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IEEE International Conference on Control Applications Part of 2010 IEEE Multi-Conference on Systems and Control

作者： Yu, Shuyou Boehm, Christoph Chen, Hong Allgoewer, Frank Univ Stuttgart Inst Syst Theory & Automat Control D-7000 Stuttgart Germany Jilin Univ State Key Lab Autumobile Dynam Simulat Changchun 130122 Peoples R China

ISBN: (纸本)9781424453634

Based on the assumption that the parameter can be measured in real time, we propose a model predictive control (MPC) method for linear-parameter varying (LPV) systems subject to possibly asymmetric constraints which adopts the analogous framework of terminal control law, terminal set and terminal penalty of nonlinear model predictive control. The optimization problem is formulated as a convex optimization problem and, recursive feasibility and closed-loop stability are guaranteed by its feasibility at initial time. For LPV systems with symmetric constraints, we reformulate the convex optimization problem as a semi-definite program. Numerical examples demonstrate the properties of the proposed MPC design.

关键词： MPC asymmetric constraints closed loop systems closed-loop stability constrained LPV systems convex optimization problem convex programming free control action linear-parameter varying nonlinear control systems nonlinear model predictive control predictive control semi-definite program stability terminal control law

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Robust Joint Transceiver Power Allocation for Multi-User Downlink MIMO Transmissions

Robust Joint Transceiver Power Allocation for Multi-User Dow...

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12th International Conference on Advanced Communication Technology (ICACT) - ICT for Green Growth and Sustainable Development

作者： Kotchasarn, Chirawat Rajamangala Univ Technol Thanyaburi Khlong Hok Thailand

ISBN: (纸本)9788955191455

We consider the problem of robust joint transmitter and receiver power allocation for downlink multiple input multiple output (MIMO) transmissions. The channel model is assumed to be Rayleigh frequency flat fading. The objective of power allocation is to minimize the total mean square error under total transmit power constraint. Under robustness issue, we consider joint power allocation with imperfect channel state information (CSI), where the CSI error is assumed to have Gaussian distribution. We show that this problem is formulated as a convex optim ization problem. Numerical results indicate the BER performance improvement obtained by considering the robustness into account in the joint power allocation process.

关键词： robust power allocation downlink convex optimization problem MIMO

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A tutorial on convex optimization

A tutorial on convex optimization

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American Control Conference

作者： Hindi, H PARC Palo Alto CA USA

ISBN: (纸本)0780383354

In recent years, convex optimization has become a computational tool of central importance in engineering, thanks to it's ability to solve very large, practical engineering problems reliably and efficiently. The goal of this tutorial is to give an overview of the basic concepts of convex sets, functions and convex optimization problems, so that the reader can more readily recognize and formulate engineering problems using modern convex optimization. This tutorial coincides with the publication of the new book on convex optimization, by Boyd and Vandenberghe [7], who have made available a large amount of free course material and links to freely available code. These can be downloaded and used immediately by the audience both for self-study and to solve real problems.

关键词： convex programming set theory tutorial publication convex sets convex functions convex optimization problem

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Robust classification of noisy data using second order cone programming approach

Robust classification of noisy data using second order cone ...

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International Conference on Intelligent Sensing and Information Processing

作者： Bhattacharyya, C Indian Inst Sci Dept Comp Sci & Automat Bangalore 560012 Karnataka India

ISBN: (纸本)0780382439

Assuming an ellipsoidal model of uncertainty a robust formulation for classifying noisy data is presented. The formulation is a convex optimization problem, in particular it is a instance of Second Order Cone Programmimg problem. The formulation is derived from a worst case consideration and the robustness properties hold for a large class of distributions. The equivalence of ellipsoidal uncertainty and Gaussian noise models is also discussed. The Generalized Optimal hyperplane is recovered as a special case of the robust formulation. Experiments on real world datasets illustrates the efficacy of the formulation.

关键词： convex programming pattern classification Gaussian noise support vector machines robust classification second order cone programming ellipsoidal uncertainty model convex optimization problem Gaussian noise models generalized optimal hyperplane robust formulation robustness Support Vector Network Gaussian noise convex programming noisy data Pattern recognition Noise models Robustness convex function

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Efficient algorithms for the inverse spanning-tree problem

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OPERATIONS RESEARCH 2003年第5期51卷 785-797页

作者： Hochbaum, DS Univ Calif Berkeley Dept Ind Engn & Operat Res Walter A Haas Sch Business Berkeley CA 94720 USA

The inverse spanning-tree problem is to modify edge weights in a graph so that a given tree T is a minimum spanning tree. The objective is to minimize the cost of the deviation. The cost of deviation is typically a convex function. We propose algorithms here that are faster than all known algorithms for the problem. Our algorithm's run time for any convex inverse spanning-tree problem is O(nm log(2) n) for a graph on n nodes and m edges plus the time required to find the minima of the n convex deviation functions. This not only improves on the complexity of previously known algorithms for the general problem, but also for algorithms devised for special cases. For the case when the objective is weighted absolute deviation, Sokkalingam et al. (1999) devised an algorithm with run time O(n(2)m log(nC)) for C the maximum edge weight. For the sum of absolute deviations our algorithm runs in time O(n(2) log n), matching the run time of a recent (Ahuja and Orlin 2000) improvement for this case. A new algorithm for bipartite matching in path graphs is reported here with complexity of O(n(1.5) log n). This leads to a second algorithm for the sum of absolute deviations running in O(n(1.5) log n log C) steps.

关键词： Programming: integer algorithms optimization in integers Network/graphs: flow algorithms parametric minimum cut Mathematics: convexity convex optimization problem

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On a proximal point method for convex optimization in Banach spaces

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NUMERICAL FUNCTIONAL ANALYSIS AND optimization 1997年第7-8期18卷 723-744页

作者： Butnariu, D Iusem, AN UNIV HAIFA DEPT MATH & COMP SCIIL-31905 HAIFAISRAEL INST MATEMATICA PURA & APLICADA BR-22460320 RIO JANEIROBRAZIL

We analyze the behavior of a parallel proximal point method for solving convex optimization problems in reflexive Banach spaces. Similar algorithms were known to converge under the implicit assumption that the norm of the space is Hilbertian. We extend the area of applicability of the proximal point method to solving convex optimization problems in Banach spaces on which totally convex functions can be found. This includes the class of all smooth uniformly convex Banach spaces. Also, our convergence results leave more flexibility for the choice of the penalty function involved in the algorithm and, in this way, allow simplification of the computational procedure.

关键词： convex optimization problem proximal point method totally convex function Bregman distance Bochner integral uniformly convex Banach space

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On the stability domain estimation via a quadratic Lyapunov function: convexity and optimality properties for polynomial systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1996年第11期41卷 1650-1657页

作者： Tesi, A Villoresi, F Genesio, R Dipartimento di Sistemie Informatica Università di Firenze Florence Italy

The problem of estimating the stability domain of the origin of an n-order polynomial system is considered. Exploiting the structure of this class of systems it is shown that, for a given quadratic Lyapunov function, an estimate of the stability domain can be obtained by solving a suitable convex optimization problem. This estimate is shown to be optimal for an important subclass including both quadratic and cubic systems, and its accuracy in the general polynomial case is discussed via several examples.

关键词： Lyapunov methods convex programming nonlinear programming polynomials stability stability criteria convex optimization problem cubic systems polynomial systems quadratic Lyapunov function quadratic systems stability domain estimation Lyapunov methods convex programming quadratic system convex function stability region cubic system Liapunov function Polynomial Quadratic Nonlinear Programming Stability criteria

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CONSTRAINED convex-optimization problemS - WELL-POSEDNESS AND STABILITY

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NUMERICAL FUNCTIONAL ANALYSIS AND optimization 1994年第7-8期15卷 889-907页

作者： KONSULOVA, AS REVALSKI, JP BULGARIAN ACAD SCI INST MATHBU-1113 SOFIABULGARIA

We study convex minimization problems in a Banach space with inequality constraints. Different notions of well-posedness (i.e. uniqueness of the solution and its stability) of such kind of problems are introduced and investigated, including relations between them. The notions and results are generalized for the case when the requirement for uniqueness of the solution is dropped.

关键词： convex FUNCTIONS convex optimization problem WELL-POSED optimization problem AW-TOPOLOGY STABILITY

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CONSTRUCTION OF SELECTION INDEXES WITH RESTRICTIONS

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BIOMETRICAL JOURNAL 1984年第1期26卷 3-12页

作者： MIELENZ, N POULTRY RES INST MERBITZGER DEM REP

AbstractIn this paper the problem of unistage selection with inequality constraints is formulated. If the predictor and criterion variables are all normally distributed, this problem can be written as a convex programming problem, with a linear objective function and with linear constraints and a quadratic constraint. Using the duality theory, for convex nonlinear programming it is proved, that its dual problem can be transformed into a convex minimization problem with non‐negativity conditions. Good computational methods are known for solving this problem. By the help of the dual problem sufficient conditions for a solution of the original primal problem are derived and illustrated by an example of practical interes

关键词： Selection index inequality constraints convex optimization problem duality theory

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