Optimization and Control (math.OC)

Abstract: A new approach for the construction of separable approximations of optimal value functions from interconnected optimal control problems is presented. The approach is based on assuming decaying sensitivities between subsystems, enabling a curse-of-dimensionality free approximation, for instance by deep neural networks.

Abstract: We present an analysis and numerical study of an optimal control problem for the Landau-de Gennes (LdG) model of nematic liquid crystals (LCs), which is a crucial component in modern technology. They exhibit long range orientational order in their nematic phase, which is represented by a tensor-valued (spatial) order parameter $Q = Q(x)$. Equilibrium LC states correspond to $Q$ functions that (locally) minimize an LdG energy functional. Thus, we consider an $L^2$-gradient flow of the LdG energy that allows for finding local minimizers and leads to a semi-linear parabolic PDE, for which we develop an optimal control framework. We then derive several a priori estimates for the forward problem, including continuity in space-time, that allow us to prove existence of optimal boundary and external ``force'' controls and to derive optimality conditions through the use of an adjoint equation. Next, we present a simple finite element scheme for the LdG model and a straightforward optimization algorithm. We illustrate optimization of LC states through numerical experiments in two and three dimensions that seek to place LC defects (where $Q(x) = 0$) in desired locations, which is desirable in applications.

Abstract: 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.

Abstract: In this paper, we consider Tsallis entropic regularized optimal transport and discuss the convergence rate as the regularization parameter $\varepsilon$ goes to $0$. In particular, we establish the convergence rate of the Tsallis entropic regularized optimal transport using the quantization and shadow arguments developed by Eckstein--Nutz. We compare this to the convergence rate of the entropic regularized optimal transport with Kullback--Leibler (KL) divergence and show that KL is the fastest convergence rate in terms of Tsallis relative entropy.

Abstract: In many safety-critical optimal control problems, users may request multiple safety constraints that are jointly infeasible due to external factors such as subsystem failures, unexpected disturbances, or fuel limitations. In this manuscript, we introduce the concept of blameless optimality to characterize control actions that a) satisfy the highest prioritized and feasible safety constraints and b) remain optimal with respect to a mission objective. For a general optimal control problem with jointly infeasible safety constraints, we prove that a single optimization problem cannot find a blamelessly optimal controller. Instead, finding blamelessly optimal control actions requires sequentially solving at least two optimal control problems: one to determine the highest priority level of constraints that is feasible and another to determine the optimal control action with respect to these constraints. We apply our results to a helicopter emergency landing scenario in which violating at least one safety-induced landing constraint is unavoidable. Leveraging the concept of blameless optimality, we formulate blamelessly optimal controllers that can autonomously prioritize human safety over property integrity.

Abstract: In multiple criteria decision aiding, very often the alternatives are compared by means of a value function compatible with the preferences expressed by the Decision Maker. The problem is that, in general, there is a plurality of compatible value functions, and providing a final recommendation on the problem at hand considering only one of them could be considered arbitrary to some extent. For such a reason, Stochastic Multicriteria Acceptability Analysis gives information in statistical terms by taking into account a sample of models compatible with the provided preferences. These statistics are given assuming the existence of a probability distribution in the space of value functions being defined a priori. In this paper, we propose some methods aiming to build a probability distribution on the space of value functions considering the preference information given by the Decision Maker. To prove the goodness of our proposal we performed an extensive set of simulations. Moreover, a sensitivity analysis on the variables of our procedure has been done as well.

Abstract: The problem of recovering the topology and parameters of an electrical network from power and voltage data at all nodes is a problem of fitting both an algebraic variety and a graph which is often ill-posed. In case there are multiple electrical networks which fit the data up to a given tolerance, we seek a solution in which the graph and therefore the algebraic equations associated with the electrical network are sparse, i.e. with few edges and terms. From an applied point of view, frequently it is difficult for system operators to know the precise information of the network. On the other hand, improvements on measurement devices increasingly provide more data about voltage and power, so it is useful to use this amount of data to estimate the network. We propose an algorithm for recovering simultaneously a sparse topology and the cable parameters of any network, combining in an iterative procedure the resolution of algebraic fitting convex problems and techniques of spectral graph sparsification. The algorithm is tested on several electrical networks.