452 research outputs found
An information-based neural approach to generic constraint satisfaction
AbstractA novel artificial neural network heuristic (INN) for general constraint satisfaction problems is presented, extending a recently suggested method restricted to boolean variables. In contrast to conventional ANN methods, it employs a particular type of non-polynomial cost function, based on the information balance between variables and constraints in a mean-field setting. Implemented as an annealing algorithm, the method is numerically explored on a testbed of Graph Coloring problems. The performance is comparable to that of dedicated heuristics, and clearly superior to that of conventional mean-field annealing
Stochastic local search: a state-of-the-art review
The main objective of this paper is to provide a state-of-the-art review, analyze and discuss stochastic local search techniques used for solving hard combinatorial problems. It begins with a short introduction, motivation and some basic notation on combinatorial problems, search paradigms and other relevant features of searching techniques as needed for background. In the following a brief overview of the stochastic local search methods along with an analysis of the state-of-the-art stochastic local search algorithms is given. Finally, the last part of the paper present and discuss some of the most latest trends in application of stochastic local search algorithms in machine learning, data mining and some other areas of science and engineering. We conclude with a discussion on capabilities and limitations of stochastic local search algorithms
An event-based architecture for solving constraint satisfaction problems
Constraint satisfaction problems (CSPs) are typically solved using
conventional von Neumann computing architectures. However, these architectures
do not reflect the distributed nature of many of these problems and are thus
ill-suited to solving them. In this paper we present a hybrid analog/digital
hardware architecture specifically designed to solve such problems. We cast
CSPs as networks of stereotyped multi-stable oscillatory elements that
communicate using digital pulses, or events. The oscillatory elements are
implemented using analog non-stochastic circuits. The non-repeating phase
relations among the oscillatory elements drive the exploration of the solution
space. We show that this hardware architecture can yield state-of-the-art
performance on a number of CSPs under reasonable assumptions on the
implementation. We present measurements from a prototype electronic chip to
demonstrate that a physical implementation of the proposed architecture is
robust to practical non-idealities and to validate the theory proposed.Comment: First two authors contributed equally to this wor
Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?
In recent years, combining neural networks with local search heuristics has
become popular in the field of combinatorial optimization. Despite its
considerable computational demands, this approach has exhibited promising
outcomes with minimal manual engineering. However, we have identified three
critical limitations in the empirical evaluation of these integration attempts.
Firstly, instances with moderate complexity and weak baselines pose a challenge
in accurately evaluating the effectiveness of learning-based approaches.
Secondly, the absence of an ablation study makes it difficult to quantify and
attribute improvements accurately to the deep learning architecture. Lastly,
the generalization of learned heuristics across diverse distributions remains
underexplored. In this study, we conduct a comprehensive investigation into
these identified limitations. Surprisingly, we demonstrate that a simple
learned heuristic based on Tabu Search surpasses state-of-the-art (SOTA)
learned heuristics in terms of performance and generalizability. Our findings
challenge prevailing assumptions and open up exciting avenues for future
research and innovation in combinatorial optimization
Efficient Probabilistic Computing with Stochastic Perovskite Nickelates
Probabilistic computing has emerged as a viable approach to solve hard
optimization problems. Devices with inherent stochasticity can greatly simplify
their implementation in electronic hardware. Here, we demonstrate intrinsic
stochastic resistance switching controlled via electric fields in perovskite
nickelates doped with hydrogen. The ability of hydrogen ions to reside in
various metastable configurations in the lattice leads to a distribution of
transport gaps. With experimentally characterized p-bits, a shared-synapse
p-bit architecture demonstrates highly-parallelized and energy-efficient
solutions to optimization problems such as integer factorization and
Boolean-satisfiability. The results introduce perovskite nickelates as scalable
potential candidates for probabilistic computing and showcase the potential of
light-element dopants in next-generation correlated semiconductors
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