arXiv subjects

Bethany I. Caldwell

Publications and source records attributed to Bethany I. Caldwell.

6 recordsLinked to original sources

arXiv2026

Best Approximation Optimal Control for Infeasible Double Integrator and Douglas--Rachford Algorithm

We consider the problem of finding (in some sense) the best approximation control for an infeasible double integrator. The control function is constrained by upper and lower bounds that are too tight and thus cause infeasibility. The infeasibility is characterized by a gap function (representing the separation between two constraint sets) whose squared ${\cal L}^2$-norm is to be minimized to find the best approximation control solution. First, we review the existing results for problems involving a general linear control system. Then, for the infeasible double integrator problem, we present an analytical solution for the bang--bang control with at most one switching. The infinite-dimensional optimization problem is reduced to the problem of solving two algebraic equations in two variables, to compute the switching time and gap function. We discuss numerical approaches to solving the system of equations. Finally, we describe the (relaxed) Douglas--Rachford algorithm for the double integrator problem and carry out numerical experiments to illustrate the implementation of the algorithm and test performance.

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arXiv2026

Interwoven SDP in Primal-Dual Proximal Splitting Methods for Adjustable Robust Convex Optimisation with SOS-Convex Polynomial Constraints

We propose a novel methodology for solving a two-stage adjustable robust convex optimisation problem with a general (proximable) convex objective function and constraints defined by sum-of-squares (SOS) convex polynomials. These problems appear in many decision-making applications. However, they are challenging to solve and typically cannot be reformulated as numerically tractable convex optimisation models, such as conic linear programs, that can be solved directly using existing software. We show that the robust problem admits an equivalent representation as a convex composite unconstrained optimisation model that preserves the same objective values, under quadratic decision rules on the adjustable decision variables. Building on this reformulation, we develop a tailored first-order primal-dual proximal splitting method. By leveraging semidefinite programming (SDP) techniques as well as tools from convex analysis and real algebraic geometry, we establish its theoretical properties, including computable SDP-based formulas for projections onto closed convex sets, specified by SOS-convex polynomial inequalities. Numerical experiments on a two-stage lot-sizing model with both linear as well as SOS-convex polynomial storage costs under demand uncertainty demonstrate the effectiveness and applicability of the proposed approach. Our approach enables the incorporation of SDP techniques into a primal-dual proximal splitting framework, thereby broadening the class of problems to which these methods can be effectively applied.

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arXiv2024

On the Douglas-Rachford and Peaceman-Rachford algorithms in the presence of uniform monotonicity and the absence of minimizers

The Douglas-Rachford and Peaceman-Rachford algorithms have been successfully employed to solve convex optimization problems, or more generally find zeros of monotone inclusions. Recently, the behaviour of these methods in the inconsistent case, i.e., in the absence of solutions has triggered significant consideration. It has been shown that under mild assumptions the shadow sequence of the Douglas-Rachford algorithm converges weakly to a generalized solution when the underlying operators are subdifferentials of proper lower semicontinuous convex functions. However, no convergence behaviour has been proved in the case of Peaceman-Rachford algorithm. In this paper, we prove the convergence of the shadow sequences associated with the Douglas-Rachford algorithm and Peaceman-Rachford algorithm when one of the operators is uniformly monotone and $3^*$ monotone but not necessarily a subdifferential. Several examples illustrate and strengthen our conclusion. We carry out numerical experiments using example instances of optimization problems.

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arXiv2024

Douglas-Rachford Algorithm for Control- and State-constrained Optimal Control Problems

We consider the application of the Douglas-Rachford (DR) algorithm to solve linear-quadratic (LQ) control problems with box constraints on the state and control variables. We split the constraints of the optimal control problem into two sets: one involving the ODE with boundary conditions, which is affine, and the other a box. We rewrite the LQ control problems as the minimization of the sum of two convex functions. We find the proximal mappings of these functions which we then employ for the projections in the DR iterations. We propose a numerical algorithm for computing the projection onto the affine set. We present a conjecture for finding the costates and the state constraint multipliers of the optimal control problem, which can in turn be used in verifying the optimality conditions. We carry out numerical experiments with two constrained optimal control problems to illustrate the working and the efficiency of the DR algorithm compared to the traditional approach of direct discretization.

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arXiv2024

Douglas--Rachford algorithm for control-constrained minimum-energy control problems

Splitting and projection-type algorithms have been applied to many optimization problems due to their simplicity and efficiency, but the application of these algorithms to optimal control is less common. In this paper we utilize the Douglas--Rachford (DR) algorithm to solve control-constrained minimum-energy optimal control problems. Instead of the traditional approach where one discretizes the problem and solves it using large-scale finite-dimensional numerical optimization techniques we split the problem in two subproblems and use the DR algorithm to find an optimal point in the intersection of the solution sets of these two subproblems hence giving a solution to the original problem. We derive general expressions for the projections and propose a numerical approach. We obtain analytic closed-form expressions for the projectors of pure, under-, critically- and over-damped harmonic oscillators. We illustrate the working of our approach to solving not only these example problems but also a challenging machine tool manipulator problem. Through numerical case studies, we explore and propose desirable ranges of values of an algorithmic parameter which yield smaller number of iterations.

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arXiv2021

A Generalized Multivariable Newton Method

It is well known that the Newton method may not converge when the initial guess does not belong to a specific quadratic convergence region. We propose a family of new variants of the Newton method with the potential advantage of having a larger convergence region as well as more desirable properties near a solution. We prove quadratic convergence of the new family, and provide specific bounds for the asymptotic error constant. We illustrate the advantages of the new methods by means of test problems, including two and six variable polynomial systems, as well as a challenging signal processing example. We present a numerical experimental methodology which uses a large number of randomized initial guesses for a number of methods from the new family, in turn providing advice as to which of the methods employed is preferable to use in a particular search domain.

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