parametricprogramming has received a lot of attention in the control literature in the past few years because model predictive controllers (MPC) can be posed in a parametric framework and hence pre-solved offline, re...
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parametricprogramming has received a lot of attention in the control literature in the past few years because model predictive controllers (MPC) can be posed in a parametric framework and hence pre-solved offline, resulting in a significant decrease in on-line computation effort. In this paper we survey recent work on parametric linear programming (pLP) from the point of view of the control engineer. We identify three types of algorithms, two arising from standard convex hull paradigms and one from a geometric intuition, and classify all currently proposed methods tinder these headings. Through this classification, we identify a third standard convex hull approach that offers significant potential for approximation of pLPs for the purpose of control. We present the resulting algorithm, based on the beneath/beyond paradigm, that computes low-complexity approximate controllers that guarantee stability and feasibility.
Constrained finite time optimal control problems can be expressed as mathematical programs parameterized by the current state of the system:the so-called multi-parametric *** problems have received a great deal of att...
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ISBN:
(纸本)9787811243901
Constrained finite time optimal control problems can be expressed as mathematical programs parameterized by the current state of the system:the so-called multi-parametric *** problems have received a great deal of attention in the control community during the last few years because solving the parametric program is equivalent to synthesizing the optimal state-feedback *** many cases of interest,the resulting synthesized controllers are simple piecewise-affine functions,which enables receding horizon control to be used not only in slowly sampled systems requiring powerful computers but now also in high-speed embedded *** primary limitation of these optimal’explicit solutions’is that the complexity can grow quickly with problem *** this talk I will introduce new methods to compute approximate explicit and online control laws that can trade-off time and space complexity against sub-optimality while providing guarantees of stability and feasibility.
parametricprogramming has received a lot of attention in the control literature in the past few years because model predictive controllers (MPC) can be posed in a parametric framework and hence pre-solved offline, re...
详细信息
parametricprogramming has received a lot of attention in the control literature in the past few years because model predictive controllers (MPC) can be posed in a parametric framework and hence pre-solved offline, resulting in a significant decrease in on-line computation effort. In this paper we survey recent work on parametric linear programming (pLP) from the point of view of the control engineer. We identify three types of algorithms, two arising from standard convex hull paradigms and one from a geometric intuition, and classify all currently proposed methods tinder these headings. Through this classification, we identify a third standard convex hull approach that offers significant potential for approximation of pLPs for the purpose of control. We present the resulting algorithm, based on the beneath/beyond paradigm, that computes low-complexity approximate controllers that guarantee stability and feasibility.
This paper provides several useful support tools of power system scheduling and planning. First, we propose a fast method, which is used to evaluate the generating units and transmission capability of power system, an...
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This paper provides several useful support tools of power system scheduling and planning. First, we propose a fast method, which is used to evaluate the generating units and transmission capability of power system, and calculate the optimal reserve of each demand side, based on two-step linearprogramming technique. Then, a new support tool of power system planning is described by using the parametric linear programming. The proposed support tool does not only fast give a optimal reserve curve of the system, but also provides some diagnosis information of system weak location for the expansion planning and maintenance scheduling decision of transmission lines or generating units. Third, we take into account the outages of generators and transmission lines, and show a possible method to estimate the reliability(LOLP) of multi-area power system by making use of linearprogramming. Several examples have been used to verify the proposed methodology and illustrate the application. The results show that our approaches are very simple, fast and efficient for the generation and transmission capability evaluation or the contingency test of power system as support tools.
In this paper a new method for generating the set of efficient extreme-points for the three-objective linearprogramming problem is presented. This method successfully exploits the fact that vectors that are strictly ...
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In this paper a new method for generating the set of efficient extreme-points for the three-objective linearprogramming problem is presented. This method successfully exploits the fact that vectors that are strictly efficient in associated multi-objective problems of lower dimensions are also efficient in the full multi-objective problem.
We consider statistical procedures for feature selection defined by a family of regularization problems with convex piecewise linear loss functions and penalties of l (1) nature. Many known statistical procedures (e.g...
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We consider statistical procedures for feature selection defined by a family of regularization problems with convex piecewise linear loss functions and penalties of l (1) nature. Many known statistical procedures (e.g. quantile regression and support vector machines with l (1)-norm penalty) are subsumed under this category. Computationally, the regularization problems are linearprogramming (LP) problems indexed by a single parameter, which are known as 'parametric cost LP' or 'parametric right-hand-side LP' in the optimization theory. Exploiting the connection with the LP theory, we lay out general algorithms, namely, the simplex algorithm and its variant for generating regularized solution paths for the feature selection problems. The significance of such algorithms is that they allow a complete exploration of the model space along the paths and provide a broad view of persistent features in the data. The implications of the general path-finding algorithms are outlined for several statistical procedures, and they are illustrated with numerical examples.
Many classes of mathematical programming problems can be formulated as a linear program with a parametric objective function. Gass and Saaty developed in the early 1950's a parametricprogramming procedure for sol...
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Many classes of mathematical programming problems can be formulated as a linear program with a parametric objective function. Gass and Saaty developed in the early 1950's a parametricprogramming procedure for solving the latter problem. This procedure is relevant to solution strategies for many problem types but is underutilized, often because the relationship of the procedure to the relevant problem class is not recognized. In this paper five recent applications of the parametricprogramming procedure are presented.
We consider a linearprogramming problem, with two parameters in the objective function, and present an algorithm for finding the decomposition of the parameter space into maximal polyhedral areas in which particular ...
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The link between linear model predictive control (MPC) and parametriclinear/quadratic programming has reached maturity in terms of the characterization of the structural properties and the numerical methods available...
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ISBN:
(纸本)9783952426937
The link between linear model predictive control (MPC) and parametriclinear/quadratic programming has reached maturity in terms of the characterization of the structural properties and the numerical methods available for the effective resolution. The computational complexity is one of the current bottlenecks for these control design methods and inverse optimality has been recently shown to provide a new perspective for this challenge. However, the question of the minimal complexity of inverse optimality formulation is still open and much under discussion. In this paper we revisit some recent results by pointing out unnecessary geometrical complications which can be avoided by the interpretation of the optimality conditions. Two algorithms for fine-tuning inverse optimality formulation will be proposed and the results will be interpreted via two illustrative examples in comparison with existing formulations.
A new zero-one integer programming model for the job shop scheduling problem with minimum makespan criterion is presented. The algorithm consists of two parts: (a) a branch and bound parametric linear programming code...
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