In this paper we introduce the concept of additive approximation schemes and apply it to load balancing problems. Additive approximation schemes aim to find a solution with an absolute error in the objective of at most $εh$ for some suitable parameter $h$. In the case that the parameter $h$ provides a lower bound an additive approximation scheme implies a standard multiplicative approximation scheme and can be much stronger when $h \ll$ OPT. On the other hand, when no PTAS exists (or is unlikely to exist), additive approximation schemes can provide a different notion for approximation. We consider the problem of assigning jobs to identical machines with lower and upper bounds for the loads of the machines. This setting generalizes problems like makespan minimization, the Santa Claus problem (on identical machines), and the envy-minimizing Santa Claus problem. For the last problem, in which the objective is to minimize the difference between the maximum and minimum load, the optimal objective value may be zero and hence it is NP-hard to obtain any multiplicative approximation guarantee. For this class of problems we present additive approximation schemes for $h = p_{\max}$, the maximum processing time of the jobs. Our technical contribution is two-fold. First, we introduce a new relaxation based on integrally assigning slots to machines and fractionally assigning jobs to the slots (the slot-MILP). We identify structural properties of (near-)optimal solutions of the slot-MILP, which allow us to solve it efficiently, assuming that there are $O(1)$ different lower and upper bounds on the machine loads (which is the relevant setting for the three problems mentioned above). The second technical contribution is a local-search based algorithm which rounds a solution to the slot-MILP introducing an additive error on the target load intervals of at most $ε\cdot p_{\max}$.

Given a complete graph with an even number of vertices, and with each edge colored with one of two colors (say red or blue), an equitable Hamiltonian cycle is a Hamiltonian cycle that can be decomposed into two perfect matchings such that both perfect matchings have the same number of red edges. We show that, for any coloring of the edges, in any complete graph on at least 6 vertices, an equitable Hamiltonian cycle exists.

Posted price mechanisms constitute a widely used way of selling items to strategic consumers. Although suboptimal, the attractiveness of these mechanisms comes from their simplicity and easy implementation. In this paper, we investigate the performance of posted price mechanisms when customers arrive in an unknown random order. We compare the expected revenue of these mechanisms to the expected revenue of the optimal auction in two different settings. Namely, the nonadaptive setting in which all offers are sent to the customers beforehand, and the adaptive setting in which an offer is made when a consumer arrives. For the nonadaptive case, we obtain a strategy achieving an expected revenue within at least a 1-1/e fraction of that of the optimal auction. We also show that this bound is tight, even if the customers have i.i.d. valuations for the item. For the adaptive case, we exhibit a posted price mechanism that achieves a factor 0.745 of the optimal revenue, when the customers have i.i.d. valuations for the item. Furthermore, we prove that our results extend to the prophet inequality setting and in particular our result for i.i.d. random valuations resolves a problem posed by Hill and Kertz. [13]

We consider the Vector Scheduling problem, a natural generalization of the classical makespan minimization problem to multiple resources. Here, we are given n jobs, represented as d-dimensional vectors in $$[0,1]^d$$ , and m identical machines, and the goal is to assign the jobs to machines such that the maximum load of each machine over all the coordinates is at most 1. For fixed d, the problem admits an approximation scheme, and the best known running time is $$n^{f(\epsilon ,d)}$$ where $$f(\epsilon ,d) = (1/\epsilon )^{\tilde{O}(d)}$$ ( $$\tilde{O}$$ suppresses polylogarithmic terms in d). In particular, the dependence on d is double exponential. In this paper we show that a double exponential dependence on d is necessary, and give an improved algorithm with essentially optimal running time. Specifically, we let $$\exp (x)$$ denote $$2^x$$ and show that: (1) For any $$\epsilon <1$$ , there is no $$(1+\epsilon )$$ -approximation with running time $$\exp \left( o(\lfloor 1/\epsilon \rfloor ^{d/3})\right) $$ unless the Exponential Time Hypothesis fails. (2) No $$(1+\epsilon )$$ -approximation with running time $$\exp \left( \lfloor 1/\epsilon \rfloor ^{o(d)}\right) $$ exists, unless NP has subexponential time algorithms. (3) Similar lower bounds also hold even if $$\epsilon m$$ extra machines are allowed (i.e. with resource augmentation), for sufficiently small $$\epsilon >0$$ . (4) We complement these lower bounds with a $$(1+\epsilon )$$ -approximation that runs in time $$\exp \left( (1/\epsilon )^{O(d \log \log d)}\right) + nd$$ . This gives the first efficient approximation scheme (EPTAS) for the problem.

Increasing interest has recently been shown in analyzing the worst-case behavior of local search algorithms. In particular, the quality of local optima and the time needed to find the local optima by the simplest form of local search has been studied. This paper deals with worst-case performance of local search algorithms for makespan minimization on parallel machines. We analyze the quality of the local optima obtained by iterative improvement over the jump, swap, multi-exchange, and the newly defined push neighborhoods. Finally, for the jump neighborhood we provide bounds on the number of local search steps required to find a local optimum.

In this paper, we introduce the notion of smoothed competitive analysis of online algorithms. Smoothed analysis has been proposed by Spielman and Teng [25] to explain the behavior of algorithms that work well in practice while performing very poorly from a worst-case analysis point of view. We apply this notion to analyze the multilevel feedback algorithm (MLF) to minimize the total flow time on a sequence of jobs released over time when the processing time of a job is only known at time of completion. The initial processing times are integers in the range [1, 2K]. We use a partial bit randomization model, i.e., the initial processing times are smoothed by changing the k least significant bits under a quite general class of probability distributions. We show that MLF admits a smoothed competitive ratio of O((2k/σ)3+ (2k/σ)22K-k), where σ denotes the standard deviation of the distribution. In particular, we obtain a competitive ratio of O(2K-k) if σ = Θ(2k). We also prove an Ω(2K-k) lower bound for any deterministic algorithm that is run on processing times smoothed according to the partial bit randomization model. For various other smoothing models, including the additive symmetric smoothing one, which is a variant of the model used by Spielman and Teng [25], we give a higher lower bound of Ω(2K). A direct consequence of our result is also the first average-case analysis of MLF. We show a constant expected ratio of the total flow time of MLF to the optimum under several distributions including the uniform one.

This paper presents an empirical comparison of polynomial-time approximation algorithms and local search heuristics for the problem of minimizing total weighted completion time on unrelated parallel machines. Algorithms with a worst-case performance guarantee are based on rounding a fractional solution to an LP-relaxation or to a convex quadratic-programming relaxation. We also investigate dominance relations among the lower bounds resulting from these relaxations.