80 research outputs found
Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems
The proximal gradient algorithm has been popularly used for convex
optimization. Recently, it has also been extended for nonconvex problems, and
the current state-of-the-art is the nonmonotone accelerated proximal gradient
algorithm. However, it typically requires two exact proximal steps in each
iteration, and can be inefficient when the proximal step is expensive. In this
paper, we propose an efficient proximal gradient algorithm that requires only
one inexact (and thus less expensive) proximal step in each iteration.
Convergence to a critical point %of the nonconvex problem is still guaranteed
and has a convergence rate, which is the best rate for nonconvex
problems with first-order methods. Experiments on a number of problems
demonstrate that the proposed algorithm has comparable performance as the
state-of-the-art, but is much faster
A Method of Electric Power Equipment Integrated Modeling Based on Carrier
AbstractModeling of electric power equipment is very important in power system. But there are several problems by the current method, such as information is incomplete. This paper proposes a method of electric power equipment integrated modeling based on carrier. Electric power equipment model are numbered and classification, then all of them are knowledge-related through knowledgebase. In the design, user-defined of meta-data structure is supported, which can be integrated with a variety of heterogeneous systems. Finally, the model can be displayed with the dynamic three-dimensional way after rendering by texturing techniques. Experiment indicates that the proposed model contain more information and more realistic
A Comprehensive Empirical Investigation on Failure Clustering in Parallel Debugging
The clustering technique has attracted a lot of attention as a promising
strategy for parallel debugging in multi-fault scenarios, this heuristic
approach (i.e., failure indexing or fault isolation) enables developers to
perform multiple debugging tasks simultaneously through dividing failed test
cases into several disjoint groups. When using statement ranking representation
to model failures for better clustering, several factors influence clustering
effectiveness, including the risk evaluation formula (REF), the number of
faults (NOF), the fault type (FT), and the number of successful test cases
paired with one individual failed test case (NSP1F). In this paper, we present
the first comprehensive empirical study of how these four factors influence
clustering effectiveness. We conduct extensive controlled experiments on 1060
faulty versions of 228 simulated faults and 141 real faults, and the results
reveal that: 1) GP19 is highly competitive across all REFs, 2) clustering
effectiveness decreases as NOF increases, 3) higher clustering effectiveness is
easier to achieve when a program contains only predicate faults, and 4)
clustering effectiveness remains when the scale of NSP1F is reduced to 20%
SURE: A Visualized Failure Indexing Approach using Program Memory Spectrum
Failure indexing is a longstanding crux in software testing and debugging,
the goal of which is to automatically divide failures (e.g., failed test cases)
into distinct groups according to the culprit root causes, as such multiple
faults in a faulty program can be handled independently and simultaneously.
This community has long been plagued by two challenges: 1) The effectiveness of
division is still far from promising. Existing techniques only employ a limited
source of run-time data (e.g., code coverage) to be failure proximity, which
typically delivers unsatisfactory results. 2) The outcome can be hardly
comprehensible. A developer who receives the failure indexing result does not
know why all failures should be divided the way they are. This leads to
difficulties for developers to be convinced by the result, which in turn
affects the adoption of the results. To tackle these challenges, in this paper,
we propose SURE, a viSUalized failuRe indExing approach using the program
memory spectrum. We first collect the run-time memory information at preset
breakpoints during the execution of failed test cases, and transform it into
human-friendly images (called program memory spectrum, PMS). Then, any pair of
PMS images that serve as proxies for two failures is fed to a trained Siamese
convolutional neural network, to predict the likelihood of them being triggered
by the same fault. Results demonstrate the effectiveness of SURE: It achieves
101.20% and 41.38% improvements in faults number estimation, as well as 105.20%
and 35.53% improvements in clustering, compared with the state-of-the-art
technique in this field, in simulated and real-world environments,
respectively. Moreover, we carry out a human study to quantitatively evaluate
the comprehensibility of PMS, revealing that this novel type of representation
can help developers better comprehend failure indexing results.Comment: Due to the limitation "The abstract field cannot be longer than 1,920
characters", the abstract here is shorter than that in the PDF fil
Combating Bilateral Edge Noise for Robust Link Prediction
Although link prediction on graphs has achieved great success with the
development of graph neural networks (GNNs), the potential robustness under the
edge noise is still less investigated. To close this gap, we first conduct an
empirical study to disclose that the edge noise bilaterally perturbs both input
topology and target label, yielding severe performance degradation and
representation collapse. To address this dilemma, we propose an
information-theory-guided principle, Robust Graph Information Bottleneck
(RGIB), to extract reliable supervision signals and avoid representation
collapse. Different from the basic information bottleneck, RGIB further
decouples and balances the mutual dependence among graph topology, target
labels, and representation, building new learning objectives for robust
representation against the bilateral noise. Two instantiations, RGIB-SSL and
RGIB-REP, are explored to leverage the merits of different methodologies, i.e.,
self-supervised learning and data reparameterization, for implicit and explicit
data denoising, respectively. Extensive experiments on six datasets and three
GNNs with diverse noisy scenarios verify the effectiveness of our RGIB
instantiations. The code is publicly available at:
https://github.com/tmlr-group/RGIB.Comment: Accepted by NeurIPS 202
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