scholarly journals Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis

10.29007/vd18 ◽  
2018 ◽  
Author(s):  
Patrick Rodler ◽  
Wolfgang Schmid ◽  
Konstantin Schekotihin
Keyword(s):  
Search Space ◽  
Two Dimensions ◽  
Expected Number ◽  
Sequential Model ◽  
Optimal Measurement ◽  
Model Based ◽  
Space Reduction ◽  
Sequential Diagnosis ◽  
Full Search ◽  
Real World Problems

In this work we present strategies for (optimal) measurement computation and selection in model- based sequential diagnosis. In particular, assuming a set of leading diagnoses being given, we show how queries (sets of measurements) can be computed and optimized along two dimensions: expected number of queries and cost per query. By means of a suitable decoupling of two optimizations and a clever search space reduction the computations are done without any inference engine calls. For the full search space, we give a method requiring only a polynomial number of inferences and guarantee- ing query properties existing methods do not provide. Evaluation results using real-world problems indicate that the new method computes (virtually) optimal queries instantly independently of the size and complexity of the considered diagnosis problems.

scholarly journals Sequential model based optimization of partially defined functions under unknown constraints

2019 ◽  
Author(s):  
Candelieri Antonio
Keyword(s):  
Optimal Solution ◽  
Search Space ◽  
Bayesian Optimization ◽  
Feasible Region ◽  
Support Vector ◽  
Second Phase ◽  
Test Functions ◽  
Current Estimate ◽  
Sequential Model ◽  
Model Based

AbstractThis paper presents a sequential model based optimization framework for optimizing a black-box, multi-extremal and expensive objective function, which is also partially defined, that is it is undefined outside the feasible region. Furthermore, the constraints defining the feasible region within the search space are unknown. The approach proposed in this paper, namely SVM-CBO, is organized in two consecutive phases, the first uses a Support Vector Machine classifier to approximate the boundary of the unknown feasible region, the second uses Bayesian Optimization to find a globally optimal solution within the feasible region. In the first phase the next point to evaluate is chosen by dealing with the trade-off between improving the current estimate of the feasible region and discovering possible disconnected feasible sub-regions. In the second phase, the next point to evaluate is selected as the minimizer of the Lower Confidence Bound acquisition function but constrained to the current estimate of the feasible region. The main of the paper is a comparison with a Bayesian Optimization process which uses a fixed penalty value for infeasible function evaluations, under a limited budget (i.e., maximum number of function evaluations). Results are related to five 2D test functions from literature and 80 test functions, with increasing dimensionality and complexity, generated through the Emmental-type GKLS software. SVM-CBO proved to be significantly more effective as well as computationally efficient.

An Integrated Design Optimization Approach for Quantitative and Qualitative Search Space

2003 ◽  
Author(s):  
Victor Oduguwa ◽  
Rajkumar Roy ◽  
Didier Farrugia
Keyword(s):  
Real World ◽  
Design Problem ◽  
Integrated Design ◽  
Search Space ◽  
Optimization Approach ◽  
Design Optimisation ◽  
Solution Approach ◽  
Rod Rolling ◽  
Algorithmic Engineering ◽  
Real World Problems

Most of the algorithmic engineering design optimisation approaches reported in the literature aims to find the best set of solutions within a quantitative (QT) search space of the given problem while ignoring related qualitative (QL) issues. These QL issues can be very important and by ignoring them in the optimisation search, can have expensive consequences especially for real world problems. This paper presents a new integrated design optimisation approach for QT and QL search space. The proposed solution approach is based on design of experiment methods and fuzzy logic principles for building the required QL models, and evolutionary multi-objective optimisation technique for solving the design problem. The proposed technique was applied to a two objectives rod rolling problem. The results obtained demonstrate that the proposed solution approach can be used to solve real world problems taking into account the related QL evaluation of the design problem.

scholarly journals SMT-Based Contention-Free Task Mapping and Scheduling on 2D/3D SMART NoC with Mixed Dimension-Order Routing

2022 ◽  
Vol 19 (1) ◽  
pp. 1-21
Author(s):  
Daeyeal Lee ◽  
Bill Lin ◽  
Chung-Kuan Cheng
Keyword(s):  
System Performance ◽  
Search Space ◽  
Satisfiability Modulo Theories ◽  
Low Latency ◽  
Task Mapping ◽  
Single Cycle ◽  
Space Reduction ◽  
Reduction Techniques ◽  
2D And 3D ◽  
Mixed Dimension

SMART NoCs achieve ultra-low latency by enabling single-cycle multiple-hop transmission via bypass channels. However, contention along bypass channels can seriously degrade the performance of SMART NoCs by breaking the bypass paths. Therefore, contention-free task mapping and scheduling are essential for optimal system performance. In this article, we propose an SMT (Satisfiability Modulo Theories)-based framework to find optimal contention-free task mappings with minimum application schedule lengths on 2D/3D SMART NoCs with mixed dimension-order routing. On top of SMT’s fast reasoning capability for conditional constraints, we develop efficient search-space reduction techniques to achieve practical scalability. Experiments demonstrate that our SMT framework achieves 10× higher scalability than ILP (Integer Linear Programming) with 931.1× (ranges from 2.2× to 1532.1×) and 1237.1× (ranges from 4× to 4373.8×) faster average runtimes for finding optimum solutions on 2D and 3D SMART NoCs and our 2D and 3D extensions of the SMT framework with mixed dimension-order routing also maintain the improved scalability with the extended and diversified routing paths, resulting in reduced application schedule lengths throughout various application benchmarks.

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