5,157 research outputs found
Global Approaches for Facility Layout and VLSI Floorplanning
This paper summarizes recent advances in the global solution of several relevant facility layout problems
Global Approaches for Facility Layout and VLSI Floorplanning
This paper summarizes recent advances in the global solution of several relevant facility layout problems
ORCSolver: An Efficient Solver for Adaptive GUI Layout with OR-Constraints
OR-constrained (ORC) graphical user interface layouts unify conventional
constraint-based layouts with flow layouts, which enables the definition of
flexible layouts that adapt to screens with different sizes, orientations, or
aspect ratios with only a single layout specification. Unfortunately, solving
ORC layouts with current solvers is time-consuming and the needed time
increases exponentially with the number of widgets and constraints. To address
this challenge, we propose ORCSolver, a novel solving technique for adaptive
ORC layouts, based on a branch-and-bound approach with heuristic preprocessing.
We demonstrate that ORCSolver simplifies ORC specifications at runtime and our
approach can solve ORC layout specifications efficiently at near-interactive
rates.Comment: Published at CHI202
Readiness of Quantum Optimization Machines for Industrial Applications
There have been multiple attempts to demonstrate that quantum annealing and,
in particular, quantum annealing on quantum annealing machines, has the
potential to outperform current classical optimization algorithms implemented
on CMOS technologies. The benchmarking of these devices has been controversial.
Initially, random spin-glass problems were used, however, these were quickly
shown to be not well suited to detect any quantum speedup. Subsequently,
benchmarking shifted to carefully crafted synthetic problems designed to
highlight the quantum nature of the hardware while (often) ensuring that
classical optimization techniques do not perform well on them. Even worse, to
date a true sign of improved scaling with the number of problem variables
remains elusive when compared to classical optimization techniques. Here, we
analyze the readiness of quantum annealing machines for real-world application
problems. These are typically not random and have an underlying structure that
is hard to capture in synthetic benchmarks, thus posing unexpected challenges
for optimization techniques, both classical and quantum alike. We present a
comprehensive computational scaling analysis of fault diagnosis in digital
circuits, considering architectures beyond D-wave quantum annealers. We find
that the instances generated from real data in multiplier circuits are harder
than other representative random spin-glass benchmarks with a comparable number
of variables. Although our results show that transverse-field quantum annealing
is outperformed by state-of-the-art classical optimization algorithms, these
benchmark instances are hard and small in the size of the input, therefore
representing the first industrial application ideally suited for testing
near-term quantum annealers and other quantum algorithmic strategies for
optimization problems.Comment: 22 pages, 12 figures. Content updated according to Phys. Rev. Applied
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