Related papers: Less Noise, More Signal: DRR for Better Optimizations of SE Tasks

Less Noise, More Signal: DRR for Better Optimizations of SE Tasks

URL: http://arxiv.org/abs/2503.21086v1
Date: Thu, 27 Mar 2025 02:02:06 GMT
Title: Less Noise, More Signal: DRR for Better Optimizations of SE Tasks
Authors: Andre Lustosa, Tim Menzies,
Abstract summary: This paper introduces the Dimensionality Reduction Ratio (DRR), a new metric for predicting when lightweight algorithms suffice.<n>DRR pinpoints "simple" tasks where costly methods like DEHB are overkill.
Score: 11.166755101891402
License: http://creativecommons.org/licenses/by/4.0/
Abstract: SE analytics problems do not always need complex AI. Better and faster solutions can sometimes be obtained by matching the complexity of the problem to the complexity of the solution. This paper introduces the Dimensionality Reduction Ratio (DRR), a new metric for predicting when lightweight algorithms suffice. Analyzing SE optimization problems from software configuration to process decisions and open-source project health we show that DRR pinpoints "simple" tasks where costly methods like DEHB (a state-of-the-art evolutionary optimizer) are overkill. For high-DRR problems, simpler methods can be just as effective and run two orders of magnitude faster.

Related papers

Quality-Diversity Algorithms Can Provably Be Helpful for Optimization [24.694984679399315]
Quality-Diversity (QD) algorithms aim to find a set of high-performing, yet diverse solutions. This paper tries to shed some light on the optimization ability of QD algorithms via rigorous running time analysis.
arXiv Detail & Related papers (2024-01-19T07:40:24Z)
Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization [6.896308995955336]
Gradient-based minimax optimal algorithms have promoted the development of continuous optimization and machine learning. In this paper, we open up a new way to design and analyze gradient-based algorithms with direct applications in machine learning.
arXiv Detail & Related papers (2023-12-06T01:16:10Z)
CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity [110.50364486645852]
Communication complexity has become a major bottleneck for speeding up training and scaling up machine numbers. We propose Common Om REOm, which can be used to compress information transmitted between machines.
arXiv Detail & Related papers (2023-09-23T08:45:27Z)
Learning for Robust Combinatorial Optimization: Algorithm and Application [26.990988571097827]
Learning to optimize (L2O) has emerged as a promising approach to solving optimization problems by exploiting the strong prediction power of neural networks. In this paper, we propose a novel learning-based optimization, called LRCO, which quickly outputs a robust solution in the presence of uncertain context. Our results highlight that LRCO can greatly reduce the worst-case cost and runtime, while having a very low complexity.
arXiv Detail & Related papers (2021-12-20T07:58:50Z)
Neural Stochastic Dual Dynamic Programming [99.80617899593526]
We introduce a trainable neural model that learns to map problem instances to a piece-wise linear value function. $nu$-SDDP can significantly reduce problem solving cost without sacrificing solution quality.
arXiv Detail & Related papers (2021-12-01T22:55:23Z)
Enhanced Bilevel Optimization via Bregman Distance [104.96004056928474]
We propose a bilevel optimization method based on Bregman Bregman functions. We also propose an accelerated version of SBiO-BreD method (ASBiO-BreD) by using the variance-reduced technique.
arXiv Detail & Related papers (2021-07-26T16:18:43Z)
A Two-stage Framework and Reinforcement Learning-based Optimization Algorithms for Complex Scheduling Problems [54.61091936472494]
We develop a two-stage framework, in which reinforcement learning (RL) and traditional operations research (OR) algorithms are combined together. The scheduling problem is solved in two stages, including a finite Markov decision process (MDP) and a mixed-integer programming process, respectively. Results show that the proposed algorithms could stably and efficiently obtain satisfactory scheduling schemes for agile Earth observation satellite scheduling problems.
arXiv Detail & Related papers (2021-03-10T03:16:12Z)
Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
arXiv Detail & Related papers (2021-01-07T08:00:02Z)
Adaptive Sampling for Best Policy Identification in Markov Decision Processes [79.4957965474334]
We investigate the problem of best-policy identification in discounted Markov Decision (MDPs) when the learner has access to a generative model. The advantages of state-of-the-art algorithms are discussed and illustrated.
arXiv Detail & Related papers (2020-09-28T15:22:24Z)
Towards Solving Large-scale Expensive Optimization Problems Efficiently Using Coordinate Descent Algorithm [3.1600708674008393]
We propose a modified Coordinate Descent algorithm (MCD) to tackle LSEGO problems with a limited computational budget. Our proposed algorithm benefits from two leading steps, namely, finding the region of interest and then shrinkage of the search space by folding it into the half with exponential speed. The proposed algorithm is compared with cooperative co-evolution with delta grouping on 20 benchmark functions with dimension 1000.
arXiv Detail & Related papers (2020-03-07T22:48:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.