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Edwin K. P. Chong

Publications and source records attributed to Edwin K. P. Chong.

At least 19 recordsLinked to original sources
arXiv2026

Performance Guarantees for Data-Driven Sequential Decision-Making

The solutions to many sequential decision-making problems are characterized by dynamic programming and Bellman's principle of optimality. However, due to the inherent complexity of solving Bellman's equation exactly, there has been significant interest in developing various approximate dynamic programming (ADP) schemes to obtain near-optimal solutions. A fundamental question that arises is: how close are the objective values produced by ADP schemes relative to the true optimal objective values? In this paper, we develop a general framework that provides performance guarantees for ADP schemes in the form of ratio bounds. Specifically, we show that the objective value under an ADP scheme is at least a computable fraction of the optimal value. We further demonstrate the applicability of our theoretical framework through several applications: data-driven robot path planning, pendulum stabilization, and multi-agent sensor coverage.

eess.SY↗
arXiv2026

A Performance Bound for the Greedy Algorithm in a Generalized Class of String Optimization Problems

We present a simple performance bound for the greedy scheme in string optimization problems that obtains strong results. Our approach vastly generalizes the group of previously established greedy curvature bounds by Conforti and Cornuéjols (1984). We consider three constants, $α_G$, $α_G'$, and $α_G''$ introduced by Conforti and Cornuéjols (1984), that are used in performance bounds of greedy schemes in submodular set optimization. We first generalize both of the $α_G$ and $α_G''$ bounds to string optimization problems in a manner that includes maximizing submodular set functions over matroids as a special case. We then derive a much simpler and computable bound that allows for applications to a far more general class of functions with string domains. We prove that our bound is superior to both the $α_G$ and $α_G''$ bounds and provide a counterexample to show that the $α_G'$ bound is incorrect under the assumptions in Conforti and Cornuéjols (1984). We conclude with two applications. The first is an application of our result to sensor coverage problems. We demonstrate our performance bound in cases where the objective function is set submodular and string submodular. The second is an application to a social welfare maximization problem with black-box utility functions.

eess.SY↗
arXiv2026

LLM-assisted gNB Parameter Configuration for Radio Access Network

gNB parameter misconfigurations are a common cause of system failures in radio access networks (RANs), and their diagnosis and correction rely on manual analysis of complex network logs that does not scale well. This paper proposes a large language model (LLM)-assisted framework for automatic gNB parameter configuration. The framework adopts a synthetic data generation pipeline following a configuration, log, correction workflow. Starting from a workable configuration and the gNB technical references, the pipeline uses a commercial LLM to generate modified configurations and derive structured reasoning traces from gNB error logs. The synthetic training data maps network states to corrective actions and is used to fine-tune an LLM for configuration correction. During inference, the fine-tuned LLM generates valid and deployable gNB parameter configurations from gNB error logs. The framework is validated on an OpenAirInterface (OAI) gNB testbed with 480 unseen misconfiguration scenarios, where fine-tuning improves correction accuracy from 13.8% (zero-shot baseline) to 85.4%, and retrieval-augmented generation (RAG) further improves accuracy to 92.7%. The results demonstrate that the framework may enable automated recovery from misconfigurations without manual intervention and supports scalable and autonomous RAN operation.

cs.NI↗
arXiv2025

Intent-Based Network for RAN Management with Large Language Models

Advanced intelligent automation becomes an important feature to deal with the increased complexity in managing wireless networks. This paper proposes a novel automation approach of intent-based network for Radio Access Networks (RANs) management by leveraging Large Language Models (LLMs). The proposed method enhances intent translation, autonomously interpreting high-level objectives, reasoning over complex network states, and generating precise configurations of the RAN by integrating LLMs within an agentic architecture. We propose a structured prompt engineering technique and demonstrate that the network can automatically improve its energy efficiency by dynamically optimizing critical RAN parameters through a closed-loop mechanism. It showcases the potential to enable robust resource management in RAN by adapting strategies based on real-time feedback via LLM-orchestrated agentic systems.

cs.NI↗
arXiv2024

On Bounds for Greedy Schemes in String Optimization based on Greedy Curvatures

We consider the celebrated bound introduced by Conforti and Cornuéjols (1984) for greedy schemes in submodular optimization. The bound assumes a submodular function defined on a collection of sets forming a matroid and is based on greedy curvature. We show that the bound holds for a very general class of string problems that includes maximizing submodular functions over set matroids as a special case. We also derive a bound that is computable in the sense that they depend only on quantities along the greedy trajectory. We prove that our bound is superior to the greedy curvature bound of Conforti and Cornuéjols. In addition, our bound holds under a condition that is weaker than submodularity.

eess.SY↗
arXiv2023

An Improved Greedy Curvature Bound in Finite-Horizon String Optimization with Application to a Sensor Coverage Problem

We study the optimization problem of choosing strings of finite length to maximize string submodular functions on string matroids, which is a broader class of problems than maximizing set submodular functions on set matroids. We provide a lower bound for the performance of the greedy algorithm in our problem, and then prove that our bound is superior to the greedy curvature bound of Conforti and Cornuejols. Our bound has lower computational complexity than most previously proposed curvature bounds. Finally, we demonstrate the strength of our result on a sensor coverage problem.

cs.DS↗
arXiv2022

Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning

Single-pixel imaging (SPI) is a novel imaging technique whose working principle is based on the compressive sensing (CS) theory. In SPI, data is obtained through a series of compressive measurements and the corresponding image is reconstructed. Typically, the reconstruction algorithm such as basis pursuit relies on the sparsity assumption in images. However, recent advances in deep learning have found its uses in reconstructing CS images. Despite showing a promising result in simulations, it is often unclear how such an algorithm can be implemented in an actual SPI setup. In this paper, we demonstrate the use of deep learning on the reconstruction of SPI images in conjunction with block compressive sensing (BCS). We also proposed a novel reconstruction model based on convolutional neural networks that outperforms other competitive CS reconstruction algorithms. Besides, by incorporating BCS in our deep learning model, we were able to reconstruct images of any size above a certain smallest image size. In addition, we show that our model is capable of reconstructing images obtained from an SPI setup while being priorly trained on natural images, which can be vastly different from the SPI images. This opens up opportunity for the feasibility of pretrained deep learning models for CS reconstructions of images from various domain areas.

eess.IV↗
arXiv2022

Well-Conditioned Linear Minimum Mean Square Error Estimation

Linear minimum mean square error (LMMSE) estimation is often ill-conditioned, suggesting that unconstrained minimization of the mean square error is an inadequate approach to filter design. To address this, we first develop a unifying framework for studying constrained LMMSE estimation problems. Using this framework, we explore an important structural property of constrained LMMSE filters involving a certain prefilter. Optimality is invariant under invertible linear transformations of the prefilter. This parameterizes all optimal filters by equivalence classes of prefilters. We then clarify that merely constraining the rank of the filter does not suitably address the problem of ill-conditioning. Instead, we adopt a constraint that explicitly requires solutions to be well-conditioned in a certain specific sense. We introduce two well-conditioned filters and show that they converge to the unconstrained LMMSE filter as their truncation-power loss goes to zero, at the same rate as the low-rank Wiener filter. We also show extensions to the case of weighted trace and determinant of the error covariance as objective functions. Finally, our quantitative results with historical VIX data demonstrate that our two well-conditioned filters have stable performance while the standard LMMSE filter deteriorates with increasing condition number.

eess.SP↗
arXiv2020

Bayesian Learning of Occupancy Grids

Occupancy grids encode for hot spots on a map that is represented by a two dimensional grid of disjoint cells. The problem is to recursively update the probability that each cell in the grid is occupied, based on a sequence of sensor measurements from a moving platform. In this paper, we provide a new Bayesian framework for generating these probabilities that does not assume statistical independence between the occupancy state of grid cells. This approach is made analytically tractable through the use of binary asymmetric channel models that capture the errors associated with observing the occupancy state of a grid cell. Binary-valued measurement vectors are the thresholded output of a sensor in a radar, sonar, or other sensory system. We compare the performance of the proposed framework to that of the classical formulation for occupancy grids. The results show that the proposed framework identifies occupancy grids with lower false alarm and miss detection rates, and requires fewer observations of the surrounding area, to generate an accurate estimate of occupancy probabilities when compared to conventional formulations.

eess.SP↗
arXiv2020

Information-Theoretic Approach to Navigation for Efficient Detection and Classification of Underwater Objects

This paper addresses an autonomous exploration problem in which a mobile sensor, placed in a previously unseen search area, utilizes an information-theoretic navigation cost function to dynamically select the next sensing action, i.e., location from which to take a measurement, to efficiently detect and classify objects of interest within the area. The information-theoretic cost function proposed in this paper consist of two \textit{information gain} terms, one for detection and localization of objects and the other for sequential classification of the detected objects. We present a novel closed-form representation for the cost function, derived from the definition of mutual information. We evaluate three different policies for choosing the next sensing action: lawn mower, greedy, and non-greedy. For these three policies, we compare the results from our information-theoretic cost functions to the results of other information-theoretic inspired cost functions. Our simulation results show that search efficiency is greater using the proposed cost functions compared to those of the other methods, and that the greedy and non-greedy policies outperform the lawn mower policy.

eess.SP↗
arXiv2020

Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network

Automated pavement crack image segmentation is challenging because of inherent irregular patterns, lighting conditions, and noise in images. Conventional approaches require a substantial amount of feature engineering to differentiate crack regions from non-affected regions. In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images. Our approach requires minimal feature engineering compared to other machine learning techniques. We propose a U-Net-based network architecture in which we replace the encoder with a pretrained ResNet-34 neural network. We use a "one-cycle" training schedule based on cyclical learning rates to speed up the convergence. Our method achieves an F1 score of 96% on the CFD dataset and 73% on the Crack500 dataset, outperforming other algorithms tested on these datasets. We perform ablation studies on various techniques that helped us get marginal performance boosts, i.e., the addition of spatial and channel squeeze and excitation (SCSE) modules, training with gradually increasing image sizes, and training various neural network layers with different learning rates.

cs.CV↗
arXiv2019

Decision Automation for Electric Power Network Recovery

Critical infrastructure systems such as electric power networks, water networks, and transportation systems play a major role in the welfare of any community. In the aftermath of disasters, their recovery is of paramount importance; orderly and efficient recovery involves the assignment of limited resources (a combination of human repair workers and machines) to repair damaged infrastructure components. The decision maker must also deal with uncertainty in the outcome of the resource-allocation actions during recovery. The manual assignment of resources seldom is optimal despite the expertise of the decision maker because of the large number of choices and uncertainties in consequences of sequential decisions. This combinatorial assignment problem under uncertainty is known to be \mbox{NP-hard}. We propose a novel decision technique that addresses the massive number of decision choices for large-scale real-world problems; in addition, our method also features an experiential learning component that adaptively determines the utilization of the computational resources based on the performance of a small number of choices. Our framework is closed-loop, and naturally incorporates all the attractive features of such a decision-making system. In contrast to myopic approaches, which do not account for the future effects of the current choices, our methodology has an anticipatory learning component that effectively incorporates \emph{lookahead} into the solutions. To this end, we leverage the theory of regression analysis, Markov decision processes (MDPs), multi-armed bandits, and stochastic models of community damage from natural disasters to develop a method for near-optimal recovery of communities. Our method contributes to the general problem of MDPs with massive action spaces with application to recovery of communities affected by hazards.

cs.AI↗
arXiv2019

Submodular Optimization Problems and Greedy Strategies: A Survey

The greedy strategy is an approximation algorithm to solve optimization problems arising in decision making with multiple actions. How good is the greedy strategy compared to the optimal solution? In this survey, we mainly consider two classes of optimization problems where the objective function is submodular. The first is set submodular optimization, which is to choose a set of actions to optimize a set submodular objective function, and the second is string submodular optimization, which is to choose an ordered set of actions to optimize a string submodular function. Our emphasis here is on performance bounds for the greedy strategy in submodular optimization problems. Specifically, we review performance bounds for the greedy strategy, more general and improved bounds in terms of curvature, performance bounds for the batched greedy strategy, and performance bounds for Nash equilibria.

math.OC↗
arXiv2019

Network Topology Inference Using Information Cascades with Limited Statistical Knowledge

We study the problem of inferring network topology from information cascades, in which the amount of time taken for information to diffuse across an edge in the network follows an unknown distribution. Unlike previous studies, which assume knowledge of these distributions, we only require that diffusion along different edges in the network be independent together with limited moment information (e.g., the means). We introduce the concept of a separating vertex set for a graph, which is a set of vertices in which for any two given distinct vertices of the graph, there exists a vertex whose distance to them are different. We show that a necessary condition for reconstructing a tree perfectly using distance information between pairs of vertices is given by the size of an observed separating vertex set. We then propose an algorithm to recover the tree structure using infection times, whose differences have means corresponding to the distance between two vertices. To improve the accuracy of our algorithm, we propose the concept of redundant vertices, which allows us to perform averaging to better estimate the distance between two vertices. Though the theory is developed mainly for tree networks, we demonstrate how the algorithm can be extended heuristically to general graphs. Simulations using synthetic and real networks, and experiments using real-world data suggest that our proposed algorithm performs better than some current state-of-the-art network reconstruction methods.

cs.SI↗
arXiv2018

Optimal Stochastic Dynamic Scheduling for Managing Community Recovery from Natural Hazards

Following the occurrence of an extreme natural or man-made event, community recovery management should aim at providing optimal restoration policies for a community over a planning horizon. Calculating such optimal restoration polices in the presence of uncertainty poses significant challenges for community leaders. Stochastic scheduling for several interdependent infrastructure systems is a difficult control problem with huge decision spaces. The Markov decision process (MDP)-based optimization approach proposed in this study incorporates different sources of uncertainties to compute the restoration policies. The computation of optimal scheduling schemes using our method employs the rollout algorithm, which provides an effective computational tool for optimization problems dealing with real-world large-scale networks and communities. We apply the proposed methodology to a realistic community recovery problem, where different decision-making objectives are considered. Our approach accommodates current restoration strategies employed in recovery management. Our computational results indicate that the restoration policies calculated using our techniques significantly outperform the current recovery strategies. Finally, we study the applicability of our method to address different risk attitudes of policymakers, which include risk-neutral and risk-averse attitudes in the community recovery management.

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arXiv2018

An approximate dynamic programming approach to food security of communities following hazards

Food security can be threatened by extreme natural hazard events for households of all social classes within a community. To address food security issues following a natural disaster, the recovery of several elements of the built environment within a community, including its building portfolio, must be considered. Building portfolio restoration is one of the most challenging elements of recovery owing to the complexity and dimensionality of the problem. This study introduces a stochastic scheduling algorithm for the identification of optimal building portfolio recovery strategies. The proposed approach provides a computationally tractable formulation to manage multi-state, large-scale infrastructure systems. A testbed community modeled after Gilroy, California, is used to illustrate how the proposed approach can be implemented efficiently and accurately to find the near-optimal decisions related to building recovery following a severe earthquake.

cs.CY↗
arXiv2018

An Approximate Dynamic Programming Approach to Community Recovery Management (Extended Abstract)

The functioning of interdependent civil infrastructure systems in the aftermath of a disruptive event is critical to the performance and vitality of any modern urban community. Post-event stressors and chaotic circumstances, time limitations, and complexities in the community recovery process highlight the necessity for a comprehensive decision-making framework at the community-level for post-event recovery management. Such a framework must be able to handle large-scale scheduling and decision processes, which involve difficult control problems with large combinatorial decision spaces. This study utilizes approximate dynamic programming algorithms along with heuristics for the identification of optimal community recovery actions following the occurrence of an extreme earthquake event. The proposed approach addresses the curse of dimensionality in its analysis and management of multi-state, large-scale infrastructure systems. Furthermore, the proposed approach can consider the cur-rent recovery policies of responsible public and private entities within the community and shows how their performance might be improved. A testbed community coarsely modeled after Gilroy, California, is utilized as an illustrative example. While the illustration provides optimal policies for the Electrical Power Network serving Gilroy following a severe earthquake, preliminary work shows that the methodology is computationally well suited to other infrastructure systems and hazards.

cs.CY↗