scholarly journals Boosting the Performance of SLS and CDCL Solvers by Preprocessor Tuning

10.29007/28ww ◽  
2018 ◽  
Author(s):  
Adrian Balint ◽  
Norbert Manthey
Keyword(s):  
State Of The Art ◽  
Performance Gain ◽  
Sat Solvers ◽  
Automated Algorithm ◽  
Algorithm Configuration ◽  
Art Performance ◽  
Preprocessing Technique

Preprocessing techniques are crucial for SAT solvers when it comes to reaching state-of-the-art performance as it was shown by the results of the last SAT Competitions. Theusefulness of a preprocessing technique depends highly on its own parameters, on the in-stances on which it is applied and on the used solver. In this paper we first give an extendedanalysis of the performance gain reached by using different preprocessing techniques in-dividually in combination with CDCL solvers on application instances and SLS solverson crafted instances. Further, we provide an analysis of combinations of preprocessingtechniques by means of automated algorithm configuration, where we search for optimalpreprocessor configurations for different scenarios. Our results show that the performanceof CDCL and especially of SLS solvers can be further improved when using appropriatepreprocessor configurations. The solvers augmented with the best found preprocessingconfigurations outperform the original solvers on the instances from the SAT Challenge2012, achieving new state-of-the-art results.

Chapter 12. Automated Configuration and Selection of SAT Solvers

2021 ◽  
Author(s):  
Holger H. Hoos ◽  
Frank Hutter ◽  
Kevin Leyton-Brown
Keyword(s):  
Research And Development ◽  
State Of The Art ◽  
Sat Solving ◽  
Sat Solvers ◽  
Effective Application ◽  
Automated Algorithm ◽  
Fertile Ground ◽  
Algorithm Configuration ◽  
Algorithmic Approaches ◽  
Selection Of

This chapter provides an introduction to the automated configuration and selection of SAT algorithms and gives an overview of the most prominent approaches. Since the early 2000s, these so-called meta-algorithmic approaches have played a major role in advancing the state of the art in SAT solving, giving rise to new ways of using and evaluating SAT solvers. At the same time, SAT has proven to be particularly fertile ground for research and development in the area of automated configuration and selection, and methods developed there have meanwhile achieved impact far beyond SAT, across a broad range of computationally challenging problems. Conceptually more complex approaches that go beyond “pure” algorithm configuration and selection are also discussed, along with some open challenges related to meta-algorithmic approaches, such as automated algorithm configuration and selection, to the tools based on these approaches, and to their effective application.

scholarly journals Automated Algorithm Selection: Survey and Perspectives

2019 ◽  
Vol 27 (1) ◽  
pp. 3-45 ◽  
Author(s):  
Pascal Kerschke ◽  
Holger H. Hoos ◽  
Frank Neumann ◽  
Heike Trautmann
Keyword(s):  
State Of The Art ◽  
Combinatorial Problems ◽  
The State ◽  
Problem Instance ◽  
Ai Planning ◽  
Algorithm Selection ◽  
Automated Algorithm ◽  
Continuous Problems ◽  
Algorithm Configuration ◽  
The One

It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

scholarly journals AutoFolio: An Automatically Configured Algorithm Selector

2015 ◽  
Vol 53 ◽  
pp. 745-778 ◽  
Author(s):  
Marius Lindauer ◽  
Holger H. Hoos ◽  
Frank Hutter ◽  
Torsten Schaub
Keyword(s):  
State Of The Art ◽  
Machine Learning Techniques ◽  
Problem Instance ◽  
Algorithm Selection ◽  
The Core ◽  
Automated Algorithm ◽  
Learning Techniques ◽  
Algorithm Configuration ◽  
Optimal Values ◽  
The One

Algorithm selection (AS) techniques -- which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently -- have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. This holds specifically for the machine learning techniques that form the core of current AS procedures, and for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered: (i) how to select an AS approach and (ii) how to set its parameters effectively. We address both of these problems simultaneously by using automated algorithm configuration. Specifically, we demonstrate that we can automatically configure claspfolio 2, which implements a large variety of different AS approaches and their respective parameters in a single, highly-parameterized algorithm framework. Our approach, dubbed AutoFolio, allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods. We demonstrate AutoFolio can significantly improve the performance of claspfolio 2 on 8 out of the 13 scenarios from the Algorithm Selection Library, leads to new state-of-the-art algorithm selectors for 7 of these scenarios, and matches state-of-the-art performance (statistically) on all other scenarios. Compared to the best single algorithm for each AS scenario, AutoFolio achieves average speedup factors between 1.3 and 15.4.

scholarly journals AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract)

2017 ◽  
Author(s):  
Marius Lindauer ◽  
Frank Hutter ◽  
Holger H. Hoos ◽  
Torsten Schaub
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
The State ◽  
Problem Instance ◽  
Algorithm Selection ◽  
Algorithm Configuration ◽  
Optimal Values ◽  
Art Performance ◽  
The One

Algorithm selection (AS) techniques -- which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently -- have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. In this extended abstract of our 2015 JAIR article of the same title, we summarize AutoFolio, which uses an algorithm configuration procedure to automatically select an AS approach and optimize its parameters for a given AS scenario. AutoFolio allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods and to automatically construct high-performance algorithm selectors. We demonstrate that AutoFolio was able to produce new state-of-the-art algorithm selectors for 7 well-studied AS scenarios and matches state-of-the-art performance statistically on all other scenarios. Compared to the best single algorithm for each AS scenario, AutoFolio achieved average speedup factors between 1.3 and 15.4.

scholarly journals Pitfalls and Best Practices in Algorithm Configuration

2019 ◽  
Vol 64 ◽  
pp. 861-893 ◽  
Author(s):  
Katharina Eggensperger ◽  
Marius Lindauer ◽  
Frank Hutter
Keyword(s):  
Best Practices ◽  
High Performance ◽  
State Of The Art ◽  
Parameter Tuning ◽  
Propositional Satisfiability ◽  
Ai Planning ◽  
Practical Applications ◽  
Automated Algorithm ◽  
Algorithm Configuration ◽  
Satisfiability Solving

Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning). Automated algorithm configuration methods have recently received much attention in the AI community since they replace tedious, irreproducible and error-prone manual parameter tuning and can lead to new state-of-the-art performance. However, practical applications of algorithm configuration are prone to several (often subtle) pitfalls in the experimental design that can render the procedure ineffective. We identify several common issues and propose best practices for avoiding them. As one possibility for automatically handling as many of these as possible, we also propose a tool called GenericWrapper4AC.

scholarly journals Greybox Algorithm Configuration

2021 ◽  
Author(s):  
Marie Anastacio
Keyword(s):  
State Of The Art ◽  
Automated Algorithm ◽  
Wide Range ◽  
Algorithm Configuration ◽  
Improve Algorithm ◽  
The Difference ◽  
Problem Instances ◽  
The Right ◽  
Parameter Values ◽  
Configuration Problem

The performance of state-of-the-art algorithms is highly dependent on their parameter values, and choosing the right configuration can make the difference between solving a problem in a few minutes or hours. Automated algorithm configurators have shown their efficiency on a wide range of applications. However, they still encounter limitations when confronted to a large number of parameters to tune or long algorithm running time. We believe that there is untapped knowledge that can be gathered from the elements of the configuration problem, such as the default value in the configuration space, the source code of the algorithm, and the distribution of the problem instances at hand. We aim at utilising this knowledge to improve algorithm configurators.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

scholarly journals Contour Detection for Fibre of Preserved Szechuan Pickle Based on Dilated Convolution

2019 ◽  
Vol 9 (13) ◽  
pp. 2684 ◽  
Author(s):  
Hongyang Li ◽  
Lizhuang Liu ◽  
Zhenqi Han ◽  
Dan Zhao
Keyword(s):  
Edge Detection ◽  
State Of The Art ◽  
Contour Detection ◽  
Experimental Results ◽  
Contour Method ◽  
Class Differences ◽  
Dilated Convolution ◽  
The Mean ◽  
Art Performance

Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automatic peeling device, is studied. The fibre contour is a kind of non-salient contour, characterized by big intra-class differences and small inter-class differences, meaning that the feature of the contour is not discriminative. The method called dilated-holistically-nested edge detection (Dilated-HED) is proposed to detect the fibre contour, which is built based on the HED network and dilated convolution. The experimental results for our dataset show that the Pixel Accuracy (PA) is 99.52% and the Mean Intersection over Union (MIoU) is 49.99%, achieving state-of-the-art performance.

Pruning by leveraging training dynamics

2021 ◽  
pp. 1-21
Author(s):  
Andrei C. Apostol ◽  
Maarten C. Stol ◽  
Patrick Forré
Keyword(s):  
State Of The Art ◽  
Object Classification ◽  
Compression Technique ◽  
Pruning Method ◽  
Model Compression ◽  
Art Performance

We propose a novel pruning method which uses the oscillations around 0, i.e. sign flips, that a weight has undergone during training in order to determine its saliency. Our method can perform pruning before the network has converged, requires little tuning effort due to having good default values for its hyperparameters, and can directly target the level of sparsity desired by the user. Our experiments, performed on a variety of object classification architectures, show that it is competitive with existing methods and achieves state-of-the-art performance for levels of sparsity of 99.6 % and above for 2 out of 3 of the architectures tested. Moreover, we demonstrate that our method is compatible with quantization, another model compression technique. For reproducibility, we release our code at https://github.com/AndreiXYZ/flipout.

Sign in / Sign up

Export Citation Format

Share Document