quasi-Newton Method Research Articles

Memoryless quasi-Newton-type methods via some weak secant relations for large-scale unconstrained optimization

This article presents two variants of memoryless quasi-Newton methods with backtracking line search for large-scale unconstrained minimization. These updating methods are derived by means of a least-change updating strategy subjected to some weaker form of secant relation obtained by projecting the secant equation onto the search direction. In such a setting, the search direction can be computed without the need of calculation and storage of matrices. We establish the convergence properties for these methods, and their performance is tested on a large set of test functions by comparing with standard methods of similar computational cost and storage requirement. Our numerical results indicate that significant improvement has been achieved with respect to iteration counts and number of function evaluations.

A Proximal Stochastic Quasi-Newton Algorithm with Dynamical Sampling and Stochastic Line Search

A Proximal Stochastic Quasi-Newton Algorithm with Dynamical Sampling and Stochastic Line Search

Adapting the population size in CMA-ES using nearest-better clustering method for multimodal optimization

The covariance matrix adaptation evolution strategy (CMA-ES) is one of the advanced algorithms for solving continuous single-objective optimization. In this algorithm, the population size is an important parameter which represents the number of candidate points generated in each iteration. A larger population size allows for better exploration of the search space and is typically required for handling multimodal functions, while a smaller population size facilitates quicker convergence. Adapting the population size in this algorithm raises a question about how to increase/decrease/keep it in an efficient way to solve specific problems. In this paper, we propose a novel approach for this purpose based on the information of niches observed during the evolution process. Firstly, the nearest-better clustering (NBC) technique is employed to detect the number of niches in each iteration, then the information will be used to adapt the population size accordingly. Additionally, a quasi-Newton method, which serves as a local search, can be incorporated into the algorithm in order to speed it up. We evaluate the effectiveness of our method through numerical simulations on multimodal test functions, the black-box optimization benchmarking (BBOB) noiseless testbed, and the CEC 2014 and CEC 2017 benchmark testing suites.

Variability in cardiometabolic parameters and all-cause and cause-specific mortality in older adults: evidence from 2 prospective cohorts.

To assess the individual and joint associations of variability in multiple cardiometabolic parameters with mortality risk across older populations. A total of 51,551 Chinese elderly participants (aged ≥60 years) with ≥3 measurements of systolic blood pressure (SBP), visceral adiposity index (VAI), fasting blood glucose (FBG) and low-density lipoprotein cholesterol (LDL-C) during 2018-2022 were included. Variability metrics included standard deviation, coefficient of variation, average real variability and variability independent of the mean (VIM, used in primary analysis). Participants were classified based on the number of high-variability (highest quartile of variability) parameters into four categories: with 0, 1, 2 and 3-4 high-variability cardiometabolic parameters. Cox regression analyses were performed in 2024. Findings were then externally validated using the Health and Retirement Study (HRS, wave 8-15). Higher SBP, VAI, FPG and LDL-C variability were associated with greater all-, cardiovascular- and other-cause mortality risk. Compared with subjects with no high-variability parameters measured as the VIM, the hazard ratios (95% confidence interval) of all-cause mortality were 1.30 (1.16-1.44) for one parameter, 1.86 (1.66-2.09) for two parameters and 2.02 (1.75-2.32) for three/four parameters. Consistent results were noted for cardiovascular-, cancer- and other-cause mortality, using other variability indices and in various sensitivity and subgroup analyses. These associations were validated in the HRS (n=1,991). Increased variability in cardiometabolic parameters is associated with elevated risks of all-cause and cause-specific mortality amongst older adults in China. Reducing variability of these parameters could serve as a target to increase life expectancy in older populations.

Grey-Box Energy Modelling of Energy-Efficient House Using Hybrid Optimization Technique of Genetic Algorithms (GA) and Quasi-Newton Algorithms with Markov Chain Monte Carlo Uncertainty Distribution

Understanding energy demands and costs is important for policy makers and the energy sector, especially in the context of residential heating and cooling systems. To estimate the thermal demand of a residential house, a grey-box modelling method with a resistance–capacitance (RC) analogy was implemented. The architectural properties used to parameterize the grey-box model were derived from a house used for research purposes in Vaughan, Ontario, Canada (TRCA-House A). The house model accounts for solar irradiance on exterior building surfaces, thermal conductivity through all surfaces, solar heat gains through windows, and thermal gains from ventilation. Two parallel short- and long-term calibrations were performed such that model outputs reflected the real-world operation of the house as best as possible. To define the unknown model parameters (such as the conductivity of building materials and some constant parameters), a hybrid optimization scheme including a genetic algorithm (GA) and the Quasi-Newton algorithm was introduced and implemented using Bayesian approximation and Markov Chain Monte Carlo (MCMC) methods. The temperature outputs from the model were compared to the data retrieved from TRCA-House A. The final iteration of the model had an RMSE for interior zone temperature estimation of 0.22 °C when compared to the retrieved interior zone temperature data from TRCA-House A. Furthermore, the annual heating and cooling energy consumption values are within 1.50% and 0.08% of target values, respectively. According to these preliminary results, the introduced model and optimization techniques could be adjusted for different types of housing, as well as for smart control applications on both a short- and long-term basis.

MiNNIE: a Mixed Multigrid Method for Real-time Simulation of Nonlinear Near-Incompressible Elastics

We propose MiNNIE, a simple yet comprehensive framework for real-time simulation of nonlinear near-incompressible elastics. To avoid the common volumetric locking issues at high Poisson's ratios of linear finite element methods (FEM), we build MiNNIE upon a mixed FEM framework and further incorporate a pressure stabilization term to ensure excellent convergence of multigrid solvers. Our pressure stabilization strategy injects bounded influence on nodal displacement which can be eliminated using a quasiNewton method. MiNNIE has a specially tailored GPU multigrid solver including a modified skinning-space interpolation scheme, a novel vertex Vanka smoother, and an efficient dense solver using Schur complement. MiNNIE supports various elastic material models and simulates them in real-time, supporting a full range of Poisson's ratios up to 0.5 while handling large deformations, element inversions, and self-collisions at the same time.

Effects of Memory on Inventory Control and Pricing Policy for Imperfect Production with Rework Process

Fractional calculus is a pertinent way to study the memory effect in an inventory model for investigating its dynamical behavior. Since inventory management is a memory-dependent process, fractional calculus approach may be employed to discover some fruitful insights and can help to gain more profit. In realistic scenarios, the manufacturing process cannot be perfect, and it delivers some faulty units due to many inevitable reasons. In literature, an imperfect production inventory problem under the memory effect has not been studied. Our study aims to investigate the memory effect on a production inventory system. In this article, a fractional order inventory control problem is formulated by considering an imperfect manufacturing process and price-sensitive demand. The faulty units are repaired through a rework process. Caputo fractional derivatives and integrals are used to consider the memory effect. Due to the nonlinear cost elements in the formulated problem, optimal pricing and production policies are investigated by using a quasi-Newton optimization algorithm and particle swarm optimization approach. The managerial implications of the proposed study are discussed with the help of numerical illustrations. The numerical outcomes suggest that consideration of memory in the inventory system boosts the profitability of the firm.

Intrinsic Functional Partially Linear Poisson Regression Model for Count Data

Poisson regression is a statistical method specifically designed for analyzing count data. Considering the case where the functional and vector-valued covariates exhibit a linear relationship with the log-transformed Poisson mean, while the covariates in complex domains act as nonlinear random effects, an intrinsic functional partially linear Poisson regression model is proposed. This model flexibly integrates predictors from different spaces, including functional covariates, vector-valued covariates, and other non-Euclidean covariates taking values in complex domains. A truncation scheme is applied to approximate the functional covariates, and the random effects related to non-Euclidean covariates are modeled based on the reproducing kernel method. A quasi-Newton iterative algorithm is employed to optimize the parameters of the proposed model. Furthermore, to capture the intrinsic geometric structure of the covariates in complex domains, the heat kernel is employed as the kernel function, estimated via Brownian motion simulations. Both simulation studies and real data analysis demonstrate that the proposed method offers significant advantages over the classical Poisson regression model.

Acute exercise impacts heart rate variability but not cognitive flexibility during subsequent simulated firefighter occupational tasks.

Acute exercise can transiently enhance cognitive flexibility. The cognitive demand of firefighters makes it relevant to understand if on-shift exercise could produce similar improvements in cognitive performance during subsequent occupational tasks. Metrics of heart rate variability (HRV), such as time- and frequency-domain outcomes, may shed light upon the influence exercise has on cognition, as they discern information related to cardiac autonomic (sympathetic/parasympathetic) function. We aimed to determine if acute resistance and aerobic exercise impact cognitive flexibility during occupational tasks and its relation to HRV. 32 participants completed a baseline Wisconsin Card Sorting Task (WCST) and three experimental trials: resistance exercise (RE), aerobic exercise (AE), or a rested control (CON). An occupational task assessment (OTA) including four rounds of 10 deadlifts and a 0.15-mile sandbag carry in an environmental chamber (35°C/50% humidity) was completed after each trial. The second round was followed by the WCST. Repeated measures ANOVAs were used to analyze differences by condition. For the WCST, total, perseverative, and non-perseverative errors did not differ (ps > 0.39). Time-domain HRV metrics were not different (ps > 0.05). All frequency-domain metrics, other than low-frequency power, were not different (ps > 0.24). Low-frequency power was lower based on condition (p = 0.03). Post hoc analysis showed low-frequency power was lower following AE compared to RE and CON. Results suggest an acute bout of on-shift aerobic or resistance exercise may not impact cognitive flexibility during subsequent simulated occupational tasks, despite depressed metrics of heart rate variability following aerobic exercise.

Inexact quasi-Newton methods under a relaxed metric regularity assumption

Inexact quasi-Newton methods under a relaxed metric regularity assumption

Do Metrics of Temporal Glycemic Variability Reveal Abnormal Glucose Rates of Change in Type 1 Diabetes?

We aimed to identify the normal range of glucose rates of change (RoC) observed in health and assess whether existing metrics of temporal glycemic variability (GV-timing), such as mean absolute glucose change (MAG) and continuous overlapping net glycemic action (CONGA), are predictive of abnormally rapid RoC in type 1 diabetes (T1D). We identified the normal range of RoC over one-hour intervals from continuous glucose monitoring (CGM) data of healthy individuals. Rapidly rising glucose was defined as RoC values above percentiles 99% (level 1, L1) or 99.9% (level 2, L2), and rapidly falling glucose as below 1% (L1) or 0.1% (L2). The percentage of time these thresholds are exceeded in a given individual is referred to as time in fluctuation (TIF). In a separate CGM dataset of 736 T1D individuals, we calculated TIF-L1 and TIF-L2, and compared them against corresponding values of MAG and CONGA. The extremum percentiles of RoC observed in health are 0.1%: -80 mg/dL/h, 1%: -50 mg/dL, 99%: +56 mg/dL/h, and 99.9%: +89 mg/dL/h. The T1D individuals spend significantly more TIF at rates exceeding these thresholds (TIF-L1: median, 16.7% [interquartile range, 12.7-21.5], TIF-L2: 5.0% [3.1-7.8]) than healthy individuals (TIF-L1: 1.4% [0.6-2.8], TIF-L2: 0.0% [0.0-0.2]). Both MAG and CONGA are highly correlated with TIF-L1 and TIF-L2 (r > .95 in each pairwise comparison). Individuals with T1D spend significant time with glucose RoC exceeding those observed in health. Existing GV-timing metrics are strongly correlated with time with abnormal RoC. Incorporation of a GV-timing metric in clinical practice is recommended.

Heart Rate Variability Metrics during Sleep in Children with Asperger’s Syndrome

Purpose: To describe the characteristics of Heart Rate Variability (HRV) metrics during sleep in children with Asperger Syndrome (AS) and children with Typical Development (TD). Methods: Children aged 6 to 10 years diagnosed with AS (n = 10) and TD (n = 10) were included in the study. Polysomnographic recordings were performed on two consecutive nights, the first night being the adaptation night and the second being used for the analysis of the characteristics of sleep and HRV metrics. Results: Inter-subject analysis showed that children with AS had a shorter heart period in all sleep stages, as well as higher REM sleep latency and fewer sleep cycles compared with children with TD. Likewise, measures related to parasympathetic activity were similar between both groups. Intra-subject analysis showed that children with AS had minimal differences between all sleep stages with most measures of the three domain methods of HRV analysis, while the children with TD presented an HRV according to the characteristics of each stage of sleep. Conclusion: Children with AS showed little autonomic flexibility when moving from one sleep stage to another, evaluated with the three-domain methods of HRV analysis. These results could indicate some degree of immaturity in sleep-related circuits in children with AS, which in turn affects HRV.

Combining Sentinel-2 Data and Risk Maps to Detect Trees Predisposed to and Attacked by European Spruce Bark Beetle

The European spruce bark beetle is a major disturbance agent in Norway spruce forests in Europe, and with a changing climate it is predicted that damage will increase. To prevent the bark beetle population buildup, and to limit further spread during outbreaks, it is crucial to detect attacked trees early. In this study, we utilize Sentinel-2 data in combination with a risk map, created from geodata and forestry data, to detect trees predisposed to and attacked by the European spruce bark beetle. Random forest models were trained over two tiles (90 × 90 km) in southern Sweden for all dates with a sufficient number of cloud-free Sentinel-2 pixels during the period May–September in 2017 and 2018. The pixels were classified into attacked and healthy to study how detection accuracy changed with time after bark beetle swarming and to find which Sentinel-2 bands are more important for detecting bark beetle attacked trees. Random forest models were trained with (1) single-date data, (2) temporal features (1-year difference), (3) single-date and temporal features combined, and (4) Sentinel-2 data and a risk map combined. We also included a spatial variability metric. The results show that detection accuracy was high already before the trees were attacked in May 2018, indicating that the Sentinel-2 data detect predisposed trees and that the early signs of attack are low for trees at high risk of being attacked. For single-date models, the accuracy ranged from 63 to 79% and 84 to 94% for the two tiles. For temporal features, accuracy ranged from 65 to 81% and 81 to 92%. When the single-date and temporal features were combined, the accuracy ranged from 70 to 84% and 90 to 96% for the two tiles, and with the risk map included, the accuracy ranged from 83 to 91% and 92 to 97%, showing that remote sensing data and geodata can be combined to increase detection accuracy. The differences in accuracy between the two tiles indicate that local differences can influence accuracy, suggesting that geographically weighted methods should be applied. For the single-date models, the SWIR, red-edge, and blue bands were generally more important, and the SWIR bands were more important after the attack, suggesting that they are most suitable for detecting the early signs of a bark beetle attack. For the temporal features, the SWIR and blue bands were more important, and for the variability metric, the green band was generally more important.

Galvanic distortion decomposition of magnetotelluric impedance tensors in 1-D electrical anisotropic media

SUMMARY The electromagnetic (EM) local distortion of the transfer function due to shallow small-scale inhomogeneities hinders the accurate interpretation of magnetotelluric (MT) data. Under the assumption that regional subsurface structures are electrically isotropic, decomposition techniques of the MT impedance tensor that focus on the galvanic distortion of the electric field have been well developed. In this paper, we present a decomposition method of MT impedance tensors over a regional 1-D conductivity anisotropic Earth, in which the galvanic distortion of both the electric and the associated magnetic fields are taken into account. An eigenparameter analysis is introduced to evaluate the intrinsic indeterminacy of magnetic distortion parameters. Regional anisotropic responses and EM distortion parameters are resolved by using a modified BFGS (Broyden–Fletcher–Goldfarb–Shanno) quasi-Newton algorithm combined with phase tensor analysis and the trust region method. In the presence of near-surface anisotropic inhomogeneities, synthetic 2-D data show an accentuated magnetic galvanic effect in the response tensors especially in the diagonal components probably due to the infinite extension in the strike direction. However, the magnetic galvanic distortion is not significant in synthetic 3-D models. The decomposition scheme is also applied to the analysis of the BC87 data set collected in southeastern British Columbia to investigate both the electric and magnetic field galvanic distortion.

Food insufficiency, SNAP status, and variability in weight and body composition: Longitudinal analysis of the National Health and Aging Trends Study Cohort 2012 - 2021

BackgroundThe impact of food insecurity and food assistance program on long-term body weight and composition is unclear. ObjectiveTo investigate the associations between baseline and duration of food insufficiency, the Supplemental Nutrition Assistance Program (SNAP) status, and variability in weight, BMI, and waist circumference (WC). MethodsData from 3,897 eligible Medicare beneficiaries aged ≥ 65 years recruited in the National Health and Aging Trends Study (2012-2021) were analyzed. At baseline, SNAP status was assessed. With repeated measurements over follow-up, baseline food insufficiency (FI) status, years with FI experience (nFI), three variability metrics for weight, BMI, and WC (i.e. standard deviation (SD), coefficient of variation (CV), root mean squared error (RMSE)) and four patterns (stable, loss, gain and cycling) were defined. Partial proportional-odds generalized ordered logit models and multinomial logistic regression models were fit to investigate the association between FI status, nFI, and SNAP status and quartiles of variability metrics and patterns, respectively. ResultsThe median [IQR] follow-up years was 8 [4, 9]. Per one-unit increase in the nFI, older adults had significantly higher variability in BMI (OR: 1.08 – 1.10), weight (OR = 1.14-1.15), and waist circumference (OR = 1.11 – 1.27) by SD, CV, and RMSE. SNAP participants did not differ from eligible non-participants in any variability metrics. Older adults with FI at baseline were 2.72 times (95%CI: 1.32, 5.58) more likely to gain weight. Relative risk of weight loss (RRR = 1.29, 95%CI: 1.06, 1.56), gain (RRR = 1.34, 95%CI: 1.09, 1.65), or cycling (RRR = 1.27, 95%CI: 1.04, 1.21) increased per one-unit increase in nFI. SNAP participants did not differ in BMI, weight, or WC patterns from eligible non-participants. ConclusionsRecurring food insecurity was associated with variability in older adults' weight and body composition. Additional interventions, beyond SNAP, are needed to prevent food insecurity and instability in body weight and composition.

A Stochastic Inertial Limited Memory BFGS Algorithm Based on the Mathematical Model of Rural Pipeline Network and its Application in Machine Learning

Abstract Stochastic algorithms are critical in addressing complex rural pipe networks and non-convex stochastic optimization problems. With the development of artificial intelligence, large-scale optimization problems that cannot be solved effectively by traditional optimization methods have emerged. Therefore, stochastic optimization algorithms are rapidly developing in the field of optimization. This paper introduces an inertial extrapolation stochastic BFGS (IESBFGS) algorithm, an innovative amalgamation of the inertial extrapolation technique and the finite memory quasi-Newton algorithm to solve nonconvex stochastic optimization problems. Firstly, the inertial extrapolation technique is employed to track the iteration point to the optimal x-value. Second, it is combined with a finite-memory proposed Newton algorithm thereby increasing the convergence speed. Then, the superiority of IESBFGS is verified by comparing the experimental performance with other better algorithms on machine learning SVM model and ERM model. Finally, it is shown that the algorithm offers good prospects for solving nonconvex problems.

Heart Rate Variability and Cerebral Autoregulation in Patients with Traumatic Brain Injury with Paroxysmal Sympathetic Hyperactivity Syndrome.

Severe traumatic brain injury (TBI) can lead to transient changes in autonomic nervous system (ANS) functioning and development of paroxysmal sympathetic hyperactivity (PSH) syndrome. Clinical manifestation of ANS disorders may be obscured by therapeutic interventions in TBI. This study aims to analyze ANS metrics and cerebral autoregulation in patients with PSH syndrome to determine their significance in early prognostication. This single-center retrospective study investigated the relationship between changes in ANS metrics, cerebral autoregulation, and PSH syndrome. Arterial blood pressure and intracranial pressure signals were monitored for 5 days post TBI. ANS metrics included time and frequency domain heart rate variability (HRV) metrics. Cerebral autoregulation was assessed using the pressure reactivity index. Sixty-six patients with severe TBI (median age 33 [interquartile range 26-50] years) were analyzed, and PSH was confirmed in nine cases. Impairment of cerebral autoregulation was observed in 67% of patients with PSH and 72% without the syndrome. Patients with PSH had higher HRV in the low-frequency range (LF; 253 ± 178 vs. 176 ± 227 ms2; p = 0.035) and lower heart rates (HRs; 70 ± 7 vs. 78 ± 19 bpm; p = 0.027) compared to those without PSH. A receiver operating characteristic curve analysis indicated that HR (area under the curve(AUC) = 0.73, p = 0.006) and HRV in the LF (AUC = 0.70, p = 0.009) are moderate predictors of PSH. In the multiple logistic regression model for PSH, diffuse axonal trauma (odds ratio(OR) = 10.82, 95% confidence interval(CI) = 1.70-68.98, p = 0.012) and HR (OR =0.91, 95% CI 0.84-0.98, p = 0.021) were significant factors. Elevated HRV in the LF and decreased HR may serve as early predictors of PSH syndrome development, particularly in patients with diffuse axonal trauma. Further research is needed to investigate the utility of the cerebral autoregulation-ANS relationship in PSH prognostication.

Beat-to-beat analysis of hemodynamic response to mental and psychological stress in sickle cell anemia

Abstract Objectives Vaso-occlusive crises are a hallmark symptom of SCD. Physical stressors can trigger decreased microvascular blood flow and increase the risk for vaso-occlusive crises. However, the effect of mental and psychological stressors on vascular physiology in SCD is not well-established. We hereby examined fluctuations in continuous blood pressure to evaluate hemodynamic changes in SCD patients during mental and psychological stress. Methods Thirteen SCD subjects from the Children’s Hospital Los Angeles and 11 healthy volunteers were recruited. Continuous blood pressure was recorded during 2 mental tasks and 1 psychological stress task. Systolic beat-to-beat blood pressure variability measurements were calculated for each subject. Three very short-term blood pressure variability metrics served as outcome measures: SD, coefficient of variation, and average real variability. Peripheral augmentation index was calculated from arterial waveforms. Linear mixed effects models evaluated associations between patient factors and outcome measures. Results SCD patients exhibit increased systolic blood pressure variability in response to psychological stress. All subjects exhibited a decrease in systolic blood pressure variability in response to mental stress tasks. During mental stress, both groups displayed increased augmentation index, reflective of stress-induced vasoconstriction, while psychological stress in SCD patients led to both decreased mean arterial pressure and increased augmentation index, suggestive of uncompensated vasoconstriction. Conclusion These findings emphasize the impact of mental and psychological stressors on vascular function in SCD and the potential for monitoring physiological signals to predict vaso-occlusive crisis events.

Temporal variability and predictability predict alpine plant community composition and distribution patterns.

One of the most reliable features of natural systems is that they change through time. Theory predicts that temporally fluctuating conditions shape community composition, species distribution patterns, and life history variation, yet features of temporal variability are rarely incorporated into studies of species-environment associations. In this study, we evaluated how two components of temporal environmental variation-variability and predictability-impact plant community composition and species distribution patterns in the alpine tundra of the Southern Rocky Mountains in Colorado (USA). Using the Sensor Network Array at the Niwot Ridge Long-Term Ecological Research site, we used in situ, high-resolution temporal measurements of soil moisture and temperature from 13 locations ("nodes") distributed throughout an alpine catchment to characterize the annual mean, variability, and predictability in these variables in each of four consecutive years. We combined these data with annual vegetation surveys at each node to evaluate whether variability over short (within-day) and seasonal (2- to 4-month) timescales could predict patterns in plant community composition, species distributions, and species abundances better than models that considered average annual conditions alone. We found that metrics for variability and predictability in soil moisture and soil temperature, at both daily and seasonal timescales, improved our ability to explain spatial variation in alpine plant community composition. Daily variability in soil moisture and temperature, along with seasonal predictability in soil moisture, was particularly important in predicting community composition and species occurrences. These results indicate that the magnitude and patterns of fluctuations in soil moisture and temperature are important predictors of community composition and plant distribution patterns in alpine plant communities. More broadly, these results highlight that components of temporal change provide important niche axes that can partition species with different growth and life history strategies along environmental gradients in heterogeneous landscapes.

The Effects of a Single Vagus Nerve's Neurodynamics on Heart Rate Variability in Chronic Stress: A Randomized Controlled Trial.

The modulation of the autonomic nervous system's activity, particularly increasing its parasympathetic tone, is of significant interest in clinical physiotherapy due to its potential benefits for stress-related conditions and recovery processes. This study evaluated the effectiveness of the addition of neurodynamics in enhancing parasympathetic activation in subjects with chronic stress. A clinical trial randomly assigned participants to a group with neurodynamics (6 bpm breathing protocol + manual therapy + neurodynamic technique) or a group without neurodynamics (6 bpm breathing protocol + manual therapy only). Metrics of heart rate variability (HRV), including the Mean Heart Rate (Mean HR), standard deviation of intervals between consecutive heartbeats (SDNN), Heart Rate Difference (Diff. HR), Root Mean Square of Successive Differences (RMSSD), number of intervals differing by more than 50 ms (NN50), percentage of consecutive NN intervals that differed by more than 50 ms (pNN50), and the high-frequency component measured in standardized units (HF), were assessed before, during, and after the intervention. During the intervention, the group with neurodynamics showed significant changes in all variables except in the pNN50 and HF while the group without neurodynamics only showed improvements in the Mean HR, SDNN, and RMSSD. In the post-intervention phase, the group with neurodynamics maintained an increase in HRV while the group without neurodynamics experienced a decrease, suggesting an increase in sympathetic activity. Vagal nerve neurodynamics appear to represent an effective method for enhancing parasympathetic activation in patients with chronic stress. The results highlight the importance of a more comprehensive analysis of HRV variables in order to obtain a correct picture of the impact of interventions on the complex and multifaceted functioning of the autonomic nervous system.