On Minimizing the Makespan When Some Jobs Cannot Be Assigned on the Same Machine

We study the classical scheduling problem of assigning jobs to machines in order to minimize the makespan. It is well-studied and admits an EPTAS on identical machines and a (2-1/m)-approximation algorithm on unrelated machines. In this paper we study a variation in which the input jobs are partitioned into bags and no two jobs from the same bag are allowed to be assigned on the same machine. Such a constraint can easily arise, e.g., due to system stability and redundancy considerations. Unfortunately, as we demonstrate in this paper, the techniques of the above results break down in the presence of these additional constraints. Our first result is a PTAS for the case of identical machines. It enhances the methods from the known (E)PTASs by a finer classification of the input jobs and careful argumentations why a good schedule exists after enumerating over the large jobs. For unrelated machines, we prove that there can be no (log n)^{1/4-epsilon}-approximation algorithm for the problem for any epsilon > 0, assuming that NP nsubseteq ZPTIME(2^{(log n)^{O(1)}}). This holds even in the restricted assignment setting. However, we identify a special case of the latter in which we can do better: if the same set of machines we give an 8-approximation algorithm. It is based on rounding the LP-relaxation of the problem in phases and adjusting the residual fractional solution after each phase to order to respect the bag constraints

On property-(R1)\bm{(R_1)} and relative Chebyshev centers in Banach spaces-II

We continue to study (strong) property-$(R_1)$ in Banach spaces. As discussed by Pai \& Nowroji in [{\it On restricted centers of sets}, J. Approx. Theory, {\bf 66}(2), 170--189 (1991)], this study corresponds to a triplet $(X,V,\mathcal{F})$, where $X$ is a Banach space, $V$ is a closed convex set, and $\mathcal{F}$ is a subfamily of closed, bounded subsets of $X$. It is observed that if $X$ is a Lindenstrauss space then $(X,B_X,\mathcal{K}(X))$ has strong property-$(R_1)$, where $\mathcal{K}(X)$ represents the compact subsets of $X$. It is established that for any $F\in\mathcal{K}(X)$, $\textrm{Cent}_{B_X}(F)\neq\emptyset$. This extends the well-known fact that a compact subset of a Lindenstrauss space $X$ admits a nonempty Chebyshev center in $X$. We extend our observation that $\textrm{Cent}_{B_X}$ is Lipschitz continuous in $\mathcal{K}(X)$ if $X$ is a Lindenstrauss space. If $Y$ is a subspace of a Banach space $X$ and $\mathcal{F}$ represents the set of all finite subsets of $B_X$ then we observe that $B_Y$ exhibits the condition for simultaneously strongly proximinal (viz. property-$(P_1)$) in $X$ for $F\in\mathcal{F}$ if $(X, Y, \mathcal{F}(X))$ satisfies strong property-$(R_1)$, where $\mathcal{F}(X)$ represents the set of all finite subsets of $X$. It is demonstrated that if $P$ is a bi-contractive projection in $\ell_\infty$, then $(\ell_\infty, Range (P), \mathcal{K}(\ell_\infty))$ exhibits the strong property-$(R_1)$, where $\mathcal{K}(\ell_\infty)$ represents the set of all compact subsets of $\ell_\infty$. Furthermore, stability results for these properties are derived in continuous function spaces, which are then studied for various sums in Banach spaces

A Constant Factor Approximation for Capacitated Min-Max Tree Cover

Given a graph G = (V,E) with non-negative real edge lengths and an integer parameter k, the (uncapacitated) Min-Max Tree Cover problem seeks to find a set of at most k trees which together span V and each tree is a subgraph of G. The objective is to minimize the maximum length among all the trees. In this paper, we consider a capacitated generalization of the above and give the first constant factor approximation algorithm. In the capacitated version, there is a hard uniform capacity (?) on the number of vertices a tree can cover. Our result extends to the rooted version of the problem, where we are given a set of k root vertices, R and each of the covering trees is required to include a distinct vertex in R as the root. Prior to our work, the only result known was a (2k-1)-approximation algorithm for the special case when the total number of vertices in the graph is k? [Guttmann-Beck and Hassin, J. of Algorithms, 1997]. Our technique circumvents the difficulty of using the minimum spanning tree of the graph as a lower bound, which is standard for the uncapacitated version of the problem [Even et al.,OR Letters 2004] [Khani et al.,Algorithmica 2010]. Instead, we use Steiner trees that cover ? vertices along with an iterative refinement procedure that ensures that the output trees have low cost and the vertices are well distributed among the trees

Minimizing Weighted lp-Norm of Flow-Time in the Rejection Model

We consider the online scheduling problem to minimize the weighted ell_p-norm of flow-time of jobs. We study this problem under the rejection model introduced by Choudhury et al. (SODA 2015) - here the online algorithm is allowed to not serve an eps-fraction of the requests. We consider the restricted assignments setting where each job can go to a specified subset of machines. Our main result is an immediate dispatch non-migratory 1/eps^{O(1)}-competitive algorithm for this problem when one is allowed to reject at most eps-fraction of the total weight of jobs arriving. This is in contrast with the speed augmentation model under which no online algorithm for this problem can achieve a competitive ratio independent of p

Fair Rank Aggregation

Ranking algorithms find extensive usage in diverse areas such as web search, employment, college admission, voting, etc. The related rank aggregation problem deals with combining multiple rankings into a single aggregate ranking. However, algorithms for both these problems might be biased against some individuals or groups due to implicit prejudice or marginalization in the historical data. We study ranking and rank aggregation problems from a fairness or diversity perspective, where the candidates (to be ranked) may belong to different groups and each group should have a fair representation in the final ranking. We allow the designer to set the parameters that define fair representation. These parameters specify the allowed range of the number of candidates from a particular group in the top-$k$ positions of the ranking. Given any ranking, we provide a fast and exact algorithm for finding the closest fair ranking for the Kendall tau metric under block-fairness. We also provide an exact algorithm for finding the closest fair ranking for the Ulam metric under strict-fairness, when there are only $O(1)$ number of groups. Our algorithms are simple, fast, and might be extendable to other relevant metrics. We also give a novel meta-algorithm for the general rank aggregation problem under the fairness framework. Surprisingly, this meta-algorithm works for any generalized mean objective (including center and median problems) and any fairness criteria. As a byproduct, we obtain 3-approximation algorithms for both center and median problems, under both Kendall tau and Ulam metrics. Furthermore, using sophisticated techniques we obtain a $(3-\varepsilon)$-approximation algorithm, for a constant $\varepsilon>0$, for the Ulam metric under strong fairness.Comment: A preliminary version of this paper appeared in NeurIPS 202

Rejecting Jobs to Minimize Load and Maximum Flow-time

Online algorithms are usually analyzed using the notion of competitive ratio which compares the solution obtained by the algorithm to that obtained by an online adversary for the worst possible input sequence. Often this measure turns out to be too pessimistic, and one popular approach especially for scheduling problems has been that of "resource augmentation" which was first proposed by Kalyanasundaram and Pruhs. Although resource augmentation has been very successful in dealing with a variety of objective functions, there are problems for which even a (arbitrary) constant speedup cannot lead to a constant competitive algorithm. In this paper we propose a "rejection model" which requires no resource augmentation but which permits the online algorithm to not serve an epsilon-fraction of the requests. The problems considered in this paper are in the restricted assignment setting where each job can be assigned only to a subset of machines. For the load balancing problem where the objective is to minimize the maximum load on any machine, we give O(\log^2 1/\eps)-competitive algorithm which rejects at most an \eps-fraction of the jobs. For the problem of minimizing the maximum weighted flow-time, we give an O(1/\eps^4)-competitive algorithm which can reject at most an \eps-fraction of the jobs by weight. We also extend this result to a more general setting where the weights of a job for measuring its weighted flow-time and its contribution towards total allowed rejection weight are different. This is useful, for instance, when we consider the objective of minimizing the maximum stretch. We obtain an O(1/\eps^6)-competitive algorithm in this case. Our algorithms are immediate dispatch, though they may not be immediate reject. All these problems have very strong lower bounds in the speed augmentation model