arXiv subjects

Silan Zhang

Publications and source records attributed to Silan Zhang.

6 recordsLinked to original sources

arXiv2026

Distributed Zeroth-Order Composite Finite-Sum Optimization via Incremental Variance Reduction

We study decentralized composite finite-sum optimization over a fixed connected undirected network, where agents access local component functions through function evaluations and share a possibly nonsmooth convex regularizer. In this setting, standard two-point gradient estimators can retain nonvanishing variance at a solution, complicating their integration with gradient tracking. We propose Distributed Zeroth-Order Incremental Variance Reduction (Distributed ZIVR), which combines incrementally updated component-gradient estimates with a proximal gradient-tracking scheme. The method admits an implementation using a constant number of function evaluations per agent at every iteration, without intermittent full-component or full-coordinate refreshes. For convex smooth component functions and strongly convex local averages, we establish linear convergence up to a controllable finite-difference error. With the finite-difference radius chosen for the target accuracy, Chebyshev-accelerated mixing yields a per-agent oracle complexity of $\widetilde{O}(d(m+κ)\log(1/ε))$ for expected squared solution error $ε$, where $d$ is the dimension, $m$ is the number of components per agent, and $κ$ is the condition number. This matches the centralized ZIVR oracle bound, while the communication bound has only an inverse square-root dependence on the network spectral gap. Experiments on logistic and robust regression demonstrate rapid reduction of solution and consensus errors.

math.OC↗

arXiv2026

Homogeneous Charge Relaxation in Organic Mixed Ionic-Electronic Conductors

Organic mixed ionic-electronic conductors (OMIECs) couple ionic and electronic signals, enabling energy storage, neuromorphic computing, and bioelectronics. However, how charge relaxes in water once the bias is removed, which determines how long stored states persist, remains unclear. Using operando charge photometry, we tracked charge in OMIEC films during biasing and open-circuit relaxation. Under bias, charges moved as redox fronts. After bias removal, charge instead relaxed uniformly across the channel. Modeling and cryo-electron microscopy indicate that swelling creates connected water-rich pathways through the bulk. These pathways speed ion motion but also let stored charge leak away. Tuning the terminal side chain of a polythiophene controls water uptake and ion trapping. Bulky hydrophobic side chains enable stable programmable states, whereas hydrophilic side chains support fast electrophysiology sensing.

cond-mat.mtrl-sci↗

arXiv2026

ZIVR: An Incremental Variance Reduction Technique For Zeroth-Order Composite Problems

This paper investigates zeroth-order (ZO) finite-sum composite optimization. Recently, variance reduction techniques have been applied to ZO methods to mitigate the non-vanishing variance of 2-point estimators in constrained/composite optimization, yielding improved convergence rates. However, existing ZO variance reduction methods typically involve batch sampling of size at least $Θ(n)$ or $Θ(d)$, which can be computationally prohibitive for large-scale problems. In this work, we propose a general variance reduction framework, Zeroth-Order Incremental Variance Reduction (ZIVR), which supports flexible implementations$\unicode{x2014}$including a pure 2-point zeroth-order algorithm that eliminates the need for large batch sampling. Furthermore, we establish comprehensive convergence guarantees for ZIVR across strongly-convex, convex, and non-convex settings that match their first-order counterparts. Numerical experiments validate the effectiveness of our proposed algorithm.

math.OC↗

arXiv2024

Zeroth-Order Katyusha: An Accelerated Derivative-Free Method for Composite Convex Optimization

We investigate accelerated zeroth-order algorithms for smooth composite convex optimization problems. While for unconstrained optimization, existing methods that merge 2-point zeroth-order gradient estimators with first-order frameworks usually lead to satisfactory performance, for constrained/composite problems, there is still a gap in the complexity bound that is related to the non-vanishing variance of the 2-point gradient estimator near an optimal point. To bridge this gap, we propose the Zeroth-Order Loopless Katyusha (ZO-L-Katyusha) algorithm, leveraging the variance reduction as well as acceleration techniques from the first-order loopless Katyusha algorithm. We show that ZO-L-Katyusha is able to achieve accelerated linear convergence for compositve smooth and strongly convex problems, and has the same oracle complexity as the unconstrained case. Moreover, the number of function queries to construct a zeroth-order gradient estimator in ZO-L-Katyusha can be made to be O(1) on average. These results suggest that ZO-L-Katyusha provides a promising approach towards bridging the gap in the complexity bound for zeroth-order composite optimization.

math.OC↗

arXiv2022

Stable ion-tunable antiambipolarity in mixed ion-electron conducting polymers enables biorealistic artificial neurons

Bio-integrated neuromorphic systems promise for new protocols to record and regulate the signaling of biological systems. Making such artificial neural circuits successful requires minimal circuit complexity and ion-based operating mechanisms similar to that of biology. However, simple leaky integrate-and-fire model neurons, commonly realized in either silicon or organic semiconductor neuromorphic systems, can emulate only a few neural features. More functional neuron models, based on traditional complex Si-based complementary-metal-oxide-semiconductor (CMOS) or negative differential resistance (NDR) device circuits, are complicated to fabricate, not biocompatible, and lack ion- and chemical-based modulation features. Here we report a biorealistic conductance-based organic electrochemical neuron (c-OECN) using a mixed ion-electron conducting ladder-type polymer with reliable ion-tunable antiambipolarity. The latter is used to emulate the activation/inactivation of Na channels and delayed activation of K channels of biological neurons. These c-OECNs can then spike at bioplausible frequencies nearing 100 Hz, emulate most critical biological neural features, demonstrate stochastic spiking, and enable neurotransmitter and Ca2+-based spiking modulation. These combined features are impossible to achieve using previous technologies.

cond-mat.soft↗

arXiv2022

Fully 3D-Printed Organic Electrochemical Transistors

Organic electrochemical transistors (OECTs) are currently being investigated for various applications, ranging from sensors to logics and neuromorphic hardware. The fabrication process must be compatible with flexible and scalable digital techniques to address this wide spectrum of applications. Here, we report a direct-write additive process to fabricate fully 3D printed OECTs. We developed 3D printable conducting, semiconducting, insulating, and electrolyte inks to achieve this. The 3D-printed OECTs, operating in the depletion mode, can be fabricated on thin and flexible substrates, yielding high mechanical and environmental stability. We also developed a 3D printable nanocellulose formulation for the OECT substrate, demonstrating one of the first examples of fully 3D printed electronic devices. Good dopamine biosensing capabilities (limit of detection down to 6 uM without metal gate electrodes) and long-term (~1 hour) synapses response underscore that the present OECT manufacturing strategy is suitable for diverse applications requiring rapid design change and digitally enabled direct-write techniques.

physics.app-ph↗