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An integer program (IP) with a finite number of feasible solutions may have an unbounded linear programming relaxation if it contains irrational parameters, due to implicit constraints enforced by the irrational numbers. We show that those constraints can be obtained if the irrational parameters are polynomials of roots of integers over the field of rational numbers, leading to an equivalent rational formulation. We also establish a weaker result for IPs involving the general class of algebraic irrational parameters, which extends to IPs with a particular form of transcendental numbers.

Chemotherapy is one of the primary modalities of cancer treatment. Chemotherapy drug administration is a complex problem that often requires expensive clinical trials to evaluate potential regimens. One way to alleviate this burden and better inform future trials is to build reliable models for drug administration. Previous chemotherapy optimization models have mainly relied on optimal control, which does not lend itself to capturing complex and vital operational constraints in chemotherapy planning involving discrete decisions, such as doses via pills and rest periods. In addition, most of the existing models for chemotherapy optimization lack an explicit toxicity measure and impose toxicity constraints primarily through (fixed) limits on drug concentration. The existing stochastic optimization models also focus on maximizing the probability of cure when tumor heterogeneity is uncertain. In this paper, we develop a mixed-integer program for combination chemotherapy (utilization of multiple drugs) optimization that incorporates various important operational constraints and, besides dose and concentration limits, controls treatment toxicity based on its effect on the count of white blood cells. To address the uncertainty of tumor heterogeneity, we propose chance constraints that guarantee reaching an operable tumor size with a high probability in a neoadjuvant setting. We present analytical results pertinent to the accuracy of the model in representing biological processes of chemotherapy and establish its merit for clinical applications through a numerical study of breast cancer.

We present a $(1+\frac{k}{k+2})$-approximation algorithm for the Maximum $k$-dependent Set problem on bipartite graphs for any $k\ge1$. For a graph with $n$ vertices and $m$ edges, the algorithm runs in $O(k m \sqrt{n})$ time and improves upon the previously best-known approximation ratio of $1+\frac{k}{k+1}$ established by Kumar et al. [Theoretical Computer Science, 526: 90--96 (2014)]. Our proof also indicates that the algorithm retains its approximation ratio when applied to the (more general) class of K\"{o}nig-Egerv\'{a}ry graphs.

A cluster graph is a graph whose every connected component is a complete graph. Given a simple undirected graph $G$, a subset of vertices inducing a cluster graph is called an independent union of cliques (IUC), and the IUC polytope associated with $G$ is defined as the convex hull of the incidence vectors of all IUCs in the graph. The {\sc Maximum IUC} problem, which is to find a maximum-cardinality IUC in a graph, finds applications in network-based data analysis. In this paper, we derive several families of facet-defining valid inequalities for the IUC polytope. We also give a complete description of this polytope for some special classes of graphs. We establish computational complexity of the separation problem for most of the considered families of valid inequalities and explore the effectiveness of employing the corresponding cutting planes in an integer (linear) programming framework for the {\sc Maximum IUC} problem through computational experiments., Comment: 40 pages, 3 figures, 5 tables