Citations with the tag: ROBUST optimization

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• Review of Robust Displacement Estimation Employing Inexpensive Webcam Based Optical Flow.
  Qureshi, Faisal Z. // Canadian Young Scientist Journal; 2010, Vol. 2010 Issue 2, p32 

  The article reviews the article "Robust Displacement Estimation Employing Inexpensive Webcam Based Optical Flow," by Christopher Nielsen, which appeared in the journal "Canadian Young Scientist Journal" on February 2010.

• Robust synthesis method improves dielectric-mirror design.
  Qureshi, Faisal Z. // Laser Focus World; Mar2011, Vol. 47 Issue 3, p10 

  The article reports on the robust synthesis method developed by the researchers at Ludwig-Maximilians-Universit�t M�nchen, Ultrafast Innovations, and Ultrafast Innovations which improves dielectric-mirror design to produce using conventional needle optimization techniques.

• Productivity improvement of recombinant Escherichia coli fermentation via robust optimization.
  J. Kavanagh; G. Barton // Bioprocess & Biosystems Engineering; Feb2008, Vol. 31 Issue 2, p137 

  Abstract  A nonlinear model of a recombinant Escherichia coli producing porcine growth hormone (pGH) fermentation was developed. The model was used to calculate a glucose feeding and temperature strategy to optimize the production of pGH. Simulations showed that the implementation of optimal...

• THE ADAPTIVE, MULTIMODEL ??THOD FOR ROBUST AND ACCURACY FORECASTING OF RANDOM PROCESSES.
  Borymsky, Yu. S.; Lyubashenko, N. D. // Naukovi visti NTUU - KPI; 2007, Vol. 2007 Issue 1, p42 

  The method of forecasting focused on simultaneous calculation both of the robust and accuracy forecast is described. Thus quality of the forecast obtained on the real data, should not concede to quality of the forecast evaluated as based on the historical data. The method is adapted to the big...

• Challenges of fragment screening.
  Joseph-McCarthy, Diane // Journal of Computer-Aided Molecular Design; Aug2009, Vol. 23 Issue 8, p449 

  The article reflects on the challenges faced in fragment based screening for lead generation. It is said that computational approaches which can aid in the optimization process either through growing or linking of fragments continue to be developed and can play an important role in reducing the...

• Heterogeneous attachment strategies optimize the topology of dynamic wireless networks.
  Holme, P.; Kim, B. J.; Fodor, V. // European Physical Journal B -- Condensed Matter; Feb2010, Vol. 73 Issue 4, p597 

  In optimizing the topology of wireless networks built of a dynamic set of spatially embedded agents, there are many trade-offs to be dealt with. The network should preferably be as small (in the sense that the average, or maximal, pathlength is short) as possible, it should be robust to...

• Robustness and Regularization of Support Vector Machines.
  Huan Xu; Caramanis, Constantine; Mannor, Shie // Journal of Machine Learning Research; 7/1/2009, Vol. 10 Issue 7, p1485 

  We consider regularized support vector machines (SVMs) and show that they are precisely equivalent to a new robust optimization formulation. We show that this equivalence of robust optimization and regularization has implications for both algorithms, and analysis. In terms of algorithms, the...

• Cautious Collective Classification.
  McDowell, Luke K.; Gupta, Kalyan Moy; Aha, David W. // Journal of Machine Learning Research; 12/1/2009, Vol. 10 Issue 12, p2777 

  Many collective classification (CC) algorithms have been shown to increase accuracy when instances are interrelated. However, CC algorithms must be carefully applied because their use of estimated labels can in some cases decrease accuracy. In this article, we show that managing this label...

• Design of robust stable controls for nonlinear objects.
  Kuntsevich, V.; Kuntsevich, A. // Automation & Remote Control; Dec2008, Vol. 69 Issue 12, p2088 

  We consider a problem of discrete control for a class of nonlinear time-varying objects. Only set estimations for object parameters are available. The aim is to design controls that ensure robust stability of closed-loop systems in a given domain of state space. Since the considered class of...

• A randomized algorithm for the min-max selecting items problem with uncertain weights.
  Kasperski, Adam; Zielinski, Pawel // Annals of Operations Research; Nov2009, Vol. 172 Issue 1, p221 

  This paper deals with the min-max version of the problem of selecting p items of the minimum total weight out of a set of n items, where the item weights are uncertain. The discrete scenario representation of uncertainty is considered. The computational complexity of the problem is explored. A...

• The semismooth approach for semi-infinite programming under the Reduction Ansatz.
  Oliver Stein; Aysun Tezel // Journal of Global Optimization; Jun2008, Vol. 41 Issue 2, p245 

  Abstract��We study convergence of a semismooth Newton method for generalized semi-infinite programming problems with convex lower level problems where, using NCP functions, the upper and lower level Karush-Kuhn-Tucker conditions of the optimization problem are reformulated as a semismooth...

• Portfolio Selection with Robust Estimation.
  DeMiguel, Victor; Nogales, Francisco J. // Operations Research; May2009, Vol. 57 Issue 3, p560 

  Mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly out of sample due to estimation error. Moreover, it is commonly accepted that estimation error in the sample mean is much larger than in the sample covariance matrix. For this reason,...

• On the Expected Probability of Constraint Violation in Sampled Convex Programs.
  Calafiore, G. // Journal of Optimization Theory & Applications; Nov2009, Vol. 143 Issue 2, p405 

  In this note, we derive an exact expression for the expected probability V of constraint violation in a sampled convex program (see Calafiore and Campi in Math. Program. 102(1):25�46, ; IEEE Trans. Autom. Control 51(5):742�753, for definitions and an introduction to this topic): This result...

• Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy.
  Rosten, Edward; Loveland, Rohan // Machine Vision & Applications; Jan2011, Vol. 22 Issue 1, p77 

  In this paper, we present a simple and robust method for self-correction of camera distortion using single images of scenes which contain straight lines. Since the most common distortion can be modelled as radial distortion, we illustrate the method using the Harris radial distortion model, but...

• ADAPTIVE PENALTY FUNCTION FOR SOLVING CONSTRAINED EVOLUTIONARY OPTIMIZATION.
  Jadaan, Omar Al; Rajamani, Lakshmi; Rao, C. R. // Journal of Theoretical & Applied Information Technology; 2009, Vol. 5 Issue 3, p339 

  A criticism of Evolutionary Algorithms might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods, because of their simplicity and ease of implementation. The penalty function approach is...

• Application of a simulated annealing optimization to a physically based erosion model.
  Santos, C. A. G.; Freire, P. K. M. M.; Arruda, P. M. // Water Science & Technology; 2012, Vol. 66 Issue 10, p2099 

  A major risk concerning the calibration of physically based erosion models has been partly attributable to the lack of robust optimization tools. This paper presents the essential concepts and application to optimize the erosion parameters of an erosion model using data collected in an...

• Is attention needed for word identification? Evidence from the Stroop paradigm.
  Joel Lachter; Eric Ruthruff; Mei-Ching Lien; Robert S McCann // Psychonomic Bulletin & Review; Oct2008, Vol. 15 Issue 5, p950 

  One of the most robust findings in attention research is that the time to name a color is lengthened markedly in the presence of an irrelevant word that spells a different color name: the Stroop effect. The Stroop effect is found even when the word is physically separated from the color,...

• Worst-Case Violation of Sampled Convex Programs for Optimization with Uncertainty.
  Kanamori, Takafumi; Takeda, Akiko // Journal of Optimization Theory & Applications; Jan2012, Vol. 152 Issue 1, p171 

  A deterministic approach called robust optimization has been recently proposed to deal with optimization problems including inexact data, i.e., uncertainty. The basic idea of robust optimization is to seek a solution that is guaranteed to perform well in terms of feasibility and near-optimality...

• Dynamic Pricing and Inventory Control: Uncertainty and Competition.
  Adida, Elodie; Perakis, Georgia // Operations Research; Mar2010, Vol. 58 Issue 2, p289 

  In this paper, we study a make-to-stock manufacturing system where two firms compete through dynamic pricing and inventory control. Our goal is to address competition (in particular a duopoly setting) together with the presence of demand uncertainty. We consider a dynamic setting where multiple...

• Robust Optimization-Based Generation Self-Scheduling under Uncertain Price.
  Xiao Luo; Chi-yung Chung; Hongming Yang; Xiaojiao Tong // Mathematical Problems in Engineering; 2011, Vol. 2011, Special section p1 

  This paper considers generation self-scheduling in electricity markets under uncertain price. Based on the robust optimization (denoted as RO) methodology, a new self-scheduling model, which has a complicated max-min optimization structure, is set up. By using optimal dual theory, the proposed...

• Simultaneous kriging-based estimation and optimization of mean response.
  Janusevskis, Janis; Le Riche, Rodolphe // Journal of Global Optimization; Feb2013, Vol. 55 Issue 2, p313 

  Robust optimization is typically based on repeated calls to a deterministic simulation program that aim at both propagating uncertainties and finding optimal design variables. Often in practice, the 'simulator' is a computationally intensive software which makes the computational cost one of the...

• Influence measures and robust estimators of dependence in multivariate extremes.
  Tsai, Yu-Ling; Murdoch, Duncan; Dupuis, Debbie // Extremes; Dec2011, Vol. 14 Issue 4, p343 

  We develop a simple influence measure to assess whether Bayesian estimators in multivariate extreme value problems are sensitive to outliers. The proposed measure is easy to compute by importance sampling and successfully captures two effects on the functional: the 'data effect' and the...

• A Robust Optimization Perspective on Stochastic Programming.
  Xin Chen; Sim, Melvyn; Pang Sun // Operations Research; Nov/Dec2007, Vol. 55 Issue 6, p1058 

  In this paper, we introduce an approach for constructing uncertainty sets for robust optimization using new deviation measures for random variables termed the forward and backward deviations. These deviation measures capture distributional asymmetry and lead to better approximations of chance...

• On the Power of Robust Solutions in Two-Stage Stochastic and Adaptive Optimization Problems.
  Bertsimas, Dimitris; Goyal, Vineet // Mathematics of Operations Research; May2010, Vol. 35 Issue 2, p284 

  We consider a two-stage mixed integer stochastic optimization problem and show that a static robust solution is a good approximation to the fully adaptable two-stage solution for the stochastic problem under fairly general assumptions on the uncertainty set and the probability distribution. In...

• Optimality of Affine Policies in Multistage Robust Optimization.
  Bertsimas, Dimitris; Iancu, Dan A.; Parrilo, Pablo A. // Mathematics of Operations Research; May2010, Vol. 35 Issue 2, p363 

  In this paper, we prove the optimality of disturbance-affine control policies in the context of one-dimensional, constrained, multistage robust optimization. Our results cover the finite-horizon case, with minimax (worst-case) objective, and convex state costs plus linear control costs. We...

• Model Selection in Kernel Based Regression using the Influence Function.
  Debruyne, Michiel; Hubert, Mia; Suykens, Johan A. K. // Journal of Machine Learning Research; 10/1/2008, Vol. 9 Issue 10, p2377 

  Recent results about the robustness of kernel methods involve the analysis of influence functions. By definition the influence function is closely related to leave-one-out criteria. In statistical learning, the latter is often used to assess the generalization of a method. In statistics, the...

• Management Insights.
  Debruyne, Michiel; Hubert, Mia; Suykens, Johan A. K. // Management Science; Apr2006, Vol. 52 Issue 4, piv 

  A section of abstracts summarizes several articles in the same issue, including "The Firm Specificity of Individual Performance: Evidence from Cardiac Surgery," by Robert S. Huckman and Gary P. Pisano, "Does Past Success Lead Analysts to Become Overconfident?" by Gilles Hilary and Lior Menzly,...

• SDP reformulation for robust optimization problems based on nonconvex QP duality.
  Nishimura, Ryoichi; Hayashi, Shunsuke; Fukushima, Masao // Computational Optimization & Applications; May2013, Vol. 55 Issue 1, p21 

  In a real situation, optimization problems often involve uncertain parameters. Robust optimization is one of distribution-free methodologies based on worst-case analyses for handling such problems. In this paper, we first focus on a special class of uncertain linear programs (LPs). Applying the...

• Robust portfolio asset allocation and risk measures.
  Scutellà, Maria; Recchia, Raffaella // Annals of Operations Research; Apr2013, Vol. 204 Issue 1, p145 

  Many financial optimization problems involve future values of security prices, interest rates and exchange rates which are not known in advance, but can only be forecast or estimated. Several methodologies have therefore been proposed to handle the uncertainty in financial optimization problems....

• Robust Programming Problems Based on the Mean-Variance Model Including Uncertainty Factors.
  Hasuike, Takashi; Ishii, Hiroaki // AIP Conference Proceedings; 1/12/2009, Vol. 1089 Issue 1, p224 

  This paper considers robust programming problems based on the mean-variance model including uncertainty sets and fuzzy factors. Since these problems are not well-defined problems due to fuzzy factors, it is hard to solve them directly. Therefore, introducing chance constraints, fuzzy goals and...

• The Bowl Result Detection for Bowling Game Videos.
  Jiann-Shu Lee; Shang-Chin Su; Shing-Tai Pan // International MultiConference of Engineers & Computer Scientists; 2007, p1844 

  In this paper, we propose a robust approach to bowling game result indexing. A reliable attribute, i.e. the lane boundary, is detected first. Based on this attribute the rudimentary result frame can be detected. The spectral features of the audio frames of the candidates are employed to...

• Computing Improved Fuzzy Optimal Hungarian Assignment Problems with Fuzzy Costs under Robust Ranking Techniques.
  Jiann-Shu Lee; Shang-Chin Su; Shing-Tai Pan // International Journal of Computer Applications; Sep2010, Vol. 6, p6 

  The article discusses the study on the use of Robust's ranking method in computing-improved Fuzzy optimal Hungarian assignment problems (AP) with Fuzzy costs. The ranking of the fuzzy numbers is evaluated. It mentions the transformation of the fuzzy assignment problem into crisp assignment...

• On Some Manipulations with Fuzzy Processes.
  Luca, Lucian; Luca, Lucian L. // Annals. Computer Science Series; 2009, Vol. 7 Issue 1, p215 

  The paper starts from the observation on the complexity of the manipulation of fuzzy processes that increases very rapidly with the extents of the processes representation. Therefore, a productive approach is to divide the problem into smaller parts, treated separately and then the results...

• Switched on.
  Glaskin, Max // Engineer (00137758); 11/13/2006, Vol. 293 Issue 7712, p11 

  The article focuses on the optimization of antenna for wearable devices to reduce signal fade caused by positioning and movement. It is inferred that cumbersome aerial requires a big battery, and the frequency used by Bluetooth is limited for the data rates needed in the communication of live...

• The Role of Robust Optimization in Single-Leg Airline Revenue Management.
  Birbil, S. Ilker; Frenk, J. B. G.; Gromicho, Joaquim A. S.; Zhang, Shuzhong // Management Science; Jan2009, Vol. 55 Issue 1, p148 

  In this paper, we introduce robust versions of the classical static and dynamic single-leg seat allocation models. These robust models take into account the inaccurate estimates of the underlying probability distributions. As observed by simulation experiments, it turns out that for these robust...

• Robust One-Period Option Hedging.
  Lutgens, Frank; Sturm, Jos; Kolen, Antoon // Operations Research; Nov/Dec2006, Vol. 54 Issue 6, p1051 

  We consider robust optimization to cope with uncertainty about the stock return process in one-period option hedging problems. The robust approach relates portfolio choice to uncertainty, making more cautious hedges when uncertainty is high. We represent uncertainty by a set of plausible...

• Two-Stage Robust Network Flow and Design Under Demand Uncertainty.
  Atamt�rk, Alper; Muhong Zhang // Operations Research; Jul2007, Vol. 55 Issue 4, p662 

  We describe a two-stage robust optimization approach for solving network flow and design problems with uncertain demand. In two-stage network optimization, one defers a subset of the flow decisions until after the realization of the uncertain demand. Availability of such a recourse action allows...

• Monotone Approximation of Decision Problems.
  Chehrazi, Naveed; Weber, Thomas A. // Operations Research; Jul2010, Vol. 58 Issue 4, p1158 

  The article examines the approximation of decision problems based on structural monotonicity constraints and considering robustly determining optimal actions. A data-driven robust optimization method for environment which can be sample-sparse was developed through the use of a constrained...

• A Soft Robust Model for Optimization Under Ambiguity.
  Ben-Tal, Aharon; Bertsimas, Dimitris; Brown, David B. // Operations Research; Jul2010, Vol. 58 Issue 4, p1220 

  In this paper, we propose a framework for robust optimization that relaxes the standard notion of robustness by allowing the decision maker to vary the protection level in a smooth way across the uncertainty set. We apply our approach to the problem of maximizing the expected value of a payoff...

• Robust Optimization Made Easy with ROME.
  Goh, Joel; Sim, Melvyn // Operations Research; Jul2011, Vol. 59 Issue 4, p973 

  We introduce ROME, an algebraic modeling toolbox for a class of robust optimization problems. ROME serves as an intermediate layer between the modeler and optimization solver engines, allowing modelers to express robust optimization problems in a mathematically meaningful way. In this paper, we...

• Envy-freeness and implementation in large economies.
  Jackson, Matthew; Kremer, Ilan // Review of Economic Design; 2007, Vol. 11 Issue 3, p185 

  We show that an asymptotic envy-freeness condition is necessary for a form of robust approximate implementation in large economies. In settings where allocations are excludable, asymptotic envy-freeness is also sufficient for implementation, while in non-excludable settings it is not sufficient.

• Mutual Information-Based 3D Object Tracking.
  Panin, Giorgio; Knoll, Alois // International Journal of Computer Vision; May2008, Vol. 78 Issue 1, p107 

  We propose a robust methodology for 3D model-based markerless tracking of textured objects in monocular image sequences. The technique is based on mutual information maximization, a widely known criterion for multi-modal image registration, and employs an efficient multiresolution strategy in...

• A Plant Location Guide for the Unsure: Approximation Algorithms for Min-Max Location Problems.
  Anthony, Barbara; Goyal, Vineet; Gupta, Anupam; Nagarajan, Viswanath // Mathematics of Operations Research; Feb2010, Vol. 35 Issue 1, p79 

  This paper studies an extension of the k-median problem under uncertain demand. We are given an n-vertex metric space (V, d) and m client sets {Si ? V}mi=1. The goal is to open a set of k facilities F such that the worst-case connection cost over all the client sets is minimized, i.e., min max...

• Screening Designs (Part I) -- Types and Properties.
  Dejaegher, Bieke; Vander Heyden, Yvan // LC-GC Europe; Oct2007, Vol. 20 Issue 10, p526 

  Screening designs are used to screen for important factors during method optimization or in robustness testing. Usually, two-level screening designs, such as fractional factorial and Plackett-Burman designs, are applied. This column discusses the properties of these designs.

• Interval Uncertainty-Based Robust Optimization for Convex and Non-Convex Quadratic Programs with Applications in Network Infrastructure Planning.
  Li, Mian; Gabriel, Steven; Shim, Yohan; Azarm, Shapour // Networks & Spatial Economics; Mar2011, Vol. 11 Issue 1, p159 

  Planning infrastructure networks such as roads, pipelines, waterways, power lines and telecommunication systems, require estimations on the future demand as well as other uncertain factors such as operating costs, degradation rates, or the like. When trying to construct infrastructure that is...

• IMPROVED EXTRACTION OF COUPLING MATRIX AND UNLOADED Q FROM S-PARAMETERS OF LOSSY RESONATOR FILTERS.
  Wang, R.; Xu, J.; Wei, C.-L.; Wang, M.-Y.; Zhang, X.-C. // Progress in Electromagnetics Research; Nov2011, Vol. 120, p67 

  This paper presents a two-stage optimization method for accurately extracting the coupling matrix (CM) and the unloaded quality factor (unloaded Q) of each resonator from the measured (or simulated) S-parameters of lossy cross-coupled resonator bandpass filters. The method can be used in...

• Factoring Information into Returns.
  Easley, David; Hvidkjaer, Soeren; O'Hara, Maureen // Journal of Financial & Quantitative Analysis; Apr2010, Vol. 45 Issue 2, p293 

  We examine the potential profits of trading on a measure of private information (PIN) in a stock. A zero-investment portfolio that is size-neutral but long in high PIN stocks and short in low PIN stocks earns a significant abnormal return. The Fama-French, momentum, and liquidity factors do not...

• A robust optimization approach for real-time multiple source drinking water blending problem.
  Wei Peng; Mayorga, Rene V.; Imran, Syed // Journal of Water Supply: Research & Technology-AQUA; Mar2012, Vol. 61 Issue 2, p111 

  Although many optimization methods can be applied to real-time multiple source drinking water blending problems, the field still lacks an approach to rapidly produce a robust optimal solution by simultaneously optimizing multiple objectives. This paper develops a fuzzy multiple response surface...

• GATEway: symbiotic inter-domain traffic engineering.
  Roughan, Matthew; Zhang, Yin // Telecommunication Systems; Jun2011, Vol. 47 Issue 1/2, p3 

  There are a group of problems in networking that can most naturally be described as optimization problems (network design, traffic engineering, etc.). There has been a great deal of research devoted to solving these problems, but this research has been concentrated on intra-domain problems where...

• Robust Optimization Model for a Dynamic Network Design Problem Under Demand Uncertainty.
  Chung, Byung; Yao, Tao; Xie, Chi; Thorsen, Andreas // Networks & Spatial Economics; Jun2011, Vol. 11 Issue 2, p371 

  This paper describes a robust optimization approach for a network design problem explicitly incorporating traffic dynamics and demand uncertainty. In particular, we consider a cell transmission model based network design problem of the linear programming type and use box uncertainty sets to...

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