Gradient Optimization Research Articles

Optimal stochastic gradient descent algorithm for filtering

Stochastic Gradient Descent (SGD) is a fundamental optimization technique in machine learning, due to its efficiency in handling large-scale data. Unlike typical SGD applications, which rely on stochastic approximations, this work explores the convergence properties of SGD from a deterministic perspective. We address the crucial aspect of learning rate settings, a common obstacle in optimizing SGD performance, particularly in complex environments. In contrast to traditional methods that often provide convergence results based on statistical expectations (which are usually not justified), our approach introduces universally applicable learning rates. These rates ensure that a model trained with SGD matches the performance of the best linear filter asymptotically, applicable irrespective of the data sequence length and independent of statistical assumptions about the data. By establishing learning rates that scale as μ=O(1t), we offer a solution that sidesteps the need for prior data knowledge, a prevalent limitation in real-world applications. To this end, we provide a robust framework for SGD's application across varied settings, guaranteeing convergence results that hold under both deterministic and stochastic scenarios without any underlying assumptions.

Dynamic range and optimization strategies for radiochromic film calibration using gradient radiation fields.

This investigation aimed to optimize gradient positioning for radiochromic film calibration to facilitate a uniform distribution of calibration points. The study investigated the influence of various parameters on gradient dose profiles generated by a physical wedge, assessing their impact on the field's dose dynamic range, a scalar quantity representing the span of absorbed doses. Numerical parameterization of the physical wedge profile was used to visualize and quantify the impact of field size, depth, and energy on the dynamic range of dose gradients. This concept enabled the optimization of the gradient positioning and estimation of the necessary number of exposures for the desired calibration dose range. An optimization algorithm based on histogram bin height minimization was developed and presented. The maximum dynamic range was achieved with a 20 20 field size at 5 cm depth. Optimization of wedge gradient positioning yielded the most uniform dose distribution with 7 exposures for the [1,10] Gy range and 8 exposures for the [1,20] Gy range. Film calibration using gradients centered at 1.6, 3, 3.5, and 7 Gy central axis (CAX), obtained through optimized gradient positioning, was showcased. The presented work demonstrates the potential for an improved film calibration process, with efficient material utilization and enhanced dosimetric accuracy for clinical applications. While the method was described for the use of a physical wedge, the methodology can be easily extended to the use of a more convenient dynamicwedge.

Using Micrometer Scale X-Ray CT and Pore Network Modeling to Assess High-Energy Bi-Layer Li-Ion Battery Cathode Structures

The optimization of cathode pore space is pivotal for enhancing the rate capability of lithium-ion battery electrodes. Previous studies on simulated cathodes have highlighted the importance of achieving an optimal porosity gradient, with a gradual increase from the current collector side to the separator side to accommodate the growing demand for ion transport in that direction [1]–[3]. In this research, we address this requirement by introducing and investigating bi-layer NCM 811 cathodes. These cathodes consist of a slurry-coated dense bottom layer and a spray-coated highly porous top layer with varying relative thickness to approximate the desired porosity gradient. Employing Micrometer-scale X-ray CT characterization and pore network modeling, we evaluate the structural features, demonstrating the effectiveness of the design, and quantify essential parameters such as porosity and tortuosity. Furthermore, the cathodes are integrated into lithium anode half-cells and subjected to electrochemical cycling at different rates to assess ion transport and rate capability. Our findings reveal that to achieve the highest discharge spatial energy density above 1C for cathodes approximately 100μm thick, the compressed bottom layer with 30% porosity should have a thickness of 60μm, while the highly porous top layer with around 47% porosity should be 40μm thick.Reference:[1] A. Shodiev et al., “Deconvoluting the benefits of porosity distribution in layered electrodes on the electrochemical performance of Li-ion batteries,” Energy Storage Mater., vol. 47, pp. 462–471, May 2022, doi: 10.1016/j.ensm.2022.01.058.[2] X. Lu et al., “3D microstructure design of lithium-ion battery electrodes assisted by X-ray nano-computed tomography and modelling,” Nat. Commun., vol. 11, no. 1, p. 2079, Apr. 2020, doi: 10.1038/s41467-020-15811-x.[3] K. Song, C. Zhang, N. Hu, X. Wu, and L. Zhang, “High performance thick cathodes enabled by gradient porosity,” Electrochimica Acta, vol. 377, p. 138105, May 2021, doi: 10.1016/j.electacta.2021.138105.

Development of surfactant formulation for high-temperature off-shore carbonate reservoirs.

The residual oil left behind after water flooding in petroleum reservoirs can be mobilized by surfactant formulations that yield ultralow interfacial tension (IFT) with oil. However, finding ultralow IFT surfactant formulations is difficult for high-temperature, off-shore, carbonate reservoirs. These reservoirs are often water-flooded with seawater (with a lot of divalent ions), which is often incompatible with many surfactants at high temperatures. The goal of this research is to develop a surfactant formulation for an off-shore carbonate reservoir at 100°C previously flooded by seawater. Surfactant-oil-brine phase behavior was studied for formulations, starting from a single surfactant to mixtures of surfactants and a co-solvent. Mixtures of three surfactants and one co-solvent were needed to produce ultralow IFT formulations for the oil of interest. The surfactant system with polymer mobility control was tested in crushed reservoir rock packs. The cumulative oil recovery was >99% for the surfactant-polymer (SP) flood with an optimal salinity gradient. The constant salinity SP floods with seawater increased oil recovery significantly beyond the water flood (cumulative oil recovery >91%), even though the recovery was lower than that of the optimal salinity gradient SP flood. Our experimental work demonstrates the effectiveness of the surfactant formulation for a high-temperature carbonate reservoir at seawater salinity.

Federated dual averaging learning algorithm with delayed gradients for composite optimization

Federated learning offers a promising paradigm for training machine learning models in distributed edge networks. This paper focuses on a critical aspect of federated learning, specifically examining the influence of communication delays between the server and edge devices. We study the problem of federated composite optimization (FCO) in the scenario where communication delays are introduced due to client-sides. To tackle this issue, we propose a federated dual averaging learning algorithm that incorporates delayed gradients. We conduct a thorough convergence analysis of our algorithm taking into account the impact of the delayed gradient information. Our analysis demonstrates that under the assumption of bounded expected random delays, the algorithm exhibits the asymptotic convergence towards the optimal value. Furthermore, we present explicit convergence rates of the proposed algorithm for fixed and random delays. Our results show that the impact of delays on FCO problems with smooth loss functions gradually becomes negligible. Finally, to demonstrate the efficacy of the proposed algorithm, we conduct numerical simulations on the FEMNIST dataset utilizing ℓ1-regularized logistic regression. These experimental results confirm our findings that the algorithm converges effectively even in the presence of delays.

Understanding world models through multi-step pruning policy via reinforcement learning

In model-based reinforcement learning, the conventional approach to addressing world model bias is to use gradient optimization methods. However, using a singular policy from gradient optimization methods in response to a world model bias inevitably results in an inherently biased policy. This is because of constraints on the imperfect and dynamic data of state-action pairs. The gap between the world model and the real environment can never be completely eliminated. This article introduces a novel approach that explores a variety of policies instead of focusing on either world model bias or singular policy bias. Specifically, we introduce the Multi-Step Pruning Policy (MSPP), which aims to reduce redundant actions and compress the action and state spaces. This approach encourages a different perspective within the same world model. To achieve this, we use multiple pruning policies in parallel and integrate their outputs using the cross-entropy method. Additionally, we provide a convergence analysis of the pruning policy theory in tabular form and an updated parameter theoretical framework. In the experimental section, the newly proposed MSPP method demonstrates a comprehensive understanding of the world model and outperforms existing state-of-the-art model-based reinforcement learning baseline techniques.

Recovery of indium by solvent extraction with crown ether in the presence of KCl and stripping with HCl: A mechanistic study

Hydration of In3+ is the main factor limiting its extraction efficiency from an aqueous solution during a liquid-liquid extraction process. In this study, KCl was introduced into the aqueous solution to facilitate the formation of InCl4− of low charge density, which is expected to possess much weaker hydration compared with In3+, promoting the solvent extraction of indium. The crown ethers (CEs) with varied cavity sizes, benzo-18-crown-6 (B18C6), benzo-15-crown-5 (B15C5), and benzo-12-crown-4 (B12C4), were synthesized. The extraction performance of the CEs toward indium in the presence of sufficient KCl in the aqueous solution was investigated. The liquid-liquid extraction process was analyzed theoretically based on density functional theory (DFT) from the aspects of thermodynamics, geometric structure optimization, electrostatic potential (ESP), and independent gradient model (IGM). The theoretical evaluations agreed well with the experimental results that the hydration of indium could be significantly weakened through the formation of InCl4− and the complexation ability of the CEs toward indium is in the order of B18C6 > B15C5 > B12C4. The complexation mechanism between the CEs and indium during the extraction process was further explored with the assistance of 1H NMR spectrum and SEM-EDS. The results indicate that crown ether coordinates with K+ to form [CE-K]+ at the two-phase interface, which further associates with InCl4− to create the complex of CE-KInCl4, realizing the efficient indium extraction. Moreover, B18C6 showed excellent selectivity toward In3+ over the competing ions such as Fe3+, Al3+, Zn2+, Sn2+ and Ca2+ in a complex system. Indium could be efficiently recovered from the loaded organic phase by using 1 M HCl as the stripping agent with a stripping efficiency of 98.1%.

Optimal measurement-based cost gradient estimate for feedback real-time optimization

This work presents a simple and efficient way of estimating the steady-state cost gradient Ju based on available uncertain measurements y. The main motivation is to control Ju to zero in order to minimize the economic cost J. For this purpose, it is shown that the optimal cost gradient estimate for unconstrained operation is simply Jˆu=H(ym−y∗) where H is a constant matrix, ym is the vector of measurements and y∗ is their nominally unconstrained optimal value. The derivation of the optimal H-matrix is based on existing methods for self-optimizing control and therefore the result is exact for a convex quadratic economic cost J with linear constraints and measurements. The optimality holds locally in other cases. For the constrained case, the unconstrained gradient estimate Jˆu should be multiplied by the nullspace of the active constraints and the resulting “reduced gradient” controlled to zero.

Separation of weak acids, neutral compounds, and permanent anions using sequential elution liquid chromatography with tandem columns

This paper discusses the development of an analytical method by an alternative separation approach, sequential elution liquid chromatography (SE-LC), to separate permanently charged ions (anions), weak acids, and neutral compounds using anion exchange and reversed-phase columns in tandem. SE-LC separates classes of compounds by group by employing two or more elution modes. Advantages to using SE-LC over conventional HPLC are a greater peak capacity and a reduced separation disorder. Importantly, the same HPLC as used for a conventional HPLC separation may be used to afford a successful SE-LC separation. Mobile phase selection and gradient optimization are integral for a successful SE-LC class separation of permanent anions, weak acids, and neutral compounds and will be discussed in detail in this paper. The most successful (best resolution and repeatability) SE-LC separation was achieved by applying isocratic elution at low pH to elute the weak acids, followed by an acetonitrile gradient to elute the neutral compounds, and last a sodium methanesulfonate gradient to elute the anionic compounds using a superficially porous C18 column coupled with a strong anion exchange (SAX) column. Repeatability (RSD) in the retention times and peak areas of the analytes was less than 0.25 % and 1.5 %, respectively.

Boosting the transferability of adversarial attacks with global momentum initialization

Deep Neural Networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding human-imperceptible perturbations to the benign inputs. Simultaneously, adversarial examples exhibit transferability across models, enabling practical black-box attacks. However, existing methods are still incapable of achieving the desired transfer attack performance. In this work, focusing on gradient optimization and consistency, we analyze the gradient elimination phenomenon as well as the local momentum optimum dilemma. To tackle these challenges, we introduce Global Momentum Initialization (GI), providing global momentum knowledge to mitigate gradient elimination. Specifically, we perform gradient pre-convergence before the attack and a global search during this stage. GI seamlessly integrates with existing transfer methods, significantly improving the success rate of transfer attacks by an average of 6.4% under various advanced defense mechanisms compared to the state-of-the-art method. Ultimately, GI demonstrates strong transferability in both image and video attack domains. Particularly, when attacking advanced defense methods in the image domain, it achieves an average attack success rate of 95.4%. The code is available at https://github.com/Omenzychen/Global-Momentum-Initialization.

Optimal bidding strategy for the price-maker virtual power plant in the day-ahead market based on multi-agent twin delayed deep deterministic policy gradient algorithm

In recent years, distributed energy resources (DERs) have developed greatly, and the total capacity of resources aggregated by virtual power plants (VPPs) has increased. The role VPP plays in the market is changing. It is necessary to explore VPP's bidding strategy in the electricity market based on the impact of VPP on the clearing price. At the same time, the strategy game of different market players will also impact VPP's strategy. The multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm is used to solve the problem of price-maker VPP participation in the day-ahead (DA) market. VPP is required to submit the power and prices of electricity in multi-segments at different times. In the market bidding process, the main market participants are considered as agents. Iterative refinement of each player's strategy is achieved through multi-agent reinforcement learning, with the primary aim of maximizing revenue. For the VPP, the MATD3 algorithm improved the reward by approximately 65 % and increased the convergence speed by 17 % compared to the multi-agent deep deterministic policy gradient and multi-agent proximal policy optimization algorithms. The effects of different influences on VPP's bidding strategy are analyzed, which can provide a decision-making reference for market participants.

Effects of a new synthetic Fe-PAM flocculants on filtration and consolidation of bentonite slurry

To improve the dewatering performances of the waste slurry, a new Fe-Polyacrylamide (Fe-PAM) flocculant was synthesized. Comparative analysis with various ionic forms of polyacrylamide (PAM) reveals that the Fe-PAM flocculant has the shortest flocculation time, the lowest optimal shear gradient for flocculation, and the best filtration performance. The capillary suction time (CST) of Fe-PAM treated slurry was improved from 262.3 s (raw sample) to 6.0 s. Moreover, sludge treated with Fe-PAM exhibits the lowest porosity under the same consolidation pressure and the highest permeability at the same void ratio. In addition, mercury intrusion porosimetry (MIP) results indicated that the Fe-PAM-treated sample features the highest number of largesized inter-aggregate pores (average value of 184.2 nm and median value of 23.2 nm) and the greatest pore connectivity (60.55 %), contributing to its superior permeability. The effectiveness of Fe-PAM is further elaborated through a synergistic flocculation mechanism, encompassing the electrical neutralization effect of the inorganic metal Fe3+ and the adsorption bridging effect of PAM's long-chains. This study demonstrates the potential of the synthesized Fe-PAM flocculant to enhance the dewatering efficiency of high-water content slurry and offers valuable insights for synthesizing advanced flocculants in slurry treatment engineering.

Thermal properties of cooling tube battery pack embedded with triangle umbrella-shaped cellular structure

To further enhance heat dissipation efficiency and temperature uniformity of battery pack, a novel cooling tube battery pack embedded with triangle umbrella-shaped cellular structure (TUCS) with negative Poisson’s ratio (NPR) is proposed. Firstly, heat generation models of battery cell are derived and temperature rise tests are conducted to verify the accuracy of heat generation theory and simulation models of lithium battery cell. Meanwhile, cooling tube battery pack with bidirectional serpentine tube is established, and the influences of different flow rates and directions of coolant, diameter of cooling tube and spoiler columns on heat dissipation efficiency of cooling tube battery pack are investigated through simulation analysis. Moreover, relative density of TUCS is calculated and thermal conductivity expressions of TUCS are derived by Maxwell-Eucken model. Subsequently, the embedded TUCS is taken as the medium to conduct heat between cooling tubes and battery modules. Then, the influences of structural parameters of TUCS on heat dissipation efficiency of cooling tube battery pack are determined. Further, the optimal TUCS with the needs of high heat dissipation efficiency, superior temperature uniformity and more lightweight space of battery pack is achieved. Similarly, density gradient of the TUCS can be defined and impacts of TUCS with density gradient distribution on thermal performances of battery pack are investigated. Eventually, the highest temperature and maximum temperature difference of the optimal cooling tube battery pack, cooling tube battery pack embedded with the optimal TUCS and cooling tube battery pack embedded with the optimal gradient distribution TUCS are obtained. Compared with the optimal cooling tube battery pack, the highest temperatures of another two cooling tube battery packs decrease 16.50 % and 16.48 %, and the corresponding maximum temperature differences decrease 93.06 % and 93.20 %, respectively. Meanwhile, the highest temperature and maximum temperature difference of another two battery modules are 303.76 K and 3.76 K at 5C discharge rate. The proposed cooling tube battery pack with the optimal gradient distribution TUCS can decrease the highest temperature and maximum temperature difference effectively.

You only compress once: Towards effective and elastic BERT compression via exploit–explore stochastic nature gradient

Despite superior performance on various natural language processing tasks, pre-trained models such as BERT are challenged by deploying on resource-constraint devices. Most existing model compression approaches require re-compression or fine-tuning across diverse constraints to accommodate various hardware deployments. This practically limits the further application of model compression. Moreover, the ineffective training and searching process of existing elastic compression paradigms (Wang et al., 2020; Cai et al., 2020) prevents the direct migration to BERT compression. Motivated by the necessity of efficient inference across various constraints on BERT, we propose a novel approach, YOCO-BERT, to achieve compress once and deploy everywhere. Specifically, we first construct a huge search space with 1013 architectures, which covers nearly all configurations in BERT model. Then, we propose a novel stochastic nature gradient optimization method to guide the generation of optimal candidate architecture which could keep a balanced trade-off between explorations and exploitation. When a certain resource constraint is given, a lightweight distribution optimization approach is utilized to obtain the optimal network for target deployment without fine-tuning. Compared with state-of-the-art algorithms, YOCO-BERT provides more compact models, yet achieving 2.1%–4.5% average accuracy improvement on the GLUE benchmark. Besides, YOCO-BERT is also more effective, e.g., the training complexity is O(1) for N different devices. Codes available https://github.com/MAC-AutoML/YOCO-BERT.

Discrete Adjoint Optimization Method for Low-Boom Aircraft Design Using Equivalent Area Distribution

This paper introduces a low-boom aircraft optimization design method guided by equivalent area distribution, which effectively improves the intuitiveness and refinement of inverse design. A gradient optimization method based on discrete adjoint equations is proposed to achieve the fast solution of the gradient information of target equivalent area distribution relative to design variables and to drive the aerodynamic shape update to the optimal solution. An optimization experiment is carried out based on a self-developed supersonic civil aircraft configuration with engines. The results show that the equivalent area distribution adjoint equation can accurately solve the gradient information. After optimization, the sonic boom level of the aircraft was reduced by 13.2 PLdB, and the drag coefficient was reduced by 60.75 counts. Moreover, the equivalent area distribution adjoint optimization method has outstanding advantages, such as high sensitivity and fast convergence speed, and can take both the low sonic boom and the low drag force of the aircraft into account, providing a powerful tool for the comprehensive optimization design of supersonic civil aircraft by considering sonic boom and aerodynamic force.

A tabular data generation framework guided by downstream tasks optimization

Recently, generative models have been gradually emerging into the extended dataset field, showcasing their advantages. However, when it comes to generating tabular data, these models often fail to satisfy the constraints of numerical columns, which cannot generate high-quality datasets that accurately represent real-world data and are suitable for the intended downstream applications. Responding to the challenge, we propose a tabular data generation framework guided by downstream task optimization (TDGGD). It incorporates three indicators into each time step of diffusion generation, using gradient optimization to align the generated fake data. Unlike the traditional strategy of separating the downstream task model from the upstream data synthesis model, TDGGD ensures that the generated data has highly focused columns feasibility in upstream real tabular data. For downstream task, TDGGD strikes the utility of tabular data over solely pursuing statistical fidelity. Through extensive experiments conducted on real-world tables with explicit column constraints and tables without explicit column constraints, we have demonstrated that TDGGD ensures increasing data volume while enhancing prediction accuracy. To the best of our knowledge, this is the first instance of deploying downstream information into a diffusion model framework.

Accelerated optimization through time-scale analysis of inertial dynamics with asymptotic vanishing and Hessian-driven dampings

ABSTRACT Gradient optimization algorithms can be effectively studied from the perspective of ordinary differential equations (ODEs), where these algorithms are obtained through the temporal discretization of the continuous dynamics. This approach provides a powerful tool to comprehend acceleration phenomena in optimization, thanks to time-scale techniques and Lyapunov analysis. In the context of a Hilbert setting, our study focuses on the rapid optimization properties of inertial dynamics, which combine asymptotically vanishing viscous damping with Hessian-driven damping. These dynamics are derived as high-resolution ODEs from both the Nesterov accelerated gradient method and the Ravine method. For a general differentiable convex function f, choosing the viscous damping coefficient in the form of α / t with 3 $ ]]> α > 3 guarantees an inverse quadratic convergence rate of the values, i.e. o ( 1 / t 2 ) , the weak convergence of trajectories towards optimal solutions, and the fast convergence of gradients towards zero. By carefully scaling the dynamics temporally and based on the tuning of the damping coefficient in front of the Hessian term, we identify the limit dynamic as α becomes large. In particular, we examine the case where the limit dynamic corresponds to the Levenberg-Marquardt regularization of Newton's continuous method. This explanation accounts for the aforementioned fast convergence properties and provides fresh insights into the complexity of these methods, which play a central role in optimization for high-dimensional problems. The interplay between numerical algorithms and continuous-time ODEs plays a fundamental role in our analysis for the design and understanding of accelerated optimization algorithms. Numerical experiments are presented to illustrate and support the theoretical results.

An enrichment strategy based on hydrophobic tagging and reversed-phase chromatographic separation for the analysis of lysine-containing peptides

Lysine (K) is widely used in the design of lysine-targeted crosslinkers, structural elucidation of protein complexes, and analysis of protein-protein interactions. In "shotgun" proteomics, which is based on liquid chromatography-tandem mass spectrometry (LC-MS/MS), proteins from complex samples are enzymatically digested, generating thousands of peptides and presenting significant challenges for the direct analysis of K-containing peptides. In view of the lack of effective methods for the enrichment of K-containing peptides, this work developed a method which based on a hydrophobic-tag-labeling reagent C10-S-S-NHS and reversed-phase chromatography (termed as HYTARP) to achieve the efficient enrichment and identification of K-containing peptides from complex samples. The C10-S-S-NHS synthesized in this work successfully labeled standard peptides containing various numbers of K and the labeling efficiency achieved up to 96% for HeLa cell protein tryptic digests. By investigating the retention behavior of these labeled peptides in C18 RP column, we found that most K-labeled peptides were eluted once when acetonitrile percentage reached 57.6% (v/v). Further optimization of the elution gradient enabled the efficient separation and enrichment of the K-labeled peptides in HeLa digests via a stepwise elution gradient. The K-labeled peptides accounted for 90% in the enriched peptides, representing an improvement of 35% compared with the number of peptides without the enrichment. The dynamic range of proteins quantified from the enriched K-containing peptides spans 5-6 orders of magnitude, and realized the detection of low-abundance proteins in the complex sample. In summary, the HYTARP strategy offers a straightforward and effective approach for reducing sample complexity and improving the identification coverage of K-containing peptides and low-abundance proteins.

Rational design of a setaria-like NiTe2/MoS2 semi-coherent heterogeneous interface for enhancing diffusion kinetics in potassium-ion batteries

Constructing unique heterostructures is a highly effective approach for enhancing the K+ storage capability of transition metal selenides. Such structures generate internal electric fields that significantly reduce the charge transfer activation energy. However, achieving a flawless interfacial region that maintains the optimal energy level gradient and degree of lattice matching remains a considerable challenge. In this study, we synthesised Setaria-like NiTe2/MoS2@C heterogeneous interfaces at which three-dimensional MoS2 nanosheets are evenly embedded in NiTe2 nanorods to form stabilised heterojunctions. The NiTe2/MoS2 heterojunctions display distinctive electronic configurations and several active sites owing to their low lattice misfits (δ = 13 %), strong electric fields, and uniform carbon shells. A NiTe2/MoS2@C anode in a potassium-ion battery (KIB) exhibited an impressive reversible capacity of 125.8 mAh/g after 1000 cycles at a rate of 500 mA g−1 and a stable reversible capacity of 111.7 mAh/g even after 3000 cycles at 1000 mA g−1. Even the NiTe2/MoS2@C//perylene tetracarboxylic dianhydride full battery configuration maintained a significant reversible capacity of 92.4 mAh/g after 100 cycles at 200 mA g−1, highlighting its considerable potential for application in KIBs. Calculations further revealed that the well-designed NiTe2/MoS2 heterojunction significantly promotes K+ ion diffusion.

Ambient noise imaging for municipal solid waste landfill structure detection based on the common-midpoint two-station analysis with distributed acoustic sensing

SUMMARY Distributed acoustic sensing (DAS) enables high-density sampling of seismic wavefields at low cost compared to conventional geophones. This capability facilitates structural detection of a municipal solid waste (MSW) landfill, which is important for protecting the surrounding ecosystem. However, processing the vast amount of data from DAS array for ambient noise imaging can be computationally intensive. To address this, we employed the common-midpoint two-station (CMP-TS) analysis to enhance the efficiency of ambient noise imaging in the MSW landfill. CMP-TS analysis involves selecting pairs of traces at equal distances on both sides with the subarray midpoint as symmetry, which reduces the number of DAS array recordings for cross-correlation calculations. After positioning the DAS arrays linearly on top of the MSW landfill to automatically collect ambient noise, we used the CMP-TS analysis in the cross-correlation calculations to speed up the measurement of dispersion. The S-wave velocity structure of the study region was obtained quickly by inverting the extracted dispersion curves using the gradient optimization method. Ambient noise imaging based on CMP-TS analysis with DAS was applied to a test of an area-type MSW landfill. The resulting S-wave velocity section revealed a discontinuous low-velocity zone, validated by the high-density resistivity method. This low-velocity zone was interpreted as containing leachate from waste decomposition, and its discontinuity may be caused by excessive differences in the waste residues settling rates under compaction. Employing CMP-TS analysis in ambient noise data collected by DAS offers more cost-effective monitoring and a reliable basis for environmental pollution prevention and control.