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At least 811 records · Page 45

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗

Sizing ramping reserve using probabilistic solar forecasts: A data-driven method

Ramping products have been introduced or proposed in several U.S. power markets to mitigate the impact of load and renewable uncertainties on market efficiency and reliability. Current methods often rely on historical data to estimate the requirements of ramping products and fail to take into account the effects of the latest weather conditions and their uncertainties, which could lead to overly conservative or insufficient requirements. This study proposes a k-nearest-neighbor-based method to give weather-informed estimates of ramping needs based on short-term probabilistic solar irradiance forecasts. Forecasts from multiple sites are employed in conjunction with principal component analysis to derive numerical classifiers to characterize system-level weather conditions. In addition, we develop a data-driven method to optimize the model parameters in a rolling-forward manner. By using real-world data from the California Independent System Operator, we design two metrics to evaluate method performance: 1) frequency of shortage and 2) oversupply of ramping product. Our proposed method presents advantages in comparison with the baseline and a set of benchmark methods: without compromising system reliability, it reduces system ramping requirements by up to 25%, therefore improving both system reliability and economics.

14 SOLAR ENERGY↗

Investigation on zinc substituted highly porous improved catalytic activity of NiFe2O4 nanocrystal by co-precipitation method

Highlights: • A thriftily viable co-precipitation technique has been used to synthesize Zn substituted NiFe{sub 2}O{sub 4} nanoparticles. • An essential structural, optical and magnetic property has been investigated. • Augmentation of Photo-Fenton activity after increasing the zinc concentration has been observed and deliberated. • Strong dependence of photo-fenton activity and cumulative increase degradation efficiency. - Abstract: Connecting to various applications of nickel ferrite as a ceramic magnetic nanocomposite in diverse manufacturing and technical categories, it is essential to optimize its optical, magnetic and microstructural characteristics. Hence a facile route was developed to fabricate nickel zinc ferrite (Ni{sub 1-x}Zn{sub x}Fe{sub 2}O{sub 4}) nanocomposites by co-precipitation route. The enviable spinel phase was studied by X-ray diffractometer and the crystallite size was found to vary from 8 to 15 nm. By Transmission Electron Microscopy, the micro structural and speck distribution was found in the range of 5–12.5 nm. Surface characterization was done using the isothermal BET technique which presents a high surface area that is exhibited by synthesized nanoparticles. Using Kubelka-Munk plot, bandgap was found to be decreasing with the increase of Zn concentration.The synthesized Ni{sub 1-x}Zn{sub x}Fe{sub 2}O{sub 4} was studied for its application in photo-Fenton degradation of organic Methylene blue (MB) dye. The synthesized nanocatalyst can bereused further which makes itself a proficient candidate in industrial effluent degradation.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Can classical DEM simultaneously capture compressibility and flowability of milled biomass?

Accurate prediction of the rheological behavior of biomass is essential for the design and operation of hoppers, feeders, and storage systems in biorefineries. This study examines whether the classical, coarse-grained discrete element method (DEM) formulation can simultaneously reproduce the compressibility and flowability of milled herbaceous biomass, using Miscanthus × giganteus as a representative material. The model represents particles as rigid spheres interacting through Hertz-Mindlin elastic-frictional contacts augmented with an area-dependent cohesion term. Laboratory cyclic compression and wedge-shaped hopper discharge experiments were used as calibration benchmarks. Although the model can independently reproduce each behavior by appropriately tuning particle Young's modulus E and cohesion energy density k, an extensive parametric investigation comprising more than 600 simulations reveals that the optimal parameter regions for compression and hopper flow are distinct and non-overlapping in (E, k) space. Surrogate surface analysis further shows that the corresponding objective-function valleys exhibit similar trends but are approximately parallel and spatially offset, precluding a unified calibration within the explored domain. Sensitivity analysis indicates that compressibility is governed predominantly by stiffness and cohesion, whereas the slope of the mass flow rate-opening relation in hopper discharge is primarily controlled by tangential friction. Extensions incorporating particle size distribution and clumped-sphere representations do not eliminate the incompatibility. These results systematically reveal, for the first time, the structural limitation of simplified DEM formulations in representing biomass rheological behavior, underscoring the necessity for models incorporating additional physical mechanisms, such as particle deformability or enhanced interlocking, to achieve unified predictive capability for biomass handling behavior.

09 BIOMASS FUELS↗

Adaptive mesh refinement in stress-constrained topology optimization

We present a topology structural optimization framework with adaptive mesh refinement and stress-constraints. Finite element approximation and geometry representation benefit from such refinement by enabling more accurate stress field predictions and greater resolution of the optimal structural boundaries. We combine a volume fraction filter to impose a minimum design feature size, the RAMP penalization to generate “black-and-white designs” and a RAMP-like stress definition to resolve the “stress singularity problem.” Regions with stress concentrations dominate the optimized design. As such, rigorous simulations are required to accurately approximate the stress field. To achieve this goal, we invoke a threshold operation and mesh refinement during the optimization. We do so in an optimal fashion, by applying adaptive mesh refinement techniques that use error indicators to refine and coarsen the mesh as needed. In this way, we obtain more accurate simulations and greater resolution of the design domain. We present results in two dimensions to demonstrate the efficiency of our method.

97 MATHEMATICS AND COMPUTING↗

Comprehensive defect evaluation of advanced nuclear fuels using high-resolution acoustic signals and optimized sensor separation

Graphite pebble composite structures based on TRistructural-ISOtropic (TRISO) particles are being developed as core nuclear fuels in advanced power reactors, promising safe operation at increased temperatures. Ensuring the structural integrity of these nuclear fuels requires comprehensive and accurate non-destructive evaluation (NDE) techniques to characterize defects and damage in the pebbles. However, traditional acoustic evaluation methods face limitations in defect characterization due to the highly attenuative, and geometrically and compositionally complex nature of these structures. This study proposes an improved acoustic NDE technique for accurate detection and classification of anticipated relevant defects and damage in graphite pebbles using high-resolution acoustic signals and optimized transmit-receive sensor networks. The proposed approach utilizes a triangular three-sensor network as the base unit, comprising three transmit-receive sensors. The sensor separation distance, as well as acoustic excitation center frequency, pulse-width, and bandwidth are optimized to enhance spatial resolution and improve signal-to-noise ratio, enabling effective characterization of the smallest size and widest range of defects in pebbles. Furthermore, the use of the triangular sensor configuration instead of a more conventional transmit-receive sensor pair expands the inspection region from a one-dimensional linear path to a two-dimensional area, increasing spatial coverage. To mitigate challenges associated with processing of complex acoustic signals arising from high-frequency, high-bandwidth excitation in these structures, a machine-learning-based signal processing algorithm is integrated with the sensor network. In the machine-learning-based algorithm, multi-domain features are extracted from the acoustic signals to capture intricate signal characteristics, significantly improving defect identification and classification compared to traditional approaches. The proposed acoustic NDE technique offers considerable promise for practical and reliable defect/damage diagnostics of advanced nuclear pebble fuels.

42 ENGINEERING↗

Ultrathin Pt x Sn 1–x Nanowires for Methanol and Ethanol Oxidation Reactions: Tuning Performance by Varying Chemical Composition

Pt-based alloys denote promising catalysts for the methanol oxidation reaction (MOR) and the ethanol oxidation reaction (EOR), due to their enhanced activity toward alcohol-oxidation reactions and reduced cost as compared with Pt alone. Among all of these binary systems, PtSn has been reported to exhibit superior methanol/ethanol oxidation activity. In this paper, we deliberatively tailor chemical composition, reduce size, and optimize morphology of the catalyst in an effort to understand structure–property correlations that can be used to improve upon the electrocatalytic activity of these systems. Previous work performed by our group suggested that Pt-based catalysts, possessing an ultrathin one-dimensional (1D) structure, dramatically promote both cathodic and anodic reactions with respect to their zero-dimensional (0D) counterparts. Herein, a novel set of ultrathin binary Pt–Sn 1D nanowire (NW) catalysts with rationally controlled chemical compositions, i.e., Pt 9 Sn 1 , Pt 8 Sn 2 , and Pt 7 Sn 3 , has been synthesized using a facile, room-temperature, wet-solution-based method. The crystallinity and chemical composition of these as-prepared samples were initially characterized using XRD, XPS, and EDX. Results revealed that this synthetic protocol could successfully generate PtSn alloys with purposely tunable chemical compositions. TEM and HRTEM verified the structural integrity of our ultrathin 1D NW morphology for our Pt 9 Sn 1 , Pt 8 Sn 2 , and Pt 7 Sn 3 samples. The effects of varying Sn content within these alloy samples toward the electro-oxidation reaction of methanol and ethanol were probed using cyclic voltammetry (CV) in acidic media. Finally, within this series, we find that the optimized chemical composition for both the MOR and the EOR is Pt 7 Sn 3 .

36 MATERIALS SCIENCE↗

Grain boundary engineering for efficient and durable electrocatalysis

Grain boundaries in noble metal catalysts have been identified as critical sites for enhancing catalytic activity in electrochemical reactions such as the oxygen reduction reaction. However, conventional methods to modify grain boundary density often alter particle size, shape, and morphology, obscuring the specific role of grain boundaries in catalytic performance. This study addresses these challenges by employing gold nanoparticle assemblies to control grain boundary density through the manipulation of nanoparticle collision frequency during synthesis. We demonstrate a direct correlation between increased grain boundary density and enhanced two-electron oxygen reduction reaction activity, achieving a significant improvement in both specific and mass activity. Additionally, the gold nanoparticle assemblies with high grain boundary density exhibit remarkable electrochemical stability, attributed to boron segregation at the grain boundaries, which prevents structural degradation. This work provides a promising strategy for optimizing the activity, selectivity, and stability of noble metal catalysts through precise grain boundary engineering.

36 MATERIALS SCIENCE↗

Accelerating flux balance calculations in genome-scale metabolic models by localizing the application of loopless constraints

Abstract Background Genome-scale metabolic network models and constraint-based modeling techniques have become important tools for analyzing cellular metabolism. Thermodynamically infeasible cycles (TICs) causing unbounded metabolic flux ranges are often encountered. TICs satisfy the mass balance and directionality constraints but violate the second law of thermodynamics. Current practices involve implementing additional constraints to ensure not only optimal but also loopless flux distributions. However, the mixed integer linear programming problems required to solve become computationally intractable for genome-scale metabolic models. Results We aimed to identify the fewest needed constraints sufficient for optimality under the loopless requirement. We found that loopless constraints are required only for the reactions that share elementary flux modes representing TICs with reactions that are part of the objective function. We put forth the concept of localized loopless constraints (LLCs) to enforce this minimal required set of loopless constraints. By combining with a novel procedure for minimal null-space calculation, the computational time for loopless flux variability analysis (ll-FVA) is reduced by a factor of 10–150 compared to the original loopless constraints and by 4–20 times compared to the current fastest method Fast-SNP with the percent improvement increasing with model size. Importantly, LLCs offer a scalable strategy for loopless flux calculations for multi-compartment/multi-organism models of large sizes, for example, shortening the CPU time for ll-FVA from 35 h to less than 2 h for a model with more than104 reactions. Availability and implementation Matlab functions are available in the Supplementary Material or at https://github.com/maranasgroup/lll-FVA Supplementary information Supplementary data are available at Bioinformatics online.

Chan, Siu H. J. (ORCID:000000027707656X)↗

Validation of RELAP-7 for Forced Convective and Natural Circulation Reactor Flows

New reactor designs such as small modular reactors and newly proposed passive safety systems rely on natural circulation two-phase flow cooling of the reactor core under accident conditions. Without a pump to drive the flow, the circulation rate is a strong function of void fraction. Under these conditions where the flow rate, pressure drop, and heat transfer are all closely coupled through the mixture density, the accurate prediction of void fraction is pivotal. Furthermore, while these passive systems boast a more reliable cooling option, the sensitivity of the system to two-phase flow instabilities is more likely than in forced convective cooling and can result in dangerous density wave or flow excursion conditions. Improvement of one-dimensional system codes and further development of three-dimensional code capability are all important efforts in addressing these issues. The proposed work is a critical step towards providing confidence through validation of the capability of thermal-hydraulic tools to meet the emerging challenges in reactor simulation. Historically, system analysis codes develop a stiffness over time due to fine-tuning of adjustable code parameters that result in a reluctance or inability to adopt improved models. Therefore, a rigorous effort should be invested in preparation for RELAP-7 to identify required modeling improvements of constitutive relations and validation of the code applicability to the wide range of possible reactor flow conditions. Part of this validation involves the uncertainty quantification which is a critical step in the Code Scaling, Applicability, and Uncertainty (CSAU) methodology and Best-Estimate Plus Uncertainty (BEPU) licensing method. BEPU allows recovery of safety margins and potentially optimizing plant performance, resulting in economic benefit for the reactor operators. Beginning with the collection of past experimental data covering prototypic LWR conditions, this document reviews three critical datasets available for the validation of system codes. The experimental methods, procedures, facilities, instrumentation, and supplementary information are consolidated to aid the validation efforts of this work and future projects. These three datasets (824 conditions) are chosen due to their wide acceptance and demonstrated application for code validation. Two experimental datasets from the Nuclear Power Engineering Corporation (NUPEC) span prototypic conditions of Boiling Water Reactors (BWR) and Pressurized Water Reactors (PWR). These datasets are labeled as the BFBT (BWR Full-size Fine-mesh Bundle Test) and the PSBT (PWR Subchannel and Bundle Test) datasets respectively. The third dataset is taken in an internally heated annulus that is scaled based on a BWR subassembly and includes two-phase flows important to post-blowdown analysis. Together, the datasets consider the full range of pressure and mass flux expected in LWR forced convective flows from steady-state to long term cooling conditions. Recognizing the deficiency of available data under low-pressure, low-flow, a new natural circulation dataset consisting of 107 conditions is obtained. With system pressures ranging between 145 and 950 kPa, natural circulation is driven by a heat flux up to 275 kW/m2 in a 3 m long heated region of the annulus test section which is followed by a 2 m long unheated region before entering a condenser. Stable natural circulation flow rates in this facility were measured to be between 182 and 590 kg/m2-s. Pressure and temperature are measured at ten locations in the facility. In addition, measurements of void fraction, interfacial area concentration, and gas velocity are obtained at five axial locations in the annulus test section. The RELAP5-3D code demonstrates good accuracy for void fraction predictions in BFBT and PSBT benchmarks, but shows relatively large discrepancy in the annulus experiments at lower pressures. In two-phase natural circulation, large oscillations are observed in many system parameters during the simulation of 19 experiment cases which were conducted at low pressures, and the model shows good predictive performance with the remaining 88 experiment cases. However, some discrepancies in void fraction and temperature predictions are found which may require improved constitutive modeling under these low-pressure, low-flow condition. The quantified uncertainty in void fraction predictions in three forced convection benchmarks due to uncertainties in boundary conditions and geometries are relatively small, except for a few cases in the annulus experiment. The output uncertainty of the natural circulation model is quantified by considering the measurement error in boundary conditions, and it is shown that the output uncertainty ranges are relatively small for the range of conditions considered. Through this work, a clear pathway is provided for validation of RELAP7 once the code is available.

42 ENGINEERING↗

Coupled Gel/Seeded Growth of Cs2AgBiBr6 Single Crystals for Radiation Detection

Abstract— The all-inorganic lead free Cs2AgBiBr6 perovskite represents a highly promising environment-friendly radiation detector material. The first attempt to grow Cs2AgBiBr6 single crystals using the unique gel growth method is reported in this study. This technique enables controlled reagent diffusion in a gel medium at room temperature, fostering the growth of visible single crystals. It offers advantages over conventional methods, leading to fewer equilibrium and non-equilibrium defects and simplified execution. Interestingly, the as-grown crystals exhibited variations in size and colors within the gel matrix, indicating the influence of Bi solutes in Bi-poor and Bi-rich Cs2AgBiBr6 crystals. Once the crystals were confirmed by powder xray diffraction, a series of comprehensive material characterizations was performed. The resistivity of Cs2AgBiBr6 single crystals was measured at 1.12x107 Ω•cm. Trap density analysis yielded a value of 2.82 x1010 cm-3. The charge carrier mobility was determined to be 32.9 cm2V-1S-1, suggesting the suitability for radiation detector applications. Additional growth optimization is going on and further characterization will be reported during the presentation. Our work helps accelerate the development of lead-free perovskite materials toward radiation detector applications.

James, Ralph B.↗

Effect of Particle Spreading Dynamics on Powder Bed Quality in Metal Additive Manufacturing

Powder spreading precedes creation of every new layer in powder bed additive manufacturing (AM). The powder spreading process can lead to powder layer defects such as porosity, poor surface roughness and particle segregation. Therefore, the creation of homogeneous layers is the first task for optimal part printing. Discrete element methods (DEM) powder spreading simulations are typically limited to a single layer and/or small number of particles. Therefore, results from such model configurations may not be generalized to multiple layer processes. In this study, a computationally efficient multi-layer powder spreading DEM simulation model is proposed. The model is calibrated experimentally using static Angle of Repose measurements. The adhesion model parameter, cohesive energy density is related to adhesive surface energy and strain energy release rate parameters. The model results show that interaction between particle and the powder spreading rake leads to noticeable variation in packing density, surface roughness, dynamic angle of repose (AOR), particle size distribution, and particle segregation. Finally, the powder model is experimentally validated using a recoater spreading rig to measure the dynamic AOR at spreading speeds consistent with recoating speeds and layer heights used in AM processes.

36 MATERIALS SCIENCE↗

Empowering Intensity Modulated Proton Therapy Through Physics and Technology: An Overview

Considering the clinical potential of protons attributable to their physical characteristics, interest in proton therapy has increased greatly in this century, as has the number of proton therapy installations. Until recently, passively scattered proton therapy was used almost entirely. Notably, the overall clinical results to date have not shown a convincing benefit of protons over photons. A rapid transition is now occurring with the implementation of the most advanced form of proton therapy, intensity modulated proton therapy (IMPT). IMPT is superior to passively scattered proton therapy and intensity modulated radiation therapy (IMRT) dosimetrically. However, numerous limitations exist in the present IMPT methods. In particular, compared with IMRT, IMPT is highly vulnerable to various uncertainties. In this overview we identify three major areas of current limitations of IMPT: treatment planning, treatment delivery, and motion management, and discuss current and future efforts for improvement. For treatment planning, we need to reduce uncertainties in proton range and in computed dose distributions, improve robust planning and optimization, enhance adaptive treatment planning and delivery, and consider how to exploit the variability in the relative biological effectiveness of protons for clinical benefit. The quality of proton therapy also depends on the characteristics of the IMPT delivery systems and image guidance. Efforts are needed to optimize the beamlet spot size for both improved dose conformality and faster delivery. For the latter, faster energy switching time and increased dose rate are also needed. Real-time in-room volumetric imaging for guiding IMPT is in its early stages with cone beam computed tomography (CT) and CT-on-rails, and continued improvements are anticipated. In addition, imaging of the proton beams themselves, using, for instance, prompt γ emissions, is being developed to determine the proton range and to reduce range uncertainty. With the realization of the advances described above, we posit that IMPT, thus empowered, will lead to substantially improved clinical results.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Localization microscopy of DNA in situ using Vybrant{sup ®} DyeCycle™ Violet fluorescent probe: A new approach to study nuclear nanostructure at single molecule resolution

Higher order chromatin structure is not only required to compact and spatially arrange long chromatids within a nucleus, but have also important functional roles, including control of gene expression and DNA processing. However, studies of chromatin nanostructures cannot be performed using conventional widefield and confocal microscopy because of the limited optical resolution. Various methods of superresolution microscopy have been described to overcome this difficulty, like structured illumination and single molecule localization microscopy. We report here that the standard DNA dye Vybrant{sup ®} DyeCycle™ Violet can be used to provide single molecule localization microscopy (SMLM) images of DNA in nuclei of fixed mammalian cells. This SMLM method enabled optical isolation and localization of large numbers of DNA-bound molecules, usually in excess of 10{sup 6} signals in one cell nucleus. The technique yielded high-quality images of nuclear DNA density, revealing subdiffraction chromatin structures of the size in the order of 100 nm; the interchromatin compartment was visualized at unprecedented optical resolution. The approach offers several advantages over previously described high resolution DNA imaging methods, including high specificity, an ability to record images using a single wavelength excitation, and a higher density of single molecule signals than reported in previous SMLM studies. The method is compatible with DNA/multicolor SMLM imaging which employs simple staining methods suited also for conventional optical microscopy. - Highlights: • Super-resolution imaging of nuclear DNA with Vybrant Violet and blue excitation. • 90nm resolution images of DNA structures in optically thick eukaryotic nuclei. • Enhanced resolution confirms the existence of DNA-free regions inside the nucleus. • Optimized imaging conditions enable multicolor super-resolution imaging.

60 APPLIED LIFE SCIENCES↗

Dynamic Model Reduction for Large-Scale Power Systems Using Wide-Area Measurements

To perform faster than the real-time dynamic simulation of large-scale power systems, it is necessary to reduce the simulated system size by using equivalents for surrounding areas of the study area, and existing dynamic model reduction approach could provide the needed structure of the reduced area. However, further parameter optimization is required to achieve the desired accuracy. In this paper, a particle swarm optimization (PSO) based approach is used to solve the above problem. Parameters for the individual dynamic elements in the reduced system are calibrated repeatedly until the wide-area measurements of the reduced model and the original model are very similar to each other with satisfactory accuracy. Results indicate that after optimization, the dynamic response of the reduced model matches better with that of the original one than using existing methods. Under both the generator-trip event and the bus-fault event, the reduced model has a higher frequency match and less power mismatch.

42 ENGINEERING↗

Adaptive Visual Sort and Summary of Micrographic Images of Nanoparticles for Forensic Analysis

Forensic analysis of nanoparticles is often conducted through the collection and identifi- cation of electron microscopy images to determine the origin of suspected nuclear material. Each image is carefully studied by experts for classification of materials based on texture, shape, and size. Manually inspecting large image datasets takes enormous amounts of time. However, automatic classification of large image datasets is a challenging problem due to the complexity involved in choosing image features, the lack of training data available for effective machine learning methods, and the availability of user interfaces to parse through images. Therefore, a significant need exists for automated and semi-automated methods to help analysts perform accurate image classification in large image datasets. We present INStINCt, our Intelligent Signature Canvas, as a framework for quickly organizing image data in a web based canvas framework. Images are partitioned using small sets of example images, chosen by users, and presented in an optimal layout based on features derived from convolutional neural networks.

active canvas↗

Multiparameter Methods are the New Field in Statistics

During the last decade statistical problems of a new kind have appeared more and more often, in which the dimensionality of observations is large, and the sample size is small or comparable with the dimensionality of observations. These problems may be characterized as multiparameter problems. The theory of multiparameter analysis, proposed by A.N.Kolmogorov and supported by the studies on the spectral theory of random matrices of V. A.Marchenko, L. A.Pastur, V. L.Girko, and others, revealed some specific phenomena appearing in statistics with a large number of weakly dependent variables. Particularly, there are stable relations between the principal parts of parameter set functions and the set of observed variables, which may be used to improve the statistical analysis. The use of these phenomena allows one to develop a thorough, always stable, and approximately optimal (irrespective of samples) versions of mostly used statistical procedures. The multiparameter versions of the discriminant analysis are of special interest.

97 MATHEMATICS AND COMPUTING↗

Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum

This paper applies an idea of adaptive momentum for the nonlinear conjugate gradient to accelerate optimization problems in sparse recovery. Specifically, we consider two types of minimization problems: a (single) differentiable function and the sum of a non-smooth function and a differentiable function. In the first case, we adopt a fixed step size to avoid the traditional line search and establish the convergence analysis of the proposed algorithm for a quadratic problem. This acceleration is further incorporated with an operator splitting technique to deal with the non-smooth function in the second case. As a result, we use the convex ι 1 and the nonconvex ι 1 – ι 2 functionals as two case studies to demonstrate the efficiency of the proposed approaches over traditional methods.

97 MATHEMATICS AND COMPUTING↗