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Analytical solution of the Voznesenskiy problem for stationary and nonstationary linear systems

JOURNAL OF COMPUTER AND SYSTEMS SCIENCES INTERNATIONAL 1996年第5期34卷 47-59页

作者： Krutko, PD Bauman Technical Univ Russian Federation

An exposition is made of the solution of the problem of decomposing and controlling the perturbed motion of a multiply connected system from the condition that autonomous subsystems with respect to each degree of freedom be asymptotically stable and possess a given spectrum. The decomposition algorithms are obtained in analytical form by the method of the inverse problems of dynamics. It is shown that the structure and the parameters of the algorithms are uniquely determined by the structure and the parameters of a mathematical model of the system being controlled and by the given spectrum of the models of the separate channels. The relationships between the algorithms of decomposition and control are written directly from the equations of motion of the system being decomposed and the standard models, with the aid of which the required dynamics of the autonomous subsystems is assigned. An examination is made of one-level and two-level structures. The solutions found are valid both for stationary and for nonstationary systems.

关键词： Voznesenskiy problem decomposition algorithms compensating feedback inverse problems of dynamics auxiliary controlling function

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Amultistage decision-dependent stochastic bilevel programming approach for power generation investment expansion planning

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IISE TRANSACTIONS 2018年第8期50卷 720-734页

作者： Zhan, Yiduo Zheng, Qipeng P. Univ Cent Florida Dept Ind Engn & Management Syst Orlando FL 32816 USA

In this article, we study the long-term power generation investment expansion planning problem under uncertainty. We propose a bilevel optimization model that includes an upper-level multistage stochastic expansion planning problem and a collection of lower-level economic dispatch problems. This model seeks for the optimal sizing and siting for both thermal and wind power units to be built to maximize the expected profit for a profit-oriented power generation investor. To address the future uncertainties in the decision-making process, this article employs a decision-dependent stochastic programming approach. In the scenario tree, we calculate the non-stationary transition probabilities based on discrete choice theory and the economies of scale theory in electricity systems. The model is further reformulated as a single-level optimization problem and solved by decomposition algorithms. The investment decisions, computation times, and optimality of the decision-dependent model are evaluated by case studies on IEEE reliability test systems. The results show that the proposed decision-dependent model provides effective investment plans for long-term power generation expansion planning.

关键词： Multistage stochastic programming bilevel optimization generation expansion decision-dependent probability decomposition algorithms

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Training SVMs Without Offset

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JOURNAL OF MACHINE LEARNING RESEARCH 2011年 12卷 141-202页

作者： Steinwart, Ingo Hush, Don Scovel, Clint Univ Stuttgart Inst Stochast & Anwendungen Fak Math & Phys D-70569 Stuttgart Germany Los Alamos Natl Lab Los Alamos NM 87545 USA

We develop, analyze, and test a training algorithm for support vector machine classifiers without offset. Key features of this algorithm are a new, statistically motivated stopping criterion, new warm start options, and a set of inexpensive working set selection strategies that significantly reduce the number of iterations. For these working set strategies, we establish convergence rates that, not surprisingly, coincide with the best known rates for SVMs with offset. We further conduct various experiments that investigate both the run time behavior and the performed iterations of the new training algorithm. It turns out, that the new algorithm needs significantly less iterations and also runs substantially faster than standard training algorithms for SVMs with offset.

关键词： support vector machines decomposition algorithms

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Global mixed-integer dynamic optimization

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AICHE JOURNAL 2005年第8期51卷 2235-2253页

作者： Chachuat, B Singer, AB Barton, PI MIT Dept Chem Engn Proc Syst Engn Lab Cambridge MA 02139 USA

Recent advances in process synthesis, design, operations, and control have created an increasing demand for efficient numerical algorithms for optimizing a dynamic system coupled with discrete decisions;these problems are termed mixed-integer dynamic optimization (MIDO). In this communication, we develop a decomposition approach for a quite general class of MIDO problems that is capable of guaranteeing finding a global solution despite the nonconvexities inherent in the dynamic optimization subproblems. Two distinct algorithms are considered. On finite termination, the first algorithm guarantees finding a global solution of the MIDO within nonzero tolerance;the second algorithm finds rigorous bounds bracketing the global solution value, with a substantial reduction in computational expense relative to the first algorithm. A case study is presented in connection with the optimal design and operation of a batch process consisting of a series reaction followed by a separation with no intermediate storage. The developed algorithms demonstrate efficiency and applicability in solving this problem. Several heuristics are tested to enhance convergence of the algorithms;in particular, the use of bounds tightening techniques and the addition of cuts resulting from a screening model of the batch process are considered. (c) 2005 American Institute of Chemical Engineers

关键词： mixed-integer dynamic optimization nonconvex problems global optimization decomposition algorithms outer-approximation batch process design

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On the Values of Vehicle-to-Grid Electricity Selling in Electric Vehicle Sharing

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2021年第2期23卷 488-507页

作者： Zhang, Yiling Lu, Mengshi Shen, Siqian Univ Minnesota Dept Ind & Syst Engn Minneapolis MN 55455 USA Purdue Univ Krannert Sch Management W Lafayette IN 47907 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

Problem definition: We study electric vehicle (EV) sharing systems and explore the opportunity for incorporating vehicle-to-grid (V2G) electricity selling in EV sharing. Academic/practical relevance: The problem involves complex planning and operational decisions, as well as multiple sources of uncertainties. The related optimization models impose significant computational challenges. The potential value of V2G integration may have far-reaching impacts on EV sharing and sustainability. Methodology: We formulate the problem as a two-stage stochastic integer linear program. In the first stage, we optimize decisions related to service planning, the capacity of parking and charging facilities, EV battery capacities, and EV allocation in each zone under uncertain time-dependent trip demand and electricity prices. In the second stage, for a realized demand-price scenario, we construct a time-and-charging-status expanded transportation network and optimize operations of the shared vehicle fleet, EV battery charging, and V2G selling. We develop Benders decomposition and scenario decomposition approaches to improve computational efficiency. A linear-decision-rule-based approximation approach is also provided to model dynamic operations. Results: Via testing instances based on real-world and synthetic data, we demonstrate the computational efficacy of our approaches and study the benefits of integrating V2G in EV sharing from the service provider, consumer, and socioenvironmental aspects. Managerial implications: V2G integration can significantly increase the profitability of EV sharing and the quality of service. It results in the preference of larger EV fleets and battery capacities, which further leads to various socioenvironmental benefits. The benefit of V2G can still prevail, even with more severe battery degradation and can be more significant when combined with (i) more stringent service levels, (ii) more traffic congestion, or (iii) urban spatial structures with conc

关键词： sustainable transportation energy systems electric vehicle sharing vehicle-to-grid stochastic integer programming decomposition algorithms

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An Efficient Solution Algorithm for Large-Scale Stochastic Mixed-Integer Linear Programs 1

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IFAC Proceedings Volumes 2012年第15期45卷 892-897页

作者： Arul Sundaramoorthy Xiang Li James M.B. Evans Paul I. Barton Process Systems Engineering Laboratory Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA (Tel: 617-253-6468) Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA (Tel: 617-253-1387) Process Systems Engineering Laboratory Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA (Tel: 617-253-6526)

In this work, we develop a decomposition strategy that exploits the underlying structure of two-stage stochastic programming (TSSP) formulations with mixed-integer linear programming (MILP) recourse problems. We demonstrate the effectiveness of the proposed algorithm using several examples of a capacity planning problem that arises in the pharmaceutical industry. The examples contain up to 10 potential products and nearly 60,000 scenarios. With the proposed decomposition algorithm, the solution time scales linearly with the number of scenarios, whereby a 10-product example with over 36 million binary variables, nearly 176 million continuous variables, and over 150 million constraints was solved in less than 3 hr of solver time.

关键词： decomposition algorithms Stochastic programming

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Minimization of a nondifferentiable convex function defined not everywhere

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OPTIMIZATION 2002年第6期51卷 819-840页

作者： Beer, K Gol'shtein, EG Sokolov, NA Russian Acad Sci Cent Econ & Math Inst Moscow 117418 Russia Tech Univ Chemnitz Fak Math D-09107 Chemnitz Germany

We examine an oracle-type method to minimize a convex function f over a convex polyhedron G. The method is an extension of the level-method to the case, when f is a not everywhere finite function, i.e., it may equal to +infinity at some points of G. An estimate of its efficiency is given, and some modifications of the method are mentioned. Finally, we indicate possible ways of its employment and report results of a numerical experiment.

关键词： nondifferentiable optimization cutting plane methods level methods decomposition algorithms

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Distributed MMSE Beamforming Design for Relay-Assisted C-RAN

Distributed MMSE Beamforming Design for Relay-Assisted C-RAN

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IEEE Vehicular Technology Conference

作者： Na Li Chengwen Xing Zesong Fei Liang Sun Ming Lei School of Information and Electronics Beijing Institute of Technology NEC Laboratories China

ISBN: (纸本)9781479944811

In this paper, the linear minimum mean square error beamformers are designed for relay-assisted cloud radio access network (C-RAN). In C-RAN, the remote radio units are separated from the baseband units to save energy cost. To further enhance network coverage, it is a good choice to duly arrange relay nodes. Regrading the per-antenna power constraints at both the relay nodes and the remote radio heads (RRHs), the beamformer matrices at the relay nodes and RRHs are jointly optimized for the relay assisted C-RAN. Since the considered problem is a non-convex optimization problem and is with multiple variables, it is in general very hard to solve. To make the design suitable for C-RAN exploiting the problem structure, two novel decomposition algorithms are proposed. One algorithm is mainly carried out at the RRHs, another is mainly performed at the relay node. Finally in the simulations, the performance of the proposed algorithms is demonstrated.

关键词： antennas array signal processing decomposition least mean squares methods optimisation radio access networks relay networks (telecommunication) telecommunication power management RRH baseband units beamformer matrices cloud radio access network decomposition algorithms distributed MMSE beamforming design energy cost linear minimum mean square error beamformers network coverage per-antenna power constraints relay nodes relay-assisted C-RAN remote radio heads remote radio units Algorithm design and analysis Array signal processing Clustering algorithms Optimization Relays Signal processing algorithms Vectors

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Optimal Demand Response in distribution Networks with several Energy Retail Companies

Optimal Demand Response in distribution Networks with severa...

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IEEE Multi-Conference on Systems and Control

作者： Sebastian Montes de Oca Pablo Belzarena Pablo Monzon Universidad de la República Montevideo Uruguay

ISBN: (纸本)9781509007561

This paper is concerned with the promising benefits of the incorporation of demand response (DR) scheme in residential end-users, but considering the AC distribution network's constraints and several retail companies or Local Service Entities (LSEs) participating in the retail market. This residential DR is modeled as an OPF problem run by the Operator of the distribution network, with the aggregated information collected by each retail company from their clients. We use a simple model and formulate the problem as a dynamic program that maximizes social welfare. The primal-dual method used to decouple some constraints and optimize the system, preserves the integrity of the private information of each participant and captures its preferences and constraints.

关键词： Demand Response Smart Grid decomposition algorithms LSE Optimal Power Flow

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The protein fluorescence and structural toolkit: Database and programs for the analysis of protein fluorescence and structural data

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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2008年第4期71卷 1744-1754页

作者： Shen, Chi Menon, Rajiv Das, Dipanwita Bansal, Nidhi Nahar, Neha Guduru, Neelima Jaegle, Stephen Peckham, Joan Reshetnyak, Yana K. Univ Rhode Isl Dept Phys Kingston RI 02881 USA Univ Rhode Isl Dept Comp Sci & Stat Kingston RI 02881 USA

Protein fluorescence is a powerful tool for studying protein structure and dynamics if we have a means to interpret the spectral data in terms Of protein structural properties. Our previous research successfully provided this support through the development of individual software modules implementing the algorithms for fluorescence and structural analyses. Now we have integrated the developed software modules, introduced a new program for the assignment of tryptophan residues to spectral-structural classes, and created a web-based toolkit PFAST: protein fluorescence and structural toolkit. http://***/. PFAST contains three modules: (1) FCAT is a fluorescence-correlation analysis tool, which decomposes protein fluorescence spectra to reveal the spectral components of individual tryptophan residues or groups of tryptophan residues located close to each other, and assigns spectral components to one of five previously established spectral-structural classes. (2) SCAT is a structural-correlation analysis tool for the calculation of the structural parameters of the environment of tryptophan residues from the atomic structures of the proteins from the Protein Data Bank (PDB), and for the assignment of tryptophan residues to one of five spectral-structural classes. (3) The last module is a PFAST database that contains protein fluorescence and structural data obtained from results of the FCAT and SCAT analyses.

关键词： decomposition algorithms tryptophan fluorescence spectral-structural classes discriminant analysis classification score scientific databases scripted online database environment

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