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James Nguyen

Publications and source records attributed to James Nguyen.

7 recordsLinked to original sources
arXiv2026

From Recency Bias to Stable Convergence Block Kaczmarz Methods for Online Preference Learning in Matchmaking Applications

We present a family of Kaczmarz-based preference learning algorithms for real-time personalized matchmaking in reciprocal recommender systems. Post-step L2 normalization, common in Kaczmarz-inspired online learners, induces exponential recency bias: the influence of the t-th interaction decays as eta^(n - t), reaching approximately 1e-6 after just 20 swipes at eta = 0.5. We resolve this by replacing the normalization step with a Tikhonov-regularized projection denominator that bounds step size analytically without erasing interaction history. When candidate tag vectors are not pre-normalized, as in realistic deployments where candidates vary in tag density, the Tikhonov denominator ||a||^2 + alpha produces genuinely per-candidate adaptive step sizes, making it structurally distinct from online gradient descent with any fixed learning rate. We further derive a block variant that processes full swipe sessions as a single Gram matrix solve. Population-scale simulation over 6,400 swipes reveals that Block Normalized Kaczmarz (BlockNK), which combines the batch Gram solve with post-session L2 normalization, achieves the highest preference alignment (Align@20 = 0.698), the strongest inter-session direction stability (delta = 0.994), and the flattest degradation profile under label noise across flip ratios p_flip in [0.10, 0.35]. Experiments under cosine similarity subsampling further show that adaptively filtering the candidate pool toward the current preference direction substantially improves asymptotic alignment, at the cost of introducing a feedback loop that may slow recovery from miscalibration. The sequential Tikhonov-Kaczmarz method performs comparably to K-NoNorm under our simulation conditions, suggesting the dominant practical gain over normalized Kaczmarz is the removal of per-step normalization rather than the Tikhonov constant alpha itself.

cs.LG↗
arXiv2025

Read Between the Hyperplanes: On Spectral Projection and Sampling Approaches to Randomized Kaczmarz

Among recent developments centered around Randomized Kaczmarz (RK), a row-sampling iterative projection method for large-scale linear systems, several adaptions to the method have inspired faster convergence. Focusing solely on ill-conditioned and overdetermined linear systems, we highlight inter-row relationships that can be leveraged to guide directionally aware projections. In particular, we find that improved convergence rates can be made by (i) projecting onto pairwise row differences, (ii) sampling from partitioned clusters of nearly orthogonal rows, or (iii) more frequently sampling spectrally-diverse rows.

math.NA↗
arXiv2023

Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium Ion Batteries, Part-1: Performance Measures

The open circuit voltage to the state of charge (OCVSOC) characteristic is crucial for battery management systems. Using the OCV-SOC curve, the SOC and the battery capacity can be estimated in real-time. Accurate SOC and capacity information are important to carry out the majority of battery management functionalities that ensure a safe, efficient, and reliable battery pack power system. Numerous approaches have been reported in the literature for improved SOC estimation and battery capacity estimation. These approaches focus on various estimation and filtering techniques to reduce the effect of measurement noise and uncertainties due to hysteresis and relaxation effects. Even though all the existing approaches to SOC estimation rely on the OCV-SOC characterization, little attention was paid to investigating the possibility of errors in the OCV-SOC characterization and the effect of uncertainty in the OCV-SOC curve on SOC and capacity estimates. In this paper, which is the first part of a series of three papers, the effect of OCV-SOC modeling error in the overall battery management system is discussed. The different sources of uncertainties in the OCV-SOC curve include cell-to-cell variation, temperature variation, aging drift, cycle rate effect, curve-fitting error, and measurement/estimation error. The proposed uncertainty models can be incorporated into battery management systems to improve their safety, performance, and reliability.

eess.SY↗
arXiv2023

Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium Ion Batteries, Part-2: Data Collection Procedure

This paper is the second part of a series of papers about empirical approaches to open circuit voltage (OCV) modeling and its performance comparison in lithium-ion batteries. The first part of the series introduced various sources of uncertainties in the OCV models and established a theoretical relationship between uncertainties and the performance of a battery management system. In this paper, clearly defined approaches for low-rate OCV data collection are defined and described in detail. The data collection is designed with consideration to several parameters that affect the experimental time. Firstly, a more suitable method to fully charge the battery at different C-Rates is defined. Secondly, the OCV characterization following the full charge is described for various performance comparisons. Finally, optimal and efficient resistance estimation profiles are discussed. From the voltage, current and time data recorded using the procedure described in this paper, the OCV-SOC relationship is characterized and its uncertainties are modeled in the third part of this series of papers.

eess.SY↗
arXiv2023

Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium Ion Batteries, Part-3: Experimental Results

This paper is the third part of a series of papers about empirical approaches to open circuit voltage (OCV) modeling of lithium-ion batteries. The first part of the series proposed models to quantify various sources of uncertainties in the OCV models; and, the second part of the series presented systematic data collection approaches to compute the uncertainties in the OCV-SOC models. This paper uses data collected from 28 OCV characterization experiments, performed according to the data collection plan presented, to compute and analyze the following three different OCV uncertainty metrics: cell-to-cell variations, cycle-rate error, and curve fitting error. From the computed metrics, it was observed that a lower C-Rate showed smaller errors in the OCV-SOC model and vice versa. The results reported in this paper establish a relationship between the C-Rate and the uncertainty of the OCV-SOC model. This research can be thus useful to battery researchers for quantifying the tradeoff between the time taken to complete the OCV characterization test and the corresponding uncertainty in the OCV-SOC modeling. Further, quantified uncertainty model parameters can be used to accurately characterize the uncertainty in various battery management functionalities, such as state of charge and state of health estimation.

eess.SY↗
arXiv2017

Why surplus structure is not superfluous

The idea that gauge theory has 'surplus' structure poses a puzzle: in one much discussed sense, this structure is redundant; but on the other hand, it is also widely held to play an essential role in the theory. In this paper, we employ category-theoretic tools to illuminate an aspect of this puzzle. We precisify what is meant by 'surplus' structure by means of functorial comparisons with equivalence classes of gauge fields, and then show that such structure is essential for any theory that represents a rich collection of physically relevant fields which are 'local' in nature.

physics.hist-ph↗