Discrete approximations for strict convex continuous time problems and duality

We propose a discrete approximation scheme to a class of Linear Quadratic Continuous Time Problems. It is shown, under positiveness of the matrix in the integral cost, that optimal solutions of the discrete problems provide a sequence of bounded variation functions which converges almost everywhere to the unique optimal solution. Furthermore, the method of discretization allows us to derive a number of interesting results based on finite dimensional optimization theory, namely, Karush-Kuhn-Tucker conditions of optimality and weak and strong duality. A number of examples are provided to illustrate the theory.8110

Higher Order Conditions in Nonlinear Optimal Control

The most widely used tool for the solution of optimal control problems is the Pontryagin Maximum Principle. But the Maximum Principle is, in general, only a necessary condition for optimality. It is therefore desirable to have supplementary conditions, for example second order sufficient conditions, which confirm optimality (at least locally) of an extremal arc, meaning one that satisfies the Maximum Principle. Standard second order sufficient conditions for optimality, when they apply, yield the information not only that the extremal is locally minimizing, but that it is also locally unique. There are problems of interest, however, where minimizers are not locally unique, owing to the fact that the cost is invariant under small perturbations of the extremal of a particular structure (translations, rotations or time-shifting). For such problems the standard second order conditions can never apply. The first contribution of this thesis is to develop new second order conditions for optimality of extremals which are applicable in some cases of interest when minimizers are not locally unique. The new conditions can, for example, be applied to problems with periodic boundary conditions when the cost is invariant under time translations. The second order conditions investigated here apply to normal extremals. These extremals satisfy the conditions of the Maximum Principle in normal form (with the cost multiplier taken to be 1). It is, therefore, of interest to know when the Maximum Principle applies in normal form. This issue is also addressed in this thesis, for optimal control problems that can be expressed as calculus of variations problems. Normality of the Maximum Principle follows from the fact that, under the regularity conditions developed, the highest time derivative of an extremal arc is essentially bounded. The thesis concludes with a brief account of possible future research directions

Analysis of Theoretical and Numerical Properties of Sequential Convex Programming for Continuous-Time Optimal Control

Sequential Convex Programming (SCP) has recently gained significant popularity as an effective method for solving optimal control problems and has been successfully applied in several different domains. However, the theoretical analysis of SCP has received comparatively limited attention, and it is often restricted to discrete-time formulations. In this paper, we present a unifying theoretical analysis of a fairly general class of SCP procedures for continuous-time optimal control problems. In addition to the derivation of convergence guarantees in a continuous-time setting, our analysis reveals two new numerical and practical insights. First, we show how one can more easily account for manifold-type constraints, which are a defining feature of optimal control of mechanical systems. Second, we show how our theoretical analysis can be leveraged to accelerate SCP-based optimal control methods by infusing techniques from indirect optimal control

Set-Valued Return Function and Generalized Solutions for Multiobjective Optimal Control Problems (MOC)

In this paper, we consider a multiobjective optimal control problem where the preference relation in the objective space is defined in terms of a pointed convex cone containing the origin, which defines generalized Pareto optimality. For this problem, we introduce the set-valued return function V and provide a unique characterization for V in terms of contingent derivative and coderivative for set-valued maps, which extends two previously introduced notions of generalized solution to the Hamilton-Jacobi equation for single objective optimal control problems.Comment: 29 pages, submitted to SICO

A Nonsmooth Augmented Lagrangian Method and its Application to Poisson Denoising and Sparse Control

In this paper, fully nonsmooth optimization problems in Banach spaces with finitely many inequality constraints, an equality constraint within a Hilbert space framework, and an additional abstract constraint are considered. First, we suggest a (safeguarded) augmented Lagrangian method for the numerical solution of such problems and provide a derivative-free global convergence theory which applies in situations where the appearing subproblems can be solved to approximate global minimality. Exemplary, the latter is possible in a fully convex setting. As we do not rely on any tool of generalized differentiation, the results are obtained under minimal continuity assumptions on the data functions. We then consider two prominent and difficult applications from image denoising and sparse optimal control where these findings can be applied in a beneficial way. These two applications are discussed and investigated in some detail. Due to the different nature of the two applications, their numerical solution by the (safeguarded) augmented Lagrangian approach requires problem-tailored techniques to compute approximate minima of the resulting subproblems. The corresponding methods are discussed, and numerical results visualize our theoretical findings.Comment: 36 pages, 4 figures, 1 tabl