We study the convergence rate of the moment-SOS (sum-of-squares) hierarchy for polynomial optimization problems (POPs) on a bounded subset of the real line described by arbitrary polynomial inequalities. We prove that, for every fixed univariate POP, the relaxation error is bounded by $O(1/r^2)$, where $r$ is the relaxation order. In particular, boundary degeneracies in the … Read more
Consider a discrete optimal transport problem that has at least two consumers. We show that the Monge and Kantorovich versions of such a discrete optimal transport problem are equivalent for all cost functions if and only if the supply from all the suppliers are equal, and the demand from every consumer is an integral multiple … Read more
For integer activities, conditioning on the support makes a fixed-charge objective affine. If every support-conditioned cell is integral, an optimal solution is a vertex of its cell for arbitrary fixed charges and marginal rates. A totally unimodular constraint matrix with integral data guarantees this condition. This surviving property is weaker than the classical conclusions. A … Read more
The symmetry measure of a convex body \(S\subset\mathbb{R}^n\) is given by: \[ \mathrm{sym}(S):=\max\{\alpha\ge0:\text{ there exists }x\in S\text{ such that } -\alpha(S-x)\subseteq S-x\}\,, \]where such an \(x\) is called a Minkowski center. We prove that every convex body \(S\) admits a \(\sqrt{\frac{n}{\mathrm{sym}(S)}}\)-rounding of \(S\), namely, there exists an origin-centered ellipsoid \(E\) and a center \(c\) such … Read more
In this paper, we develop a systematic framework for deriving explicit error bounds for semidefinite feasibility problems without assuming strict feasibility (Slater’s condition), a setting in which existing results are limited. Our main technical contribution is the introduction of radial-type H\”{o}lder error bounds, where the error bound constant depends explicitly on the norm of the … Read more
In this paper, we propose a unified framework for accelerated gradient methods, dubbed UGM, which subsumes a wide range of accelerated and conventional gradient-type methods designed for minimizing $L$-smooth and $\mu$-strongly convex functions. We demonstrate that the iteration update of the proposed framework can be intrinsically interpreted as a hybrid combination of the heavy-ball method … Read more
We classify the facial exposedness of completely positive cones over symmetric cones in terms of the rank of the associated Euclidean Jordan algebras. The completely positive cones are facially exposed when the rank is at most $2$, but are not facially exposed when the rank is at least $5$. Facial exposedness is not completely determined … Read more
In this paper, we propose new inexact adaptive proximal gradient algorithms for solving nonconvex composite optimization problems, where the objective is the sum of a differentiable nonconvex function and a convex non-differentiable function. A new relative error criterion to compute the proximal operator inexactly has been proposed together with new adaptive strate- gies for selecting … Read more
Modern supply chains differ in lead times, bottlenecks, and demand volatility. Advanced Planning Systems conventionally decouple Master Planning (MP) and Demand Fulfillment (DF) hierarchically, which improves tractability but ignores critical interdependencies between material availability, capacity utilization, and delivery promising. We propose an integrated MP‑DF model, formulate it as a Minimum‑Cost Multicommodity Flow (MCMCF) on a … Read more
Feature-based newsvendor models use observable covariates to tailor inventory decisions, aiming to balance holding and shortage costs under demand uncertainty. However, high-dimensional feature sets often hinder interpretability and inflate data collection and implementation costs. This paper studies variable selection for the feature-based newsvendor problem under a hard cardinality constraint on the number of selected features. … Read more