ABSTRACT The minimum ‐‐cut problem is one of the most‐studied problems in discrete optimization and has a unique complexity status in multi‐objective optimization. Even though the single‐objective version of the problem can be solved in polynomial time, it has been shown in the seminal work of Papadimitriou and Yannakakis (2000) that there does not exist a multi‐objective fully polynomial‐time approximation scheme (MFPTAS) for the minimum ‐‐cut problem unless . This holds both for the case of objective functions with arc capacities in and for objective functions with general capacities, and even for tractable instances where the number of non‐dominated points is only quadratic in the input size. In this article, we strengthen these results by showing that, assuming , there does not exist an MFPTAS for the minimum ‐‐cut problem with two objectives and arc capacities in , nor for the minimum ‐‐cut problem with two objectives and arc capacities in . This advancement is particularly interesting since the considered problem variants are the only known problems in multi‐objective optimization that do not admit an MFPTAS even though their single‐objective versions are solvable in polynomial time and the problems are tractable , that is, the numbers of non‐dominated points are polynomial (even linear) in the input size. Furthermore, we complement this result by showing that, on graphs of bounded tree‐width, the minimum ‐‐cut problem with polynomially bounded arc capacities can be solved exactly in polynomial time for any constant number of objectives.

Convex approximation sets for multiobjective optimization problems are a well-studied relaxation of the common notion of approximation sets. Instead of approximating each image of a feasible solution by the image of some solution in the approximation set up to a multiplicative factor in each component, a convex approximation set only requires this multiplicative approximation to be achieved by some convex combination of finitely many images of solutions in the set. This makes convex approximation sets efficiently computable for a wide range of multiobjective problems: even for many problems for which (classic) approximations sets are hard to compute. In this article, we propose a polynomial-time algorithm to compute convex approximation sets that builds on an exact or approximate algorithm for the weighted sum scalarization and is therefore applicable to a large variety of multiobjective optimization problems. The provided convex approximation quality is arbitrarily close to the approximation quality of the underlying algorithm for the weighted sum scalarization. In essence, our algorithm can be interpreted as an approximate version of the dual variant of Benson’s outer approximation algorithm. Thus, in contrast to existing convex approximation algorithms from the literature, information on solutions obtained during the approximation process is utilized to significantly reduce both the practical running time and the cardinality of the returned solution sets while still guaranteeing the same worst-case approximation quality. We underpin these advantages by the first comparison of all existing convex approximation algorithms on several instances of the triobjective knapsack problem and the triobjective symmetric metric traveling salesman problem. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This research was supported by the German Research Foundation [Project 398572517]. 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.2023.0220 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0220 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Abstract Linear-parametric optimization, where multiple objectives are combined into a single objective using linear combinations with parameters as coefficients, has numerous links to other fields in optimization and a wide range of application areas. In this survey, we provide a comprehensive overview of structural results and algorithmic strategies for solving linear-parametric optimization problems exactly and approximately. Transferring concepts from related areas such as multi-objective optimization provides further relevant results. The survey consists of two parts: First, we list strategies that work in a general fashion and do not rely on specific problem structures. Second, we look at well-studied parametric optimization problems and cover both important theoretical results and specialized algorithmic approaches for these problems. Among these problems are parametric variants of shortest path problems, minimum cost flow and maximum flow problems, spanning tree problems, the knapsack problem, and matching problems. Overall, we cover the results from 128 publications (and refer to 35 supplemental works) published between 1963 and 2024.

Abstract Given a directed temporal graph, a start node , and objectives, the task in the single‐source multiobjective temporal shortest path problem (SSMTSPP) consists of computing the set of nondominated images of temporal ‐‐paths for each node as well as one corresponding efficient path for each of these images. This problem generalizes both the multiobjective shortest path problem in static graphs and the single‐objective temporal shortest path problem. In this article, we provide a general label setting algorithm for the SSMTSPP that can handle a large variety of different objectives. The only condition imposed on the objectives is a monotonicity property that generalizes the nonnegativity of the arc costs required for the well‐known label setting algorithm for solving the static single‐source shortest path problem in both the single objective and the multiobjective case. Our analysis of the presented algorithm shows that its worst‐case running time is polynomial in the sum of the input size of the problem instance and the number of nondominated images, which implies that it runs in polynomial time as long as the number of nondominated images is polynomial in the instance size (i.e., for all tractable versions of the problem). To complement this result, we provide a complete classification into tractable and intractable problems for all SSMTSPPs involving a large variety of objectives. In particular, using our general analysis, this provides a large range of specific SSMTSPPs for which our general label setting algorithm runs in polynomial time.

In the network flow interdiction problem (NFI), an interdictor aims to remove arcs of total cost at most a given budget B from a network with given arc costs and capacities such that the value of a maximum flow from a source s to a sink t is minimized. We present a polynomial-time (B+1)-approximation algorithm for NFI with unit arc costs, which is the first approximation algorithm for any variant of network flow interdiction whose approximation ratio only depends on the budget available to the interdictor, but not on the size of the network.

Abstract This article investigates the approximation quality achievable for biobjective minimization problems with respect to the Pareto cone by solutions that are (approximately) optimal with respect to larger ordering cones. When simultaneously considering $$\alpha $$ α -approximations for all closed convex ordering cones of a fixed inner angle $$\gamma \in \left[ \frac{\pi }{2}, \pi \right] $$ γ ∈ π 2 , π , an approximation guarantee between $$\alpha $$ α and $$2 \alpha $$ 2 α is achieved, which depends continuously on $$\gamma $$ γ . The analysis is best-possible for any inner angle and it generalizes and unifies the known results that the set of supported solutions is a 2-approximation and that the efficient set itself is a 1-approximation. Moreover, it is shown that, for maximization problems, no approximation guarantee is achievable in general by considering larger ordering cones in the described fashion, which again generalizes a known result about the set of supported solutions.

Abstract We determine the power of the weighted sum scalarization with respect to the computation of approximations for general multiobjective minimization and maximization problems. Additionally, we introduce a new multi-factor notion of approximation that is specifically tailored to the multiobjective case and its inherent trade-offs between different objectives. For minimization problems, we provide an efficient algorithm that computes an approximation of a multiobjective problem by using an exact or approximate algorithm for its weighted sum scalarization. In case that an exact algorithm for the weighted sum scalarization is used, this algorithm comes arbitrarily close to the best approximation quality that is obtainable by supported solutions – both with respect to the common notion of approximation and with respect to the new multi-factor notion. Moreover, the algorithm yields the currently best approximation results for several well-known multiobjective minimization problems. For maximization problems, however, we show that a polynomial approximation guarantee can, in general, not be obtained in more than one of the objective functions simultaneously by supported solutions.

Algorithms for approximating the nondominated set of multiobjective optimization problems are reviewed. The approaches are categorized into general methods that are applicable under mild assumptions and, thus, to a wide range of problems, and into algorithms that are specifically tailored to structured problems. All in all, this survey covers 52 articles published within the last 41 years, that is, between 1979 and 2020. Summary of Contribution: In many problems in operations research, several conflicting objective functions have to be optimized simultaneously, and one is interested in finding Pareto optimal solutions. Because of the high complexity of finding Pareto optimal solutions and their usually very large number, however, the exact solution of such multiobjective problems is often very difficult, which motivates the study of approximation algorithms for multiobjective optimization problems. This research area uses techniques and methods from algorithmics and computing in order to efficiently determine approximate solutions to many well-known multiobjective problems from operations research. Even though approximation algorithms for multiobjective optimization problems have been investigated for more than 40 years and more than 50 research articles have been published on this topic, this paper provides the first survey of this important area at the intersection of computing and operations research.

Abstract Papadimitriou and Yannakakis (Proceedings of the 41st annual IEEE symposium on the Foundations of Computer Science (FOCS), pp 86–92, 2000) show that the polynomial-time solvability of a certain auxiliary problem determines the class of multiobjective optimization problems that admit a polynomial-time computable $$(1+\varepsilon , \dots , 1+\varepsilon )$$ ( 1 + ε , ⋯ , 1 + ε ) -approximate Pareto set (also called an $$\varepsilon $$ ε -Pareto set). Similarly, in this article, we characterize the class of multiobjective optimization problems having a polynomial-time computable approximate $$\varepsilon $$ ε -Pareto set that is exact in one objective by the efficient solvability of an appropriate auxiliary problem. This class includes important problems such as multiobjective shortest path and spanning tree, and the approximation guarantee we provide is, in general, best possible. Furthermore, for biobjective optimization problems from this class, we provide an algorithm that computes a one-exact $$\varepsilon $$ ε -Pareto set of cardinality at most twice the cardinality of a smallest such set and show that this factor of 2 is best possible. For three or more objective functions, however, we prove that no constant-factor approximation on the cardinality of the set can be obtained efficiently.

We analyze the computational complexity of the many types of pencil-and-paper-style puzzles featured in the 2016 puzzle video game The Witness. In all puzzles, the goal is to draw a simple path in a rectangular grid graph from a start vertex to a destination vertex. The different puzzle types place different constraints on the path: preventing some edges from being visited (broken edges); forcing some edges or vertices to be visited (hexagons); forcing some cells to have certain numbers of incident path edges (triangles); or forcing the regions formed by the path to be partially monochromatic (squares), have exactly two special cells (stars), or be singly covered by given shapes (polyominoes) and/or negatively counting shapes (antipolyominoes). We show that any one of these clue types (except the first) is enough to make path finding NP-complete ("witnesses exist but are hard to find"), even for rectangular boards. Furthermore, we show that a final clue type (antibody), which necessarily "cancels" the effect of another clue in the same region, makes path finding Σ2-complete ("witnesses do not exist"), even with a single antibody (combined with many anti/polyominoes), and the problem gets no harder with many antibodies. On the positive side, we give a polynomial-time algorithm for monomino clues, by reducing to hexagon clues on the boundary of the puzzle, even in the presence of broken edges, and solving "subset Hamiltonian path" for terminals on the boundary of an embedded planar graph in polynomial time.

We consider the (linear) parametric 0–1 knapsack problem in which the profits of the items are affine-linear functions of a real-valued parameter and the task is to compute a solution for all values of the parameter. For this problem, it is known that the piecewise linear convex function mapping the parameter to the optimal objective value of the corresponding instance (called the optimal value function ) can have exponentially many breakpoints (points of slope change), which implies that every optimal algorithm for the problem must output a number of solutions that is exponential in the number of items. We provide the first (parametric) polynomial time approximation scheme (PTAS) for the parametric 0–1 knapsack problem. Moreover, we exploit the connection between the parametric problem and the bicriteria problem in order to show that the parametric 0–1 knapsack problem admits a parametric FPTAS when the parameter is restricted to the positive real line and the slopes and intercepts of the affine-linear profit functions of the items are nonnegative. The method used to obtain this result applies to many linear parametric optimization problems and provides a general connection between bicriteria and linear parametric optimization problems.

We consider network design games with weighted players and uniform edge capacities and study their Nash equilibria. In these games, each player has to choose a path from her source to her sink through a network subject to the constraint that the total weight of all players using an edge within their chosen path does not exceed the capacity of the edge. The fixed cost of each edge that is used by some player is shared among the players using the edge by charging each player a fraction of the edge's cost equal to the ratio of her weight to the total weight of all players using the edge. We show that there exist instances of capacitated network design games with weighted players and uniform capacities that do not admit a Nash equilibrium even in the case that all players share the same source and sink. Moreover, we show that it is strongly ‐hard to decide whether a given instance admits a Nash equilibrium even if a feasible solution for the underlying network design problem is guaranteed to exist. In contrast, we prove that, for series‐parallel graphs, there always exists a Nash equilibrium whose total cost equals the cost of an optimal solution of the corresponding network design problem and provide an (exponential‐time) algorithm to compute this equilibrium. © 2016 Wiley Periodicals, Inc. NETWORKS, Vol. 68(2), 141–158 2016

Abstract We consider the maximum flow problem with minimum quantities (MFPMQ), which is a variant of the maximum flow problem where the flow on each arc in the network is restricted to be either zero or above a given lower bound (a minimum quantity), which may depend on the arc. This problem has recently been shown to be weakly NP ‐complete even on series–parallel graphs. In this article, we provide further complexity and approximability results for MFPMQ and several special cases. We first show that it is strongly NP ‐hard to approximate MFPMQ on general graphs (and even bipartite graphs) within any positive factor. On series–parallel graphs, however, we present a pseudo‐polynomial time dynamic programming algorithm for the problem. We then study the case that the minimum quantity is the same for each arc in the network and show that, under this restriction, the problem is still weakly NP ‐complete on general graphs, but can be solved in strongly polynomial time on series–parallel graphs. On general graphs, we present a \documentclass{article}\usepackage{mathrsfs}\usepackage{amsmath, amssymb}\pagestyle{empty}\begin{document}\begin{align*}(2-\frac{1}{\lambda})\end{align*}\end{document} ‐approximation algorithm for this case, where λ denotes the common minimum quantity of all arcs. © 2013 Wiley Periodicals, Inc. NETWORKS, 2013