• Aucun résultat trouvé

Chance-constrained optimization

Numerical methods for hybrid control and chance-constrained optimization problems

Numerical methods for hybrid control and chance-constrained optimization problems

... initializing it with the solution of the deterministic counterpart (7.2.2) of problem (7.2.3) leads to a sequence converging to zero. In contrast to this, the same initialization for the KDE returns a very good ...

181

A Chance-Constrained Optimization Approach for Control of Transmission Voltages

A Chance-Constrained Optimization Approach for Control of Transmission Voltages

... Index Terms—Chance-constrained optimization (CCO), long-term voltage instability, voltage control. I. I NTRODUCTION T HE goal of voltage control is to keep system voltages within specified limits ...

9

Chance-Constrained Optimization of Reliable Fixed Broadband Wireless Networks

Chance-Constrained Optimization of Reliable Fixed Broadband Wireless Networks

... a chance-constrained programming approach to tackle this problem and we present reformula- tions to standard Integer Linear Programming (ILP) models, including a budget constrained ...

31

Solving chance constrained optimal control problems in aerospace via Kernel Density Estimation

Solving chance constrained optimal control problems in aerospace via Kernel Density Estimation

... to chance constrained optimization problems is not new (see for instance [29, 7] where this technique has been applied to an opti- mization problem in finite ...the optimization variables x ...

34

Chance constrained business case of a three-engines hybrid aircraft

Chance constrained business case of a three-engines hybrid aircraft

... a Chance Constrained Optimization of an unconventional config- uration of ...The Chance Constrained Methodology is applied using a method of uncertainty propagation based on error ...

11

Chance Constrained RRT for Probabilistic Robustness to Environmental Uncertainty

Chance Constrained RRT for Probabilistic Robustness to Environmental Uncertainty

... the chance constraint formu- lation developed in Blackmore et ...the chance-constrained optimization framework to consider other kinds of uncertainty, such as collision avoidance between ...

22

Chance-Constrained Outage Scheduling using a Machine Learning Proxy

Chance-Constrained Outage Scheduling using a Machine Learning Proxy

... University of Li`ege, L.Wehenkel@ulg.ac.be Abstract—Outage scheduling aims at defining, over a horizon of several months to years, when different components needing maintenance should be taken out of operation. Its ...

13

A Chance-Constrained Model & Cutting Planes for Fixed Broadband Wireless Networks

A Chance-Constrained Model & Cutting Planes for Fixed Broadband Wireless Networks

... a chance-constrained optimization model and, for the case where the outage probabilities are independent, an integer linear programming (ILP) formulation (Section ...

6

Chance-Constrained Optimal Path Planning With Obstacles

Chance-Constrained Optimal Path Planning With Obstacles

... Chance-Constrained Optimal Path Planning with Obstacles Lars Blackmore, Masahiro Ono and Brian ...a chance-constrained approach, which uses instead a prob- abilistic representation of ...vex ...

16

Chance Constrained Finite Horizon Optimal Control

Chance Constrained Finite Horizon Optimal Control

... ∧ 1 2 Pr[ C 2 ] 1 Fig. 3. The search tree of the branch and bound algorithm for the decomposed non-convex chance constraints (22) linear programming. For example, the relaxed problem at the middle left node in ...

9

Surrogate-based agents for constrained optimization

Surrogate-based agents for constrained optimization

... to optimization using a single agent that optimized in the entire design space for 100 iterations, which is approximately 100 expensive function evaluations, and for 100 ...

17

Differential stability of solutions to constrained optimization problems

Differential stability of solutions to constrained optimization problems

... L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r] ...

30

Inertial Game Dynamics and Applications to Constrained Optimization

Inertial Game Dynamics and Applications to Constrained Optimization

... an optimization viewpoint, we show that isolated maximizers of smooth functions defined on simplex- like objects are asymptotically stable; as a result, Nash equilibria that are potential maximizers are ...

29

Constrained optimization for hierarchical control system design

Constrained optimization for hierarchical control system design

... When the Basis Function Algorithm with time scale iterations was applied to the same constraints, it produced an efficient 8th order solution controller.. The basis was [r] ...

116

Globally convergent evolution strategies for constrained optimization

Globally convergent evolution strategies for constrained optimization

... global optimization in an heuristic sense. For bound-constrained problems, the implementation is enhanced by applying a search step, before the main ES one, based on the minimization of quadratic models ...

24

The cluster problem in constrained global optimization

The cluster problem in constrained global optimization

... the context of interval branch-and-bound algorithms for unconstrained global optimization. They established that the accuracy with which the bounding scheme estimates the range of the objective function, as ...

43

The cluster problem in constrained global optimization

The cluster problem in constrained global optimization

... the context of interval branch-and-bound algorithms for unconstrained global optimization. They established that the accuracy with which the bounding scheme estimates the range of the objective function, as ...

43

Globally convergent evolution strategies for constrained optimization

Globally convergent evolution strategies for constrained optimization

... as the average best value found by all solvers considered, but under a relatively low maximal computational budget, it makes some sense then to consider a high accuracy level (like 10 −7 or less). We now comment on the ...

25

A quasi-Riemannian approach to constrained optimization

A quasi-Riemannian approach to constrained optimization

... 6 Conclusions The development of a quasi-Riemannian method made in this report will permit to study the application of the BFGS algorithm in case of equality constraints that are not necessarily defined explicitly in ...

20

Constrained Consensus and Optimization in Multi-Agent Networks

Constrained Consensus and Optimization in Multi-Agent Networks

... subgradient algorithm, which for each agent involves a local averaging operation, a step along the subgradient of the local objective function, and a projection on the local constraint set. We study the convergence ...

18

Show all 3039 documents...

Sujets connexes