6,931 research outputs found
Strong Stationarity Conditions for Optimal Control of Hybrid Systems
We present necessary and sufficient optimality conditions for finite time
optimal control problems for a class of hybrid systems described by linear
complementarity models. Although these optimal control problems are difficult
in general due to the presence of complementarity constraints, we provide a set
of structural assumptions ensuring that the tangent cone of the constraints
possesses geometric regularity properties. These imply that the classical
Karush-Kuhn-Tucker conditions of nonlinear programming theory are both
necessary and sufficient for local optimality, which is not the case for
general mathematical programs with complementarity constraints. We also present
sufficient conditions for global optimality.
We proceed to show that the dynamics of every continuous piecewise affine
system can be written as the optimizer of a mathematical program which results
in a linear complementarity model satisfying our structural assumptions. Hence,
our stationarity results apply to a large class of hybrid systems with
piecewise affine dynamics. We present simulation results showing the
substantial benefits possible from using a nonlinear programming approach to
the optimal control problem with complementarity constraints instead of a more
traditional mixed-integer formulation.Comment: 30 pages, 4 figure
Differential-Algebraic Equations and Beyond: From Smooth to Nonsmooth Constrained Dynamical Systems
The present article presents a summarizing view at differential-algebraic
equations (DAEs) and analyzes how new application fields and corresponding
mathematical models lead to innovations both in theory and in numerical
analysis for this problem class. Recent numerical methods for nonsmooth
dynamical systems subject to unilateral contact and friction illustrate the
topicality of this development.Comment: Preprint of Book Chapte
Network Cournot Competition
Cournot competition is a fundamental economic model that represents firms
competing in a single market of a homogeneous good. Each firm tries to maximize
its utility---a function of the production cost as well as market price of the
product---by deciding on the amount of production. In today's dynamic and
diverse economy, many firms often compete in more than one market
simultaneously, i.e., each market might be shared among a subset of these
firms. In this situation, a bipartite graph models the access restriction where
firms are on one side, markets are on the other side, and edges demonstrate
whether a firm has access to a market or not. We call this game \emph{Network
Cournot Competition} (NCC). In this paper, we propose algorithms for finding
pure Nash equilibria of NCC games in different situations. First, we carefully
design a potential function for NCC, when the price functions for markets are
linear functions of the production in that market. However, for nonlinear price
functions, this approach is not feasible. We model the problem as a nonlinear
complementarity problem in this case, and design a polynomial-time algorithm
that finds an equilibrium of the game for strongly convex cost functions and
strongly monotone revenue functions. We also explore the class of price
functions that ensures strong monotonicity of the revenue function, and show it
consists of a broad class of functions. Moreover, we discuss the uniqueness of
equilibria in both of these cases which means our algorithms find the unique
equilibria of the games. Last but not least, when the cost of production in one
market is independent from the cost of production in other markets for all
firms, the problem can be separated into several independent classical
\emph{Cournot Oligopoly} problems. We give the first combinatorial algorithm
for this widely studied problem
Generalized Newton's Method based on Graphical Derivatives
This paper concerns developing a numerical method of the Newton type to solve
systems of nonlinear equations described by nonsmooth continuous functions. We
propose and justify a new generalized Newton algorithm based on graphical
derivatives, which have never been used to derive a Newton-type method for
solving nonsmooth equations. Based on advanced techniques of variational
analysis and generalized differentiation, we establish the well-posedness of
the algorithm, its local superlinear convergence, and its global convergence of
the Kantorovich type. Our convergence results hold with no semismoothness
assumption, which is illustrated by examples. The algorithm and main results
obtained in the paper are compared with well-recognized semismooth and
-differentiable versions of Newton's method for nonsmooth Lipschitzian
equations
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