The Bayesian paradigm offers principled tools for sequential decision-making under uncertainty, but its reliance on a probabilistic model for all parameters can hinder the incorporation of complex structural constraints. We introduce a minimalist Bayesian framework that places a prior only on the component of interest, such as the location of the optimum. Nuisance parameters are … Read more
In mathematical optimization, we want to find the best possible solution for a decision-making problem. Curiously, these problems are harder to solve if they have discrete decisions. Imagine that you would like to buy chocolate: you can buy no chocolate or one chocolate bar, but typically you cannot buy just half of a bar. Now … Read more
Wasserstein distributionally robust optimization (DRO), a leading paradigm in data-driven decision-making, requires evaluating worst-case risk over a high-dimensional Wasserstein ball. We study when this worst-case evaluation admits an exact reduction to a one-dimensional formulation, in the sense that it can be carried out over a one-dimensional Wasserstein ball centered at the projected reference distribution. We … Read more
A widely used approximation concept in multiobjective optimization is the concept of enclosures. These are unions of boxes defined by lower and upper bound sets that are used to cover optimal sets of multiobjective optimization problems in the image space. The width of an enclosure is taken as a quality measure. In this paper, we … Read more
Hypervolume is most likely the most often used set quality indicator in (evolutionary) multi-objective optimization. It may be used to compare the quality of solution sets whose images in the objective space are approximations of the true Pareto front. Although in this way we may compare two or more approximations, our knowledge is limited without … Read more
In the Euclidean space \(\mathbb{R}^3\), we ask whether one can visit each of the \(27\) vertices of the grid \(G_3:=\{0,1,2\}^3\) exactly once using as few straight-line segments, connected end to end, as possible (an optimal polygonal chain). We give a constructive proof that there exists a \(13\)-segment perfect simple path (i.e., an optimal chain that … Read more
With e-commerce expanding rapidly, last-mile delivery challenges have been exacerbated, necessitating innovative logistics to reduce operational costs and improve delivery speed. This paper investigates a traveling salesman problem with drone stations, where a truck collaborates with multiple drones docked at candidate drone stations to serve customers. In contrast to existing studies that typically assume fixed … Read more
This paper introduces a generalized isotonic optimization framework over an arborescence graph, where each node incurs state-dependent convex costs and a fixed cost upon strict increases. We begin with the special case in which the arborescence is a path and develop a dynamic programming (DP) algorithm with an initial complexity of $O(n^3)$, which we improve … Read more
This paper presents a novel hybrid algorithm for minimizing the sum of a continuously differentiable loss function and a nonsmooth, possibly nonconvex, sparsity‑promoting regularizer. The proposed method adaptively switches between solving a reweighted \(\ell_1\)-regularized subproblem and performing an inexact subspace Newton step. The reweighted \(\ell_1\)-subproblem admits an efficient closed-form solution via the soft-thresholding operator, thereby … Read more
Current nonlinear model predictive control (NMPC) strategies are formulated as finite predictive horizon nonlinear programs (NLPs), which maintain NMPC stability and recursive feasibility through the construction of terminal cost functions and/or terminal constraints. However, computing these terminal properties may pose formidable challenges with a fixed horizon, particularly in the context of nonlinear dynamic processes. Motivated … Read more