On the complexity of finding a local minimizer of a quadratic function over a polytope

Mathematical Programming - We show that unless P=NP, there cannot be a polynomial-time algorithm that finds a point within Euclidean distance $$c^n$$ (for any constant $$c \ge 0$$ ) of a local...     

Non-approximability of precedence-constrained sequencing to minimize setups

It is a basic scheduling problem to sequence a set of precedence-constrained tasks to minimize the number of setups, where the tasks are partitioned into classes that require the same setup. We prove a conjecture in (Ph.D. Thesis, School of ISyE, Georgia Institute of Technology, August 1986; Oper. Res. 39 (1991) 1012) that no polynomial-time algorithm for this problem has constant worst-case performance ratio unless P=NP. A very simple algorithm has performance ratio $\text{n}$.     

Guarantees for the success frequency of an algorithm for finding Dodgson-election winners

In the year 1876 the mathematician Charles Dodgson, who wrote fiction under the now more famous name of Lewis Carroll, devised a beautiful voting system that has long fascinated political scientists. However, determining the winner of a Dodgson election is known to be complete for the Θ ₂ ^p level of the polynomial hierarchy. This implies that unless P=NP no polynomial-time solution to this problem exists, and unless the polynomial hierarchy collapses to NP the problem is not even in NP. Nonetheless, we prove that when the number of voters is much greater than the number of candidates—although the number of voters may still be polynomial in the number of candidates—a simple greedy algorithm very frequently finds the Dodgson winners in such a way that it “knows” that it has found them, and furthermore the algorithm never incorrectly declares a nonwinner to be a winner.     

A class of polynomially solvable 0-1 programming problems and an application

It is well known that general 0-1 programming problems are NP-Complete and their optimal solutions cannot be found with polynomial-time algorithms unless P=NP. In this paper, we identify a specific class of 0-1 programming problems that is polynomially solvable, and propose two polynomial-time algorithms to find its optimal solutions. This class of 0-1 programming problems commits to a wide range of real-world industrial applications. We provide an instance of representative in the field of supply chain man...     

Computing the minimal covering set

We present the first polynomial-time algorithm for computing the minimal covering set of a (weak) tournament. The algorithm draws upon a linear programming formulation of a subset of the minimal covering set known as the essential set. On the other hand, we show that no efficient algorithm exists for two variants of the minimal covering set–the minimal upward covering set and the minimal downward covering set–unless P equals NP. Finally, we observe a strong relationship between von Neumann–Morgenstern stable sets and upward covering on the one hand, and the Banks set and downward covering on the other.     

Inapproximability of finding maximum hidden sets on polygons and terrains

How many people can hide in a given terrain, without any two of them seeing each other? We are interested in finding the precise number and an optimal placement of people to be hidden, given a terrain with n vertices. In this paper, we show that this is not at all easy: The problem of placing a maximum number of hiding people is almost as hard to approximate as the problem, i.e., it cannot be approximated by any polynomial-time algorithm with an approximation ratio of n for some >0, unless P=NP. This is already true for a simple polygon with holes (instead of a terrain). If we do not allow holes in the polygon, we show that there is a constant >0 such that the problem cannot be approximated with an approximation ratio of 1+.     

Rescheduling with new orders and general maximum allowable time disruptions

We study the rescheduling with new orders on a single machine under the general maximum allowable time disruptions. Under the restriction of general maximum allowable time disruptions, each original job has an upper bound for its time disruption (regarded as the maximum allowable time disruption of the job), or equivalently, in every feasible schedule, the difference of the completion time of each original job compared to that in the pre-schedule does not exceed its maximum allowable time disruption. We also consider a stronger restriction which additionally requires that, in a feasible schedule, the starting time of each original job is not allowed to be scheduled smaller than that in the pre-schedule. Scheduling objectives to be minimized are the maximum lateness and the total completion time, respectively, and the pre-schedules of original jobs are given by EDD-schedule and SPT-schedule, respectively. Then we have four problems for consideration. For the two problems for minimizing the maximum lateness, we present strong NP-hardness proof, provide a simple 2-approximation polynomial-time algorithm, and show that, unless \(\text {P}= \text {NP}\), the two problems cannot have an approximation polynomial-time algorithm with a performance ratio less than 2. For the two problems for minimizing the total completion time, we present strong NP-hardness proof, provide a simple heuristic algorithm, and show that, unless \(\text {P}= \text {NP}\), the two problems cannot have an approximation polynomial-time algorithm with a performance ratio less than 4/3. Moreover, by relaxing the maximum allowable time disruptions of the original jobs, we present a super-optimal dual-approximation polynomial-time algorithm. As a consequence, if the maximum allowable time disruption of each original job is at least its processing time, then the two problems for minimizing the total completion time are solvable in polynomial time. Finally, we show that, under the agreeability assumption (i.e., the nondecreasing order of the maximum allowable time disruptions of the original jobs coincides with their scheduling order in the pre-schedule), the four problems in consideration are solvable in polynomial time.     

The complexity of approximating a nonlinear program

We consider the problem of finding the maximum of a multivariate polynomial inside a convex polytope. We show that there is no polynomial time approximation algorithm for this problem, even one with a very poor guarantee, unless P = NP. We show that even when the polynomial is quadratic (i.e. quadratic programming) there is no polynomial time approximation unless NP is contained in quasi-polynomial time.Our results rely on recent advances in the theory of interactive proof systems. They exemplify an interesting interplay of discrete and continuous mathematics—using a combinatorial argument to get a hardness result for a continuous optimization problem.     

Computing the norm ∥A∥∞,1 is NP-hard

It is proved that computing the subordinate matrix norm ∥A∥∞1 is NP-hard, Even more, existence of a polynomial-time algorithm for computing this norm with relative accuracy less than 1/(4n²), where n is matrix size, implies P = NP.     

Fully polynomial-time approximation scheme for a special case of a quadratic Euclidean 2-clustering problem

The strongly NP-hard problem of partitioning a finite set of points of Euclidean space into two clusters of given sizes (cardinalities) minimizing the sum (over both clusters) of the intracluster sums of squared distances from the elements of the clusters to their centers is considered. It is assumed that the center of one of the sought clusters is specified at the desired (arbitrary) point of space (without loss of generality, at the origin), while the center of the other one is unknown and determined as the mean value over all elements of this cluster. It is shown that unless P = NP, there is no fully polynomial-time approximation scheme for this problem, and such a scheme is substantiated in the case of a fixed space dimension.     

Two-stage open shop scheduling with a bottleneck machine

It is known that for the open shop scheduling problem to minimize the makespan there exists no polynomial-time heuristic algorithm that guarantees a worst-case performance ratio better than 5/4, unless P≠NP. However, this result holds only if the instance of the problem contains jobs consisting of at least three operations. This paper considers the open shop scheduling problem, provided that each job consists of at most two operations, one of which is to be processed on one of the m⩾2 machines, while the other operation must be performed on the bottleneck machine, the same for all jobs. For this NP-hard problem we present a heuristic algorithm and show that its worst-case performance ratio is 5/4.     

Computing the norm ∥A∥∞,1 is NP-hard ∗

It is proved that computing the subordinate matrix norm ∥A∥∞1 is NP-hard, Even more, existence of a polynomial-time algorithm for computing this norm with relative accuracy less than 1/(4n² ), where n is matrix size, implies P = NP.     

On the computational complexity of best Chebyshev approximations

The best Chebyshev approximation of degree n to a continuous function f on [0, 1] is the unique polynomial ϕ of degree less than or equal to n such that the maximum difference of f and ϕ on [0, 1] is minimized. On the basis of a formal model of computation, it is shown that the question of whether the best Chebyshev approximations of polynomial-time computable functions on [0, 1] are always polynomial-time computable depends on the relationship among well-known discrete complexity classes. In particular, P = NP implies that these best approximations are polynomial-time computable, and EXP ≠ NEXP implies that these best approximations are not polynomial-time computable. It is also pointed out that the fact that the popular Remes algorithm converges fast does not conflict with the above result, since the Remes algorithm requires, in each iteration, the finding of maximal points of continuous functions on an interval [a, b], which is, in general, provably intractable.     

NP-hardness of deciding convexity of quartic polynomials and related problems

We show that unless P = NP, there exists no polynomial time (or even pseudo-polynomial time) algorithm that can decide whether a multivariate polynomial of degree four (or higher even degree) is globally convex. This solves a problem that has been open since 1992 when N. Z. Shor asked for the complexity of deciding convexity for quartic polynomials. We also prove that deciding strict convexity, strong convexity, quasiconvexity, and pseudoconvexity of polynomials of even degree four or higher is strongly NP-hard. By contrast, we show that quasiconvexity and pseudoconvexity of odd degree polynomials can be decided in polynomial time.     

On the complexity of enumerating pseudo-intents

We investigate whether the pseudo-intents of a given formal context can efficiently be enumerated. We show that they cannot be enumerated in a specified lexicographic order with polynomial delay unless P=NP. Furthermore we show that if the restriction on the order of enumeration is removed, then the problem becomes at least as hard as enumerating minimal transversals of a given hypergraph. We introduce the notion of minimal pseudo-intents and show that recognizing minimal pseudo-intents is polynomial. Despite their less complicated nature, surprisingly it turns out that minimal pseudo-intents cannot be enumerated in output-polynomial time unless P=NP.     

On the approximability of the minimum strictly fundamental cycle basis problem

We consider the problem of finding a strictly fundamental cycle basis of minimum weight in the cycle space associated with an undirected connected graph G, where a nonnegative weight is assigned to each edge of G and the total weight of a basis is defined as the sum of the weights of all the cycles in the basis. Several heuristics have been proposed to tackle this NP-hard problem, which has some interesting applications. In this paper we show that this problem is APX-hard, even when restricted to unweighted graphs, and hence does not admit a polynomial-time approximation scheme, unless P=NP. Using a recent result on the approximability of lower-stretch spanning trees (Elkin et al. (2005) [7]), we obtain that the problem is approximable within O(log²nloglogn) for arbitrary graphs. We obtain tighter approximability bounds for dense graphs. In particular, the problem restricted to complete graphs admits a polynomial-time approximation scheme.     

Approximability of the Problem of Finding a Vector Subset with the Longest Sum

We answer the question of existence of polynomial-time constant-factor approximation algorithms for the space of nonfixed dimension. We prove that, in Euclidean space the problem is solvable in polynomial time with accuracy \(\sqrt a \), where α = 2/π, and if P ≠ NP then there are no polynomial algorithms with better accuracy. It is shown that, in the case of the ?_p spaces, the problem is APX-complete if p ∈ [1, 2] and not approximable with constant accuracy if P ≠ NP and p ∈ (2,∞).     

单台机以总完工时间为目标的批排序问题

研究单台机,工件加工时间相等,大小不同的批排序问题,给出了一个最坏情况界为9+3~(1/2)/6≈1.7817的多项式时间近似算法,并证明了即使工件总大小不超过2,该问题也不存在FPTAS,除非P=NP.     

Some provably hard crossing number problems

This paper presents a connection between the problem of drawing a graph with the minimum number of edge crossings, and the theory of arrangements of pseudolines, a topic well-studied by combinatorialists. In particular, we show that any given arrangement can be forced to occur in every minimum crossing drawing of an appropriate graph. Using some recent results of Goodman, Pollack, and Sturmfels, this yields that there exists no polynomial-time algorithm for producing a straight-line drawing of a graph, which achieves the minimum number of crossings from among all such drawings. While this result has no bearing on the P versus NP question, it is fairly negative with regard to applications. We also study the problem of drawing a graph with polygonal edges, to achieve the (unrestricted) minimum number of crossings. Here we obtain a tight bound on the smallest number of breakpoints which are required in the polygonal lines. This work was partially supported by the Center for Telecommunications Research, Columbia University.