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Winter Simulation Conference (WSC 2008)

作者： Eric Buist Wyean Chan Pierre L'Ecuyer Université de Montréal Montréal (Québec) Canada

ISBN: (纸本)9781424427086

Staffing and scheduling optimization in large multiskill call centers is time-consuming, mainly because it requires lengthy simulations to evaluate performance measures and their sensitivity. Simplified models that provide tractable formulas are unrealistic in general. In this paper we explore an intermediate solution, based on an approximate continuous-time Markov chain model of the call center. This model is more accurate than the commonly used approximations, and yet can be simulated faster than a more realistic simulation (based on non-exponential distributions and additional details). To speed up the simulation, we uniformize the Markov chain and simulate only its discrete-time version. We show how performance measures such as the fraction of calls of each type answered within a given waiting time limit can be recovered from this simulation, how to synchronize common random numbers in this setting, and how to use this in the first phase of an optimization algorithm based on the cutting plane method. We also discuss various implementation issues and provide empirical results.

关键词： simulating Call Centers Markov chain cutting plane method CHL1 gene optimization algorithms random numbers realistic simulation discrete-time Personnel Staffing and Scheduling

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Cardinal Optimizer (COPT) User Guide

arXiv

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arXiv 2022年

作者： Ge, Dongdong Huangfu, Qi Wang, Zizhuo Wu, Jian Ye, Yinyu

Cardinal Optimizer is a high-performance mathematical programming solver forefficiently solving largescale optimization problem. This documentationprovides basic introduction to the Cardinal Optimizer. © 2022, CC... 详细信息

关键词： optimization algorithms

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An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem

arXiv

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arXiv 2023年

作者： Qiao, Gang Tewari, Ambuj Department of Statistics University of Michigan United States

We study the convex hull membership (CHM) problem in the pure exploration setting where one aims to efficiently and accurately determine if a given point lies in the convex hull of means of a finite set of distributions. We give a complete characterization of the sample complexity of the CHM problem in the one-dimensional case. We present the first asymptotically optimal algorithm called Thompson-CHM, whose modular design consists of a stopping rule and a sampling rule. In addition, we extend the algorithm to settings that generalize several important problems in the multi-armed bandit literature. Furthermore, we discuss the extension of Thompson-CHM to higher dimensions. Finally, we provide numerical experiments to demonstrate the empirical behavior of the algorithm matches our theoretical results for realistic time horizons. © 2023, CC BY.

关键词： optimization algorithms

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Constrained Stochastic Recursive Momentum Successive Convex Approximation

arXiv

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arXiv 2024年

作者： Idrees, Basil M. Arora, Lavish Rajawat, Ketan

We consider stochastic optimization problems with non-convex functional constraints, such as those arising in trajectory generation, sparse approximation, and robust classification. To this end, we put forth a recursive momentum-based accelerated successive convex approximation (SCA) algorithm. At each iteration, the proposed algorithm entails constructing convex surrogates of the stochastic objective and the constraint functions, and solving the resulting convex optimization problem. A recursive update rule is employed to track the gradient of the stochastic objective function, which contributes to variance reduction and hence accelerates the algorithm convergence. A key ingredient of the proof is a new parameterized version of the standard Mangasarian-Fromowitz Constraints Qualification, that allows us to bound the dual variables and hence obtain problem-dependent bounds on the rate at which the iterates approach an ǫ-stationary point. Remarkably, the proposed algorithm achieves near-optimal stochastic first-order (SFO) complexity with adaptive step sizes and optimal SFO complexity with non-adaptive step sizes at par with that achieved by state-of-the-art stochastic optimization algorithms for solving unconstrained problems. As an example, we detail a obstacle-avoiding trajectory optimization problem that can be solved using the proposed algorithm and show that its performance is superior to that of the existing algorithms used for trajectory optimization. The performance of the proposed algorithm is also shown to be comparable to that of a specialized sparse classification algorithm applied to a binary classification problem. © 2024, CC BY.

关键词： optimization algorithms

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A PENALIZED WEIGHTED LEAST SQUARES APPROACH FOR RESTORING DATA CORRUPTED WITH SIGNAL-DEPENDENT NOISE

A PENALIZED WEIGHTED LEAST SQUARES APPROACH FOR RESTORING DA...

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European Signal Processing Conference

作者： Audrey Repetti Emilie Chouzenoux Jean-Christophe Pesquet Universite Paris-Est Lab. D'Informatique Gaspard Monge

ISBN: (纸本)9781467310680

This paper addresses the problem of recovering an image degraded by a linear operator and corrupted with an additive Gaussian noise with a signal-dependent variance. The considered observation model arises in several digital imaging devices. To solve this problem, a variational approach is adopted relying on a weighted least squares criterion which is penalized by a non-smooth function. In this context, the choice of an efficient optimization algorithm remains a challenging task. We propose here to extend a recent primal-dual proximal splitting approach by introducing a preconditioning strategy that is shown to significantly speed up the algorithm convergence. The good performance of the proposed method is illustrated through image restoration examples.

关键词： Preconditioning Convex optimization Image restoration Denoising Deconvolution Total variation Regularization Gaussian noise optimization algorithms Noise signal noise WWOX gene image restoration technique Conference documents Digital images

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Phase Retrieval via Wirtinger Flow Algorithm and Its Variants

Phase Retrieval via Wirtinger Flow Algorithm and Its Variant...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jian-Wei Liu Zhi-Juan Cao Jing Liu Xiong-Lin Luo Wei-Min Li Nobuyasu Ito Long-Chuan Guo University of Petroleum Beijing Beijing China School of Computer Engineering and Technology Shanghai University Shanghai China Center for Computational Science RIKEN Kobe Hyogo Japan School of Mechanical Engineering Hangzhou Dianzi University Xiasha Higher Education Zone Hangzhou Zhejiang Province P. R. China

ISBN: (纸本)9781728128177

Almost three-quarters of the underling information in the light wave field is embodied in the phase. However, the early optical detectors can only record the intensity or amplitude of the light wave field and cannot directly extract the phase information of the light wave field. Therefore, it is necessary to use the measured amplitude or strength to reconstruct the phase information of the object, this problem is denoted phase retrieval. Phase retrieval is a matter of cardinal significance in signal processing and machine learning. The phase retrieval by convex optimization algorithm is ideal but the computational complexity is high. In 2015, Candès proposed a very effective non-convex optimization algorithm-Wirtinger flow algorithm which used spectral initialization to get a better initial value and then gradient iteration to get a promised recovery effect. Subsequently, in line with the idea, a large number of variants are devised, such as: Wirtinger flow(WF), Truncated Wirtinger Flow (TWF), Truncated Amplitude Flow (TAF), Reshaped Wirtinger Flow (RWF), Incremental Truncated Wirtinger Flow (ITWF), Incremental Reshaped Wirtinger Flow (IRWF), Robust Wirtinger Flow (Robust-WF), Sparse Wirtinger Flow (SWF), Median-TWF, Median-RWF, Generalized Wirtinger Flow (GWF), Accelerated Wirtinger Flow (AWF), Thresholded Wirtinger Flow Revisited (THWFR), Thresholded Wirtinger Flow (THWF), Reweighted Wirtinger Flow (REWF), Wirtinger Flow Method With Optimal Stepsize (WFOS), Stochastic Truncated Wirtinger Flow Algorithm (STWF), Stochastic Truncated Amplitude Flow (STAF), Reweighted Amplitude Flow (RAF), Compressive Reweighted Amplitude Flow (CRAF), SPARse Truncated Amplitude flow (SPARTA) and Sparse Wirtinger Flow Algorithm with Optimal Stepsize (SWFOS), etc. This paper analyzes and summarizes these algorithms according to their characteristics such as: initialization method, step size, iteration times, sample complexity, computational complexity, etc., so that readers can intuitiv

关键词： Wirtingr flow Phase retrieval Provable nonconvex optimization algorithms Amplitude variants retrieval Flow wave fields Light waves Reader Device Component Source code step size optimization algorithms

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Design and implementation of adaptive resource co-allocation approaches for cloud service environments

Design and implementation of adaptive resource co-allocation...

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International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： Xiaoying Wang Hui Xie Rui Wang Zhihui Du Li Jin Department of Computer Technology and Application Qinghai University Xining Qinghai China Tsinghua National Laboratory of Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781424465392;9781424465422

As cloud computing grows rapidly and virtualization techniques become more widely-used, it is critical and important to allocate limited resources to various applications on demand for the cloud service environments. In this article, we propose an adaptive resource management approach considering multi-resource transformation to fully utilize extra resource capacity. The definition of the optimization problem concerning resource co-allocation is presented and then an optimization algorithm is developed and described, which carries out stochastic and directional search step by step to jointly schedule different resources. The evaluation results of simulation experiments demonstrate that by using the resource co-allocation approach we designed, the performance of different applications deployed in the cloud environment could be guaranteed subject to the QoS (Quality of Service) specification, despite of the significant fluctuation of workloads.

关键词： cloud computing resource allocation virtualization Cloud Computing Virtualization Resource utilization management of resources optimization algorithms Environments Workload Quality of service

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Alternating Direction Method of Multipliers Applied to 3D Light Sheet Fluorescence Microscopy Image Deblurring Using GPU Hardware

Alternating Direction Method of Multipliers Applied to 3D Li...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： F. de Vieilleville P. Weiss V. Lobjois D. Kouame Universite de Toulouse ITAV

ISBN: (纸本)9781424441211

This paper focuses on the deblurring and denoising of Poisson noise contaminated images acquired with a new imaging technique producing large 3D data sets: Light Sheet Fluorescence Microscopy. This paper details the optimization algorithm used, which is based on the Alternating Direction Method of Multipliers, and its efficient implementation using GPU hardware. In practice, a 3D 100 million voxel image is deconvolved in five minutes, which is at least 25 times faster than a state-of-the-art MATLAB implementation.

关键词： Multiplying circuits THIN PLATES optimization algorithms GRAPPER PICK UP Graphics Processing Unit Fluorescence microscopy Poisson noise deblurring image reconstruction Computer hardware Noise reduction

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Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space

Discrete MRF Inference of Marginal Densities for Non-uniform...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Masaki Saito Takayuki Okatani Koichiro Deguchi Tohoku University Japan

ISBN: (纸本)9781467364102

This paper is concerned with the inference of marginal densities based on MRF models. The optimization algorithms for continuous variables are only applicable to a limited number of problems, whereas those for discrete variables are versatile. Thus, it is quite common to convert the continuous variables into discrete ones for the problems that ideally should be solved in the continuous domain, such as stereo matching and optical flow estimation. In this paper, we show a novel formulation for this continuous-discrete conversion. The key idea is to estimate the marginal densities in the continuous domain by approximating them with mixtures of rectangular densities. Based on this formulation, we derive a mean field (MF) algorithm and a belief propagation (BP) algorithm. These algorithms can correctly handle the case where the variable space is discretized in a non-uniform manner. By intentionally using such a non-uniform discretization, a higher balance between computational efficiency and accuracy of marginal density estimates could be achieved. We present a method for actually doing this, which dynamically discretizes the variable space in a coarse-to-fine manner in the course of the computation. Experimental results show the effectiveness of our approach.

关键词： problems efficiency accuracy discrete transforms Traffic estimation optimization algorithms Computational efficiency

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Curvature and Optimal algorithms for Learning and Minimizing Submodular Functions 13

Curvature and Optimal Algorithms for Learning and Minimizing...

引用

Annual Conference on Neural Information Processing Systems

作者： Rishabh Iyer Stefanie Jegelka Jeff Bilmes University of Washington Dept. of EE Seattle U.S.A. University of California Dept. of EECS Berkeley U.S.A.

ISBN: (纸本)9781632660244

We investigate three related and important problems connected to machine learning: approximating a submodular function everywhere, learning a submodular function (in a PAC-like setting), and constrained minimization of submodular functions. We show that the complexity of all three problems depends on the "curvature" of the submodular function, and provide lower and upper bounds that refine and improve previous results. Our proof techniques are fairly generic. We either use a black-box transformation of the function (for approximation and learning), or a transformation of algorithms to use an appropriate surrogate function (for minimization). Curiously, curvature has been known to influence approximations for submodular maximization, but its effect on minimization, approximation and learning has hitherto been open. We complete this picture, and also support our theoretical claims by empirical results.

关键词： Learning optimization algorithms Curvature Function approximation

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