222,144 research outputs found
Randomized Solutions to Convex Programs with Multiple Chance Constraints
The scenario-based optimization approach (`scenario approach') provides an
intuitive way of approximating the solution to chance-constrained optimization
programs, based on finding the optimal solution under a finite number of
sampled outcomes of the uncertainty (`scenarios'). A key merit of this approach
is that it neither assumes knowledge of the uncertainty set, as it is common in
robust optimization, nor of its probability distribution, as it is usually
required in stochastic optimization. Moreover, the scenario approach is
computationally efficient as its solution is based on a deterministic
optimization program that is canonically convex, even when the original
chance-constrained problem is not. Recently, researchers have obtained
theoretical foundations for the scenario approach, providing a direct link
between the number of scenarios and bounds on the constraint violation
probability. These bounds are tight in the general case of an uncertain
optimization problem with a single chance constraint. However, this paper shows
that these bounds can be improved in situations where the constraints have a
limited `support rank', a new concept that is introduced for the first time.
This property is typically found in a large number of practical
applications---most importantly, if the problem originally contains multiple
chance constraints (e.g. multi-stage uncertain decision problems), or if a
chance constraint belongs to a special class of constraints (e.g. linear or
quadratic constraints). In these cases the quality of the scenario solution is
improved while the same bound on the constraint violation probability is
maintained, and also the computational complexity is reduced.Comment: This manuscript is the preprint of a paper submitted to the SIAM
Journal on Optimization and it is subject to SIAM copyright. SIAM maintains
the sole rights of distribution or publication of the work in all forms and
media. If accepted, the copy of record will be available at
http://www.siam.or
Complexity of Bradley-Manna-Sipma Lexicographic Ranking Functions
In this paper we turn the spotlight on a class of lexicographic ranking
functions introduced by Bradley, Manna and Sipma in a seminal CAV 2005 paper,
and establish for the first time the complexity of some problems involving the
inference of such functions for linear-constraint loops (without precondition).
We show that finding such a function, if one exists, can be done in polynomial
time in a way which is sound and complete when the variables range over the
rationals (or reals). We show that when variables range over the integers, the
problem is harder -- deciding the existence of a ranking function is
coNP-complete. Next, we study the problem of minimizing the number of
components in the ranking function (a.k.a. the dimension). This number is
interesting in contexts like computing iteration bounds and loop
parallelization. Surprisingly, and unlike the situation for some other classes
of lexicographic ranking functions, we find that even deciding whether a
two-component ranking function exists is harder than the unrestricted problem:
NP-complete over the rationals and -complete over the integers.Comment: Technical report for a corresponding CAV'15 pape
Discrete-time output feedback sliding-mode control design for uncertain systems using linear matrix inequalities
An output feedback-based sliding-mode control design methodology for discrete-time systems is considered in this article. In previous work, it has been shown that by identifying a minimal set of current and past outputs, an augmented system can be obtained which permits the design of a sliding surface based upon output information only, if the invariant zeros of this augmented system are stable. In this work, a procedure for realising discrete-time controllers via a particular set of extended outputs is presented for non-square systems with uncertainties. This method is applicable when unstable invariant zeros are present in the original system. The conditions for existence of a sliding manifold guaranteeing a stable sliding motion are given. A procedure to obtain a Lyapunov matrix, which simultaneously satisfies both a Riccati inequality and a structural constraint, is used to formulate the corresponding control to solve the reachability problem. A numerical method using linear matrix inequalities is suggested to obtain the Lyapunov matrix. Finally, the design approach given in this article is applied to an aircraft problem and the use of the method as a reconfigurable control strategy in the presence of sensor failure is demonstrated
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