On the On-line Number of Snacks Problem

In the number of snacks problem (NSP), which was originally proposed by our team, an on-line player is given the task of deciding how many shares of snacks his noshery should prepare each day. The on-line player must make his decision and then finish the preparation before the customers come to his noshery for the snacks; in other words, he must make decision in an on-line fashion. His goal is to minimize the competitive ratio, defined as _: C_A()/C_OPT(), where denotes a sequence of numbers of customers, C _OPT() is the cost of satisfying by an optimal off-line algorithm, and C _A() is the cost of satisfying by an on-line algorithm. In this paper we give a competitive algorithm for on-line number of snacks problem P1, the Extreme Numbers Harmonic Algorithm(ENHA), with competitive ratio 1+p(M-m)/(M+m), where M and m are two extreme numbers of customers over the total period of the game, and p is a ratio concerning the cost of the two types of situations, and then prove that this competitive ratio is the best one if an on-line player chooses a fixed number of shares of snacks for any sequence of numbers of customers. We also discuss several variants of the NSP and give some results for it. Finally, we propose a conjecture for the on-line NSP.     

Minimizing flow-time on a single machine with integer batch sizes

We address a classical minimum flow-time, single-machine, batch-scheduling problem. Processing times and setups are assumed to be identical for all jobs and batches, respectively. Santos and Magazine (Oper. Res. Lett. 4(1985) 99) introduced an efficient solution for the relaxed (non-integer) problem. We introduce a simple rounding procedure for Santos and Magazine's solution, which guarantees optimal integer batches.     

A single machine batch scheduling problem with bounded batch size

We address a single-machine batch scheduling problem to minimize total flow time. Processing times are assumed to be identical for all jobs. Setup times are assumed to be identical for all batches. As in many practical situations, batch sizes may be bounded. In the first setting studied in this paper, all batch sizes cannot exceed a common upper bound. In the second setting, all batch sizes share a common lower bound. An optimal solution consists of the number of batches and their (integer) size. We introduce an efficient solution for both problems.     

On Multi-threaded Metrical Task Systems

Traditionally, on-line problems have been studied under the assumption that there is a unique sequence of requests that must be served. This approach is common to most general models of on-line computation, such as Metrical Task Systems. However, there exist on-line problems in which the requests are organized in more than one independent thread. In this more general framework, at every moment the first unserved request of each thread is available. Therefore, apart from deciding how to serve a request, at each stage it is necessary to decide which request to serve among several possibilities.In this paper we introduce Multi-threaded Metrical Task Systems, that is, the generalization of Metrical Task Systems to the case in which there are many threads of tasks. We study the problem from a competitive analysis point of view, proving lower and upper bounds on the competitiveness of on-line algorithms. We consider finite and infinite sequences of tasks, as well as deterministic and randomized algorithms. In this work we present the first steps towards a more general framework for on-line problems which is not restricted to a sequential flow of information.     

Dynamically maintaining split graphs

We present an algorithm that supports operations for modifying a split graph by adding edges or vertices and deleting edges, such that after each modification the graph is repaired to become a split graph in a minimal way. In particular, if the graph is not split after the modification, the algorithm computes a minimal, or if desired even a minimum, split completion or deletion of the modified graph. The motivation for such operations is similar to the motivation for fully dynamic algorithms for particular graph classes. In our case we allow all modifications to the graph and repair, rather than allowing only the modifications that keep the graph split. Fully dynamic algorithms of the latter kind are known for split graphs [L. Ibarra, Fully dynamic algorithms for chordal graphs and split graphs, Technical Report DCS-262-IR, University of Victoria, Canada, 2000].Our results can be used to design linear time algorithms for some recognition and completion problems, where the input is supplied in an on-line fashion.     

Randomized on-line algorithms and lower bounds for computing large independent sets in disk graphs

We study the on-line version of the maximum independent set problem, for the case of disk graphs which are graphs resulting from intersections of disks on the plane. In particular, we investigate whether randomization can be used to break known lower bounds for deterministic on-line independent set algorithms and present new upper and lower bounds.     

On-line scheduling on a batch machine to minimize makespan with limited restarts

We study the on-line scheduling on an unbounded batch machine to minimize makespan. In this model, jobs arrive over time and batches are allowed limited restarts. Any batch that contains a job which has already been restarted once cannot be restarted any more. We provide a best possible on-line algorithm for the problem with a competitive ratio .     

On-line construction of the upper envelope of triangles and surface patches in three dimensions

In this paper, we describe a randomized incremental algorithm for computing the upper envelope (i.e., the pointwise maximum) of a set of n triangles in three dimensions. This algorithm is an on-line algorithm. It is structure-sensitive: the expected cost of inserting the n-th triangle is O(log nΣ_r=1ⁿτ(r)/r²) and depends on the expected size τ(r) of an intermediate result for r triangles. Since τ(r) can be Θ(r²(r)) in the worst case, this cost is bounded in the worst case by O(n(n) log n). (The expected behaviour is analyzed by averaging over all possible orderings of the input.) The main new characteristics is the use of a two-level history graph. (The history graph is an auxiliary data structure maintained by randomized incremental algorithms.) Our algorithm is fairly simple and appears to be efficient in practice. It extends to surfaces and surface patches of fixed maximum algebraic degree.     

Improved algorithm for a generalized on-line scheduling problem on identical machines

This paper considers the problem of on-line scheduling a list of independent jobs in which each job has an arbitrary release time on m parallel identical machines. In this problem, jobs arrive in form of order before its release time and decisions have to be made whenever an order is placed and the orders arrive according to any sequence. A heuristic algorithm, NMLS, better than MLS is given for any m ? 2. The competitive ratio is improved from 2.93920 to 2.78436.     

Optimal algorithms for page migration in dynamic networks

We present an extension of a classical data management subproblem, the page migration. The problem is investigated in dynamic networks, where costs of communication between different nodes may change with time. We construct asymptotically optimal online algorithms for this problem, both in deterministic and randomized scenarios.     

Randomized algorithm for the k-server problem on decomposable spaces

We study the randomized k-server problem on metric spaces consisting of widely separated subspaces. We give a method which extends existing algorithms to larger spaces with the growth rate of the competitive quotients being at most O(logk). This method yields o(k)-competitive algorithms solving the randomized k-server problem for some special underlying metric spaces, e.g. HSTs of “small” height (but unbounded degree). HSTs are important tools for probabilistic approximation of metric spaces.     

The on-line asymmetric traveling salesman problem

We consider two on-line versions of the asymmetric traveling salesman problem with triangle inequality. For the homing version, in which the salesman is required to return in the city where it started from, we give a $\frac{3+\sqrt{5}}{2}$-competitive algorithm and prove that this is best possible. For the nomadic version, the on-line analogue of the shortest asymmetric Hamiltonian path problem, we show that the competitive ratio of any on-line algorithm depends on the amount of asymmetry of the space in which the salesman moves. We also give bounds on the competitive ratio of on-line algorithms that are zealous, that is, in which the salesman cannot stay idle when some city can be served.     

An improved on-line algorithm for scheduling on two unrestrictive parallel batch processing machines

We consider the problem of on-line scheduling a set of n jobs on two parallel batch processing machines. The objective is to minimize the makespan. We provide an algorithm for the problem that is better than one given in the literature, improving the competitive ratio from to .     

Union and split operations on dynamic trapezoidal maps

We propose algorithms to perform two new operations on an arrangement of line segments in the plane, represented by a trapezoidal map: the split of the map along a given vertical line D, and the union of two trapezoidal maps computed in two vertical slabs of the plane that are adjacent through a vertical line D. The data structure we use is a modified Influence Graph, still allowing dynamic insertions and deletions of line segments in the map. The algorithms for both operations run in O(s_D logn+log²n) time, where n is the number of line segments in the map, and s_D is the number of line segments intersected by D.     

On-line k-Truck Problem and Its Competitive Algorithms

In this paper, based on the Position Maintaining Strategy (PMS for short), on-line scheduling of k-truck problem, which is a generalization of the famous k-server problem, is originally presented by our team. We proposed several competitive algorithms applicable under different conditions for solving the on-line k-truck problem. First, a competitive algorithm with competitive ratio 2k+1/ is given for any 1. Following that, if (c+1)/(c-1) holds, then there must exist a (2k-1)-competitive algorithm for k-truck problem, where c is the competitive ratio of the on-line algorithm about the relevant k-server problem. And then a greedy algorithm with competitive ratio 1+/, where lambda is a parameter related to the structure property of a given graph, is given. Finally, competitive algorithms with ratios 1+1/ are given for two special families of graphs.     

An improved algorithm for online machine minimization

The online machine minimization problem seeks to design a preemptive scheduling algorithm on multiple machines — each job $j$ arrives at its release time $r_j$, has to be processed for $p_j$ time units, and must be completed by its deadline $d_j$. The goal is to minimize the number of machines the algorithm uses. We improve the $O(logm)$-competitive algorithm by Chen, Megow and Schewior (SODA 2016) and provide an $O\left(\frac{logm}{loglogm}\right)$-competitive algorithm.     

A new approximation algorithm for the multilevel facility location problem

In this paper we propose a new integer programming formulation for the multilevel facility location problem and a novel 3-approximation algorithm based on LP-rounding. The linear program that we use has a polynomial number of variables and constraints, thus being more efficient than the one commonly used in the approximation algorithms for these types of problems.     

Randomized and quantum algorithms yield a speed-up for initial-value problems

Quantum algorithms and complexity have recently been studied not only for discrete, but also for some numerical problems. Most attention has been paid so far to the integration and approximation problems, for which a speed-up is shown in many important cases by quantum computers with respect to deterministic and randomized algorithms on a classical computer. In this paper, we deal with the randomized and quantum complexity of initial-value problems. For this nonlinear problem, we show that both randomized and quantum algorithms yield a speed-up over deterministic algorithms. Upper bounds on the complexity in the randomized and quantum settings are shown by constructing algorithms with a suitable cost, where the construction is based on integral information. Lower bounds result from the respective bounds for the integration problem.