Machine learning for electronic design automation: A survey
With the down-scaling of CMOS technology, the design complexity of very large-scale
integrated is increasing. Although the application of machine learning (ML) techniques in …
integrated is increasing. Although the application of machine learning (ML) techniques in …
Machine learning for automated theorem proving: Learning to solve SAT and QSAT
SB Holden - Foundations and Trends® in Machine Learning, 2021 - nowpublishers.com
The decision problem for Boolean satisfiability, generally referred to as SAT, is the
archetypal NP-complete problem, and encodings of many problems of practical interest exist …
archetypal NP-complete problem, and encodings of many problems of practical interest exist …
Learning combinatorial optimization algorithms over graphs
The design of good heuristics or approximation algorithms for NP-hard combinatorial
optimization problems often requires significant specialized knowledge and trial-and-error …
optimization problems often requires significant specialized knowledge and trial-and-error …
Algorithm selection for combinatorial search problems: A survey
L Kotthoff - Data mining and constraint programming: Foundations …, 2016 - Springer
Abstract The Algorithm Selection Problem is concerned with selecting the best algorithm to
solve a given problem on a case-by-case basis. It has become especially relevant in the last …
solve a given problem on a case-by-case basis. It has become especially relevant in the last …
Learning rate based branching heuristic for SAT solvers
In this paper, we propose a framework for viewing solver branching heuristics as
optimization algorithms where the objective is to maximize the learning rate, defined as the …
optimization algorithms where the objective is to maximize the learning rate, defined as the …
SATzilla: portfolio-based algorithm selection for SAT
It has been widely observed that there is no single" dominant" SAT solver; instead, different
solvers perform best on different instances. Rather than following the traditional approach of …
solvers perform best on different instances. Rather than following the traditional approach of …
[HTML][HTML] Aslib: A benchmark library for algorithm selection
The task of algorithm selection involves choosing an algorithm from a set of algorithms on a
per-instance basis in order to exploit the varying performance of algorithms over a set of …
per-instance basis in order to exploit the varying performance of algorithms over a set of …
Learning local search heuristics for boolean satisfiability
We present an approach to learn SAT solver heuristics from scratch through deep
reinforcement learning with a curriculum. In particular, we incorporate a graph neural …
reinforcement learning with a curriculum. In particular, we incorporate a graph neural …
Venture: a higher-order probabilistic programming platform with programmable inference
We describe Venture, an interactive virtual machine for probabilistic programming that aims
to be sufficiently expressive, extensible, and efficient for general-purpose use. Like Church …
to be sufficiently expressive, extensible, and efficient for general-purpose use. Like Church …
[图书][B] Reactive search and intelligent optimization
Reactive Search integrates sub-symbolic machine learning techniques into search
heuristics for solving complex optimization problems. By automatically adjusting the working …
heuristics for solving complex optimization problems. By automatically adjusting the working …