$ will cause obvious errors), but can not be used for incoherent photoproduction processes. The exact treatment is needed to deal accurately with the photoproduction of large $p_{T}$ dileptons and photons.Comment: 13 pages, 24 figure

An online learning approach to dynamic pricing for demand response

In this paper, the problem of optimal dynamic pricing for retail electricity with an unknown demand model is considered. Under the day-ahead dynamic pricing (a.k.a. real time pricing) mechanism, a retailer obtains electricity in a twosettlement wholesale market and serves its customers in real time. Without knowledge on the aggregated demand function of its customers, the retailer aims to maximize its retail surplus by sequentially adjusting its price based on the behavior of its customers in the past. An online learning algorithm, referred to as piecewise linear stochastic approximation (PWLSA), is proposed. It is shown that PWLSA achieves the optimal rate of learning defined by the growth rate of cumulative regret. In particular, the regret of PWLSA is shown to grow logarithmically with respect to the learning horizon, and no other on-line learning algorithm can have the growth rate slower than that of PWLSA. Simulation studies are presented using traces of actual day-ahead prices, and PWLSA compares favorably under both static and dynamically changing parameters

Estimating Distances via Received Signal Strength and Connectivity in Wireless Sensor Networks

Distance estimation is vital for localization and many other applications in wireless sensor networks (WSNs). Particularly, it is desirable to implement distance estimation as well as localization without using specific hardware in low-cost WSNs. As such, both the received signal strength (RSS) based approach and the connectivity based approach have gained much attention. The RSS based approach is suitable for estimating short distances, whereas the connectivity based approach obtains relatively good performance for estimating long distances. Considering the complementary features of these two approaches, we propose a fusion method based on the maximum-likelihood estimator (MLE) to estimate the distance between any pair of neighboring nodes in a WSN through efficiently fusing the information from the RSS and local connectivity. Additionally, the method is reported under the practical log-normal shadowing model, and the associated Cramer-Rao lower bound (CRLB) is also derived for performance analysis. Both simulations and experiments based on practical measurements are carried out, and demonstrate that the proposed method outperforms any single approach and approaches to the CRLB as well

Trading Strategy with Stochastic Volatility in a Limit Order Book Market

In this paper, we employ the Heston stochastic volatility model to describe the stock's volatility and apply the model to derive and analyze the optimal trading strategies for dealers in a security market. We also extend our study to option market making for options written on stocks in the presence of stochastic volatility. Mathematically, the problem is formulated as a stochastic optimal control problem and the controlled state process is the dealer's mark-to-market wealth. Dealers in the security market can optimally determine their ask and bid quotes on the underlying stocks or options continuously over time. Their objective is to maximize an expected profit from transactions with a penalty proportional to the variance of cumulative inventory cost

On Optimal Pricing Model for Multiple Dealers in a Competitive Market

In this paper, the optimal pricing strategy in Avellande-Stoikov's for a monopolistic dealer is extended to a general situation where multiple dealers are present in a competitive market. The dealers' trading intensities, their optimal bid and ask prices and therefore their spreads are derived when the dealers are informed the severity of the competition. The effects of various parameters on the bid-ask quotes and profits of the dealers in a competitive market are also discussed. This study gives some insights on the average spread, profit of the dealers in a competitive trading environment

Plasma q-plate for generation and manipulation of intense optical vortices

An optical vortex is a light wave with a twisting wavefront around its propagation axis and null intensity in the beam center. Its unique spatial structure of field lends itself to a broad range of applications, including optical communication, quantum information, superresolution microscopy, and multi-dimensional manipulation of particles. However, accessible intensity of optical vortices have been limited to material ionization threshold. This limitation might be removed by using the plasma medium. Here we propose the design of suitably magnetized plasmas which, functioning as a q-plate, leads to a direct convertion from a high-intensity Gaussian beam into a twisted beam. A circularly polarized laser beam in the plasma accumulates an azimuthal-angle-dependent phase shift and hence forms a twisting wavefront. Our three-dimensional particle-in-cell simulations demonstrate extremely high power conversion efficiency. The plasma q-plate can work in a large range of frequencies spanning from terahertz to the optical domain

Optimal Liquidation Problems in a Randomly-Terminated Horizon

In this paper, we study optimal liquidation problems in a randomly-terminated horizon. We consider the liquidation of a large single-asset portfolio with the aim of minimizing a combination of volatility risk and transaction costs arising from permanent and temporary market impact. Three different scenarios are analyzed under Almgren-Chriss's market impact model to explore the relation between optimal liquidation strategies and potential inventory risk arising from the uncertainty of the liquidation horizon. For cases where no closed-form solutions can be obtained, we verify comparison principles for viscosity solutions and characterize the value function as the unique viscosity solution of the associated Hamilton-Jacobi-Bellman (HJB) equation

General PT-Symmetric Matrices

Three ways of constructing a non-Hermitian matrix with possible all real eigenvalues are discussed. They are PT symmetry, pseudo-Hermiticity, and generalized PT symmetry. Parameter counting is provided for each class. All three classes of matrices have more real parameters than a Hermitian matrix with the same dimension. The generalized PT-symmetric matrices are most general among the three. All self-adjoint matrices process a generalized PT symmetry. For a given matrix, it can be both PT-symmetric and P'-pseudo-Hermitian with respect to some P' operators. The relation between corresponding P and P' operators is established. The Jordan block structures of each class are discussed. Explicit examples in 2x2 are shown.Comment: 27 page

Speeding Up Classical and Quantum Adiabatic Processes: Implications for Work Functions and Heat Engine Designs

Adiabatic processes are important for studying the dynamics of a time-dependent system. Conventionally, the adiabatic processes can only be achieved by varying the system slowly. We speed up both classical and quantum adiabatic processes by adding control protocols. In classical systems, we work out the control protocols by analyzing the classical adiabatic approximation. In quantum systems, we follow the idea of transitionless driving by Berry [J. Phys. A: Math. Theor. Vol.42 365303 (2009)]. Such fast-forward adiabatic processes can be performed at arbitrary fast speed, and in the meanwhile reduce the work fluctuation. In both systems, we use a time-dependent harmonic oscillator model to work out explicitly the work function and the work fluctuation in three types of processes: fast-forward adiabatic processes, adiabatic processes, and non-adiabatic processes. We show the significant reduction on work fluctuation in fast-forward adiabatic process. We further illustrate how the fast-forward process improved the converging rate of the Jarzynski equality between the work function and the free energy. As an application, we show that the fast-forward process not only maximizes the output power but also improve the efficiency of a quantum engine.Comment: This is a rough progress report. Manuscripts are being prepared for submission for publicatio