The goal of this repository is to demonstrate the efficiency of our algorithm for Minimum-Weight ( 1 , m ) --Connected Dominating Set.

Source codes and data set used in “On interdicting dense clusters in a network” paper. Data set contains 20 randomly-generated networks and 38 real-life networks. Each file contains 1+X+Y lines, where X and Y are the number of vertices and the number of edges in the network, respectively. It starts with two numbers indicating the number of vertices X and the number of edges Y. Then, it follows with X lines of two numbers. Note that we do not assign indices to the vertices. The index of a vertex is the same as the line number it appears on. The first number in each line is the weight of a vertex and the second number is its blocking cost. The last Y lines contain three numbers. The first and second numbers in each line are the head and the tail of an edge. Note that the edges are undirected. The third number is the blocking cost of an edge.

The goal of this repository to be able to trace and reproduce the results and plots in the referenced article. For the results in this article, we used the general purpose integer programming solver Gurobi version 10.0.0. In addition, you need to have the python packages pandas, numpy, scipy, and pybind11 installed to be able to build the code for computing the weights of the cutting planes from 5.1.1.

We analyze integer linear programs that we obtain after discretizing two-dimensional subproblems arising from a trust-region algorithm for mixed integer optimal control problems with total variation regularization. We discuss NP-hardness of the discretized problems and the connection to graph-based problems. We show that the underlying polyhedron exhibits structural restrictions in its vertices with regard to which variables can attain fractional values at the same time. Based on this property, we derive cutting planes by employing a relation to shortest-path and minimum bisection problems. We propose a branching rule and a primal heuristic which improves previously found feasible points. We validate the proposed tools with a numerical benchmark in a standard integer programming solver. We observe a significant speedup for medium-sized problems. Our results give hints for scaling toward larger instances in the future. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by the Deutsche Forschungsgemeinschaft [Grant MA 10080/2-1]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0680 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0680 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Robust optimization (RO) is one of the key paradigms for solving optimization problems affected by uncertainty. Two principal approaches for RO, the robust counterpart method and the adversarial approach, potentially lead to excessively large optimization problems. For that reason, first-order approaches, based on online convex optimization, have been proposed as alternatives for the case of large-scale problems. However, existing first-order methods are either stochastic in nature or involve a binary search for the optimal value. We show that this problem can also be solved with deterministic first-order algorithms based on a saddle-point Lagrangian reformulation that avoids both of these issues. Our approach recovers the other approaches’ [Formula: see text] convergence rate in the general case and offers an improved [Formula: see text] rate for problems with constraints that are affine both in the decision and in the uncertainty. Experiment involving robust quadratic optimization demonstrates the numerical benefits of our approach. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant VI.Veni.191E.035] and the Israel Science Foundation [Grant 1460/19]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0200 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0200 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The one-warehouse multi-retailer (OWMR) problem is an inventory control problem in a two-echelon supply chain. This study extends the OWMR problem to the data-driven multi-item OWMR problems with backlog and lost sales, designs a new deep network structure that is suitable for the specific multi-item OWMR problems, and develops the LRTO algorithm and the greedy heuristic.

This repository provides the data for the problem (mE-VRSPTW) and the code for the solution method (a BPC algorithm).

This directory contains the code for the K-adaptability branch-and-bound (K-B&B) algorithm, with random node selection and ML-informed node selection (i.e., K-B&B-NodeSelection). K-adaptability is a solution approach for two-stage robust optimization problems.

Ranking problems are commonly encountered in practical applications, including order priority ranking, wine quality ranking, and piston slap noise performance ranking. The responses of these ranking applications are often considered as continuous responses, and there is uncertainty on which scoring function is used to model the responses. In this paper, we address the scoring function uncertainty of continuous response ranking problems by proposing a ranking model averaging (RMA) method. With a set of candidate models varied by scoring functions, RMA assigns weights for each model determined by a K-fold crossvalidation criterion based on pairwise loss. We provide two main theoretical properties for RMA. First, we prove that the averaging ranking predictions of RMA are asymptotically optimal in achieving the lowest possible ranking risk. Second, we provide a bound on the difference between the empirical RMA weights and theoretical optimal ones, and we show that RMA weights are consistent. Simulation results validate RMA superiority over competing methods in reducing ranking risk. Moreover, when applied to empirical examples—order priority, wine quality, and piston slap noise—RMA shows its effectiveness in building accurate ranking systems. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This research was supported by the Beijing Municipal Natural Science Foundation [Grant 1222002], the National Natural Science Foundation of China [Grants 12071457, 12201018, 12301364, 71925007, 72091212, and 72273120], National Quality Infrastructure [Grant 2022YFF0609903], the Chinese Academy of Sciences Project for Young Scientists in Basic Research [Grant YSBR-008], and the Natural Science Foundation of Anhui Province [Grant 2308085QA09]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0257 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0257 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .