72 research outputs found
Streaming algorithms for bin packing and vector scheduling
Problems involving the efficient arrangement of simple objects, as captured by bin packing and makespan scheduling, are fundamental tasks in combinatorial optimization. These are well understood in the traditional online and offline cases, but have been less well-studied when the volume of the input is truly massive, and cannot even be read into memory. This is captured by the streaming model of computation, where the aim is to approximate the cost of the solution in one pass over the data, using small space. As a result, streaming algorithms produce concise input summaries that approximately preserve the optimum value. We design the first efficient streaming algorithms for these fundamental problems in combinatorial optimization. For BIN PACKING, we provide a streaming asymptotic (1 + ε)-approximation wit
Streaming algorithms for bin packing and vector scheduling
Problems involving the efficient arrangement of simple objects, as captured by bin packing and makespan scheduling, are fundamental tasks in combinatorial optimization. These are well understood in the traditional online and offline cases, but have been less well-studied when the volume of the input is truly massive, and cannot even be read into memory. This is captured by the streaming model of computation, where the aim is to approximate the cost of the solution in one pass over the data, using small space. As a result, streaming algorithms produce concise input summaries that approximately preserve the optimum value. We design the first efficient streaming algorithms for these fundamental problems in combinatorial optimization. For BIN PACKING, we provide a streaming asymptotic (1 + ε)-approximation wit
Streaming algorithms for bin packing and vector scheduling
Problems involving the efficient arrangement of simple objects, as captured by bin packing and makespan scheduling, are fundamental tasks in combinatorial optimization. These are well understood in the traditional online and offline cases, but have been less well-studied when the volume of the input is truly massive, and cannot even be read into memory. This is captured by the streaming model of computation, where the aim is to approximate the cost of the solution in one pass over the data, using small space. As a result, streaming algorithms produce concise input summaries that approximately preserve the optimum value. We design the first efficient streaming algorithms for these fundamental problems in combinatorial optimization. For BIN PACKING, we provide a streaming asymptotic (1 + ε)-approximation wit
Streaming algorithms for multitasking scheduling with shared processing
In this paper, we design the first streaming algorithms for the problem of multitasking scheduling on parallel machines with shared processing. In one pass, our streaming approximation schemes can provide an approximate value of the optimal makespan. If the jobs can be read in two passes, the algorithm can find the schedule with the approximate value. This work not only provides an algorithmic big data solution for the studied problem, but also gives an insight into the design of streaming algorithms for other problems in the area of scheduling
Streaming Algorithms for Multitasking Scheduling with Shared Processing
In this paper, we design the first streaming algorithms for the problem of
multitasking scheduling on parallel machines with shared processing. In one
pass, our streaming approximation schemes can provide an approximate value of
the optimal makespan. If the jobs can be read in two passes, the algorithm can
find the schedule with the approximate value. This work not only provides an
algorithmic big data solution for the studied problem, but also gives an
insight into the design of streaming algorithms for other problems in the area
of scheduling
Hitting Subgraphs in Sparse Graphs and Geometric Intersection Graphs
We investigate a fundamental vertex-deletion problem called (Induced)
Subgraph Hitting: given a graph and a set of forbidden
graphs, the goal is to compute a minimum-sized set of vertices of such
that does not contain any graph in as an (induced)
subgraph. This is a generic problem that encompasses many well-known problems
that were extensively studied on their own, particularly (but not only) from
the perspectives of both approximation and parameterization. We focus on the
design of efficient approximation schemes, i.e., with running time
, which are also of significant
interest to both communities. Technically, our main contribution is a
linear-time approximation-preserving reduction from (Induced) Subgraph Hitting
on any graph class of bounded expansion to the same problem on
bounded degree graphs within . This yields a novel algorithmic
technique to design (efficient) approximation schemes for the problem on very
broad graph classes, well beyond the state-of-the-art. Specifically, applying
this reduction, we derive approximation schemes with (almost) linear running
time for the problem on any graph classes that have strongly sublinear
separators and many important classes of geometric intersection graphs (such as
fat-object graphs, pseudo-disk graphs, etc.). Our proofs introduce novel
concepts and combinatorial observations that may be of independent interest
(and, which we believe, will find other uses) for studies of approximation
algorithms, parameterized complexity, sparse graph classes, and geometric
intersection graphs. As a byproduct, we also obtain the first robust algorithm
for -Subgraph Isomorphism on intersection graphs of fat objects and
pseudo-disks, with running time .Comment: 60 pages, abstract shortened to fulfill the length limi
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
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