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American Control Conference

作者： Wentao Tang Prodromos Daoutidis University of Minnesota Twin Cities Minneapolis MN 55455 U.S.A.

ISBN: (纸本)9781538679012;9781538679265

Alternating direction method of multipliers (ADMM), as a powerful distributed optimization algorithm, provides a framework for distributed model predictive control (MPC) for nonlinear process systems based on local subsystem model information. However, the practical application of classical ADMM is largely limited by the high computational cost caused by its slow (linear) rate of convergence and non-parallelizability. In this work, we combine a recently developed multi-block parallel ADMM algorithm with a Nesterov acceleration technique into a fast ADMM scheme, and apply it to the solution of optimal control problems associated with distributed nonlinear MPC. A benchmark chemical process is considered for a case study, which demonstrates a significant reduction of computational time and communication effort compared to non-parallel and non-accelerated ADMM counterparts.

关键词： nonlinear model predictive control process systems nonparallelism computational time optimization algorithms Distribute Optimal control Parallel Lines Converge

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A Survivability-Centered Research Agenda for Cloud Computing Supported Emergency Response and Management Systems

A Survivability-Centered Research Agenda for Cloud Computing...

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IEEE Aerospace Conference

作者： Zhanshan (Sam) Ma Ronald P. Neilson Liexun Yang Andrew Hess Richard Millar Computational Biology & Medical Ecology Lab State Key Lab. of Genetic Resources & Evolution KIZ Chinese Academy of Sciences BioClimatologist USDA Forest Service USDA Forest Services & Oregon State University Department of Management Sciences National Natural Science Foundation of China The Hess PHM Group Inc Department of Systems Engineering Naval Postgraduate School

ISBN: (纸本)9781479916191

Cloud computing is evolved from grid computing with a key support from the rapidly expanding virtualization technology. We argue that clouding computing is particularly suitable for supporting emergency response and management (ERM) because of some of its unique features such as rapid setup and deployment on ad hoc basis, highly flexible platforms (PaaS: Platform as a Service) and application services (SaaS: Software as a Service) with little time-space constraints. ERM is one of the seven critical national infrastructures and services mandated to protect by the 1999 US President's Executive Order (PCCIP). The paradigm of survivability and survivable network systems was a response of academia to the president's executive order. We concur that survivability should be the lifeline of any ERM, including the cloud computing supported (CCS) ERM systems. In this article, we present a research agenda that is aimed at developing a survivability-centered architecture for evolving reliable and survivable CCS-ERM systems. The research agenda suggests that biological and computational evolutions should be rich sources of biological inspirations as well as powerful optimization algorithm for designing (evolving) the ERM systems. The proposed research agenda advocates the application of three-layer survivability analysis, dynamic hybrid fault models, and extended evolutionary game theory modeling developed by Ma & Krings [Ma & Krings (2008a-e, 2011), Ma et al. (2009a), Ma (2008, 2009, 2010, 2011a,b). We use banking system survivability as an example to illustrate the proposed research agenda.

关键词： Cloud Computing Emergency Response and Management Reliability Survivability Three-Layer Survivability Analysis Extended Evolutionary Game Theory Dynamic Hybrid Fault Models Evolutionary Computing Cloud Computing survivability Legal Services Software as a service optimization algorithms executive command EMERGENCY RESPONSE Emergency Services VT Griffin

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An Adaptive Genetic Algorithm for determining optimal structures for atomic clusters

arXiv

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arXiv 2024年

作者： Willnecker, Brandon Moodley, Mervlyn School of Chemistry and Physics University of KwaZulu-Natal Westville Campus Private Bag X54001 Durban4000 South Africa

The implementation of adaptive genetic algorithms (AGA) for optimization problems has proven to be superior than many other methods due to its nature of producing more robust and high quality solutions. Considering the complexity involved in many-body simulations, a novel AGA is proposed for applications to such systems and is specifically used to determine the lowest energy structures of various sized atomic clusters. For demonstrative purposes, we apply our method to various sized Lennard-Jones clusters and show that our results are more accurate than those found in the literature employing different methods. © 2024, CC BY.

关键词： optimization algorithms

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Learning complexity of gradient descent and conjugate gradient algorithms

arXiv

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arXiv 2024年

作者： Jiao, Xianqi Liu, Jia Chen, Zhiping School of Mathematics and Statistics Xi’an Jiaotong University Shaanxi China

Gradient Descent (GD) and Conjugate Gradient (CG) methods are among the most effective iterative algorithms for solving unconstrained optimization problems, particularly in machine learning and statistical modeling, where they are employed to minimize cost functions. In these algorithms, tunable parameters, such as step sizes or conjugate parameters, play a crucial role in determining key performance metrics, like runtime and solution quality. In this work, we introduce a framework that models algorithm selection as a statistical learning problem, and thus learning complexity can be estimated by the pseudo-dimension of the algorithm group. We first propose a new cost measure for unconstrained optimization algorithms, inspired by the concept of primal-dual integral in mixed-integer linear programming. Based on the new cost measure, we derive an improved upper bound for the pseudo-dimension of gradient descent algorithm group by discretizing the set of step size configurations. Moreover, we generalize our findings from gradient descent algorithm to the conjugate gradient algorithm group for the first time, and prove the existence a learning algorithm capable of probabilistically identifying the optimal algorithm with a sufficiently large sample size. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Linear model predictive control based on approximate optimal control inputs and constraint tightening

Linear model predictive control based on approximate optimal...

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IEEE Annual Conference on Decision and Control

作者： Ion Necoara Valentin Nedelcu Tamas Keviczky Minh Dang Doan Bart de Schutter Automatic Control and Systems Engineering Department University Politehnica Bucharest Romania Delft Center for Systems and Control Delft University of Technology The Netherlands Can Tho University of Technology Vietnam

ISBN: (纸本)9781467357159

In this paper we propose a model predictive control scheme for discrete-time linear time-invariant systems based on inexact numerical optimization algorithms. We assume that the solution of the associated quadratic program produced by some numerical algorithm is possibly neither optimal nor feasible, but the algorithm is able to provide estimates on primal suboptimality and primal feasibility violation. By tightening the complicating constraints we can ensure the primal feasibility of the approximate solutions generated by the algorithm. Finally, we derive a control strategy that has the following properties: the constraints on the states and inputs are satisfied, asymptotic stability of the closed-loop system is guaranteed, and the number of iterations needed for a desired level of suboptimality can be determined.

关键词： nonlinear model predictive control Tightening Predictive control control strategy control input optimization algorithms asymptotic stability region Closed loop systems Approximate iterative methods linear models Optimal control Constraint

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Fuzzy Robust and Non-fragile Minimax Control of a Trailer-Truck Model

Fuzzy Robust and Non-fragile Minimax Control of a Trailer-Tr...

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Proceedings of the 46th IEEE Conference on Decision and Control, Pages 3678-4134

作者： Yuanwei Jing Nan Jiang Yan Zheng Georgi M. Dimirovski Faculty of Information Science and Engineering Northeastern University Shenyang Liaoning China Faculty of Engineering Computer Engg.Dept Dogus University Istanbul Turkey

ISBN: (纸本)9781424414970;1424414970

This paper deals with a fuzzy robust and non-fragile minimax control problem of a trailer-truck model. By introducing parametric uncertainty terms into the T-S model for trailer-truck systems, the fuzzy model approaches to the original system more exactly. Existence conditions are derived for the robust and non-fragile minimax control in the sense of Lyapunov asymptotic stability and formulated in the form of Linear Matrix Inequalities (LMIs). The convex optimization algorithm is used to get the minimal upper bound of the performance cost and parameter of the optimal minimax controller. Then the closed-loop system will be asymptotically stable under the condition of the worst disturbance and uncertainty. Finally, an illustrative example is used to demonstrate the better robust and non-fragile performance of the controller design.

关键词： Minimax controller design fuzzy modeling Robustness Fuzzy optimization algorithms

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Localization of a moving non-cooperative RF target in NLOS environment using RSS and AOA measurements

Localization of a moving non-cooperative RF target in NLOS e...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： C. Cheng W. Hu W. P. Tay Sch. of Electr. & Electron. Eng. Nanyang Technol. Univ. Singapore Singapore

ISBN: (纸本)9781467369985

We propose an alternating optimization algorithm for localizing a mobile non-cooperative target using a wireless sensor network. We consider the scenario where sensors receive single-bounce non-line-of-sight signals from the moving target. Each sensor is able to measure the target signal's angle-of-arrival and received signal strength. The transmit powers of the non-cooperative target at different locations are unknown, and estimated jointly with its locations and the orientations of the scatterers off which the target signals are reflected before reaching the sensors. We formulate the problem as a non-convex least squares problem, and then transform and approximate it into a form that is solvable by an alternating algorithm. We show that our algorithm converges, and simulation results demonstrate that our algorithm is able to localize the target with good accuracy.

关键词： AOA Localization RSS alternating optimization wireless sensor network RSS gene Really Simple Syndication REACTOR SAFETY STUDY Wireless sensor networks ANGLE OF ATTACK Targets optimization algorithms Moving Sensors

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Quantum algorithms for optimizers

arXiv

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arXiv 2024年

作者： Nannicini, Giacomo

This is a set of lecture notes for a Ph.D.-level course on quantumalgorithms, with an emphasis on quantum optimization algorithms. It isdeveloped for applied mathematicians and engineers, and requires no previousbackground in quantum mechanics. The main topics of this course, in addition toa rigorous introduction to the computational model, are: input/output models,quantum search, the quantum gradient algorithm, matrix manipulation algorithms,the matrix multiplicative weights update framework for semidefiniteoptimization, adiabatic optimization. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Second-Order DMOC Using Projection

Second-Order DMOC Using Projection

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2010 49th IEEE Conference on Decision and Control

作者： Kristine L. Snyder Todd D. Murphey Department of Applied Mathematics 526 UCB University of Colorado Boulder CO 80309 Department of Mechanical Engineering Northwestern University 2145 Sheridan Road Evanston IL 60208 USA

ISBN: (纸本)9781424477456

Discrete mechanics and optimal control (DMOC) is a recent development in optimal control of mechanical systems that takes advantage of the variational structure of mechanics when discretizing the optimal control problem. Typically, the discrete Euler-Lagrange equations are used as constraints on the feasible set of solutions, and then the objective function is minimized using a constrained optimization algorithm, such as sequential quadratic programming (SQP). In contrast, this paper illustrates that by reducing dimensionality by projecting onto the feasible subspace and then performing optimization, one can obtain significant improvements in convergence, going from superlinear to quadratic convergence. Moreover, whereas numerical SQP can run into machine precision problems before terminating, the projection-based technique converges easily. Double and single pendulum examples are used to illustrate the technique.

关键词： Optimal control mechanic Mechanics Supralinearity Mechanical systems optimization algorithms Objective function Machine Precision Quadratic Convergence Converge sequential quadratic programming

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Better Balance by Being Biased: A 0.8776-Approximation for Max Bisection 13

Better Balance by Being Biased: A 0.8776-Approximation for M...

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Annual ACM-Society for Industrial and Applied Mathmatics Symposium on Discrete algorithms

作者： Per Austrin Siavosh Benabbas Konstantinos Georgiou Aalto University Department of Computer Science University of Toronto Department of Combinatorics & Optimization University of Waterloo

ISBN: (纸本)9781611972511

Recently Raghavendra and Tan (SODA 2012) gave a 0.85 approximation algorithm for the Max Bisection problem. We improve their algorithm to a 0.8776-approximation. As Max Bisection is hard to approximate within α_(GW) + ε ≈ 0.8786 under the Unique Games Conjecture (UGC), our algorithm is nearly optimal. We conjecture that Max Bisection is approximable within α_(GW)-ε, i.e., the bisection constraint (essentially) does not make Max Cut harder. We also obtain an optimal algorithm (assuming the UGC) for the analogous variant of MAX 2-Sat. Our approximation ratio for this problem exactly matches the optimal approximation ratio for MAX 2-Sat, i.e., α_(LLZ)+ε≈ 0.9401, showing that the bisection constraint does not make MAX 2-Sat harder. This improves on a 0:93-approximation for this problem due to Raghavendra and Tan.

关键词： Max Bisection Max BISECTION problem bisection constraint User generated content optimal approximation optimization algorithms tan Conjecture Biased algorithms Approximate

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