8 citations · 27 across the 45 of their papers we have counts for
49 papers
From Inverse Problems to Adjoint Gradients: A Discrete, Implementation-Oriented Tutorial on PDE-Constrained Optimization
Solmaz S. Kia
The adjoint method is a standard approach for computing gradients in PDE-constrained optimization and central to parameter estimation, model calibration, and optimal control. This…
Submodular Welfare under Routing Coupling: A Hierarchical Decomposition with Perturbation Guarantees
Joan Vendrell Gallart, Nhat-Minh Tang-Nguyen, Alan Kuhnle +1
This paper studies joint submodular welfare maximization and routing over graphs, where agents select items under diminishing returns and transport them through a network with cong…
B-ActiveSEAL: Scalable Uncertainty-Aware Active Exploration with Tightly Coupled Localization-Mapping
Min-Won Seo, Aamodh Suresh, Carlos Nieto-Granda +1
Active robot exploration requires decision-making processes that integrate localization and mapping under tightly coupled uncertainty. However, managing these interdependent uncert…
Microgrids optimal radial reconfiguration via FORWARD algorithm
Joan Vendrell Gallart, Russell Bent, Solmaz Kia
Microgrids offer a promising paradigm for integrating distributed energy resources, bolstering energy resilience, and reducing the impact of blackouts. However, their inherent dece…
FORWARD: A Feasible Radial Reconfiguration Algorithm for Multi-Source Distribution Networks
Joan Vendrell Gallart, Russell Bent, Solmaz Kia
This paper considers an optimal radial reconfiguration problem in multi-source distribution networks, where the goal is to find a radial configuration that minimizes quadratic dist…
BEASST: Behavioral Entropic Gradient based Adaptive Source Seeking for Mobile Robots
Donipolo Ghimire, Aamodh Suresh, Carlos Nieto-Granda +1
This paper presents BEASST (Behavioral Entropic Gradient-based Adaptive Source Seeking for Mobile Robots), a novel framework for robotic source seeking in complex, unknown environm…
ResQue Greedy: Rewiring Sequential Greedy for Improved Submodular Maximization
Joan Vendrell Gallart, Alan Kuhnle, Solmaz Kia
This paper introduces Rewired Sequential Greedy (ResQue Greedy), an enhanced approach for submodular maximization under cardinality constraints. By integrating a novel set curvatur…
NavEX: A Multi-Agent Coverage in Non-Convex and Uneven Environments via Exemplar-Clustering
Donipolo Ghimire, Carlos Nieto-Granda, Solmaz S. Kia
This paper addresses multi-agent deployment in non-convex and uneven environments. To overcome the limitations of traditional approaches, we introduce Navigable Exemplar-Based Disp…
Submodular Maximization Subject to Uniform and Partition Matroids: From Theory to Practical Applications and Distributed Solutions
Solmaz S. Kia
This article provides a comprehensive exploration of submodular maximization problems, focusing on those subject to uniform and partition matroids. Crucial for a wide array of appl…
FORWARD: Feasibility Oriented Random-Walk Inspired Algorithm for Radial Reconfiguration in Distribution Networks
Joan Vendrell, Russell Bent, Solmaz Kia
We consider an optimal flow distribution problem in which the goal is to find a radial configuration that minimizes resistance-induced quadratic distribution costs while ensuring d…
Optimality Gap of Decentralized Submodular Maximization under Probabilistic Communication
Joan Vendrell, Solmaz Kia
This paper considers the problem of decentralized submodular maximization subject to partition matroid constraint using a sequential greedy algorithm with probabilistic inter-agent…
Multi-agent Coverage Control: From Discrete Assignments to Continuous Multi-agent Distribution Matching
Solmaz Kia, Sonia Martinez
The multi-agent spatial coverage control problem encompasses a broad research domain, dealing with both dynamic and static deployment strategies, discrete-task assignments, and spa…
Sequential Gaussian Variational Inference for Nonlinear State Estimation and Its Application in Robot Navigation
Min-Won Seo, Solmaz S. Kia
Probabilistic state estimation is essential for robots navigating uncertain environments. Accurately and efficiently managing uncertainty in estimated states is key to robust robot…
Projected Forward Gradient-Guided Frank-Wolfe Algorithm via Variance Reduction
M. Rostami, S. S. Kia
This paper aims to enhance the use of the Frank-Wolfe (FW) algorithm for training deep neural networks. Similar to any gradient-based optimization algorithm, FW suffers from high c…
Stein Coverage: a Variational Inference Approach to Distribution-matching Multisensor Deployment
Donipolo Ghimire, Solmaz S. Kia
This paper examines the spatial coverage optimization problem for multiple sensors in a known convex environment, where the coverage service of each sensor is heterogeneous and ani…
A Computationally Efficient Maximum A Posteriori Sequence Estimation via Stein Variational Inference
Min-Won Seo, Solmaz S. Kia
State estimation in robotic systems presents significant challenges, particularly due to the prevalence of multimodal posterior distributions in real-world scenarios. One effective…
First-Order Dynamic Optimization for Streaming Convex Costs
M. Rostami, H. Moradian, S. S. Kia
This paper proposes a set of novel optimization algorithms for solving a class of convex optimization problems with time-varying streaming cost function. We develop an approach to…
Bayesian Online Learning for Human-assisted Target Localization
Min-Won Seo, Solmaz S. Kia
We consider a human-assisted autonomy sensor fusion for dynamic target localization in a Bayesian framework. Autonomous sensor-based tracking systems can suffer from observability…
Distributed Unconstrained Optimization with Time-varying Cost Functions
Amir-Salar Esteki, Solmaz S. Kia
In this paper, we propose a novel solution for the distributed unconstrained optimization problem where the total cost is the summation of time-varying local cost functions of a gr…
Fast model averaging via buffered states and first-order accelerated optimization algorithms
Amir-Salar Esteki, Hossein Moradian, Solmaz S. Kia
In this letter, we study the problem of accelerating reaching average consensus over connected graphs in a discrete-time communication setting. Literature has shown that consensus…
Federated Learning Using Variance Reduced Stochastic Gradient for Probabilistically Activated Agents
M. R. Rostami, S. S. Kia
This paper proposes an algorithm for Federated Learning (FL) with a two-layer structure that achieves both variance reduction and a faster convergence rate to an optimal solution i…
The fastest linearly converging discrete-time average consensus using buffered information
Amir-Salar Esteki, Hossein Moradian, Solmaz S. Kia
In this letter, we study the problem of accelerating reaching average consensus over connected graphs in a discrete-time communication setting. Literature has shown that consensus…
Online Target Localization using Adaptive Belief Propagation in the HMM Framework
Min-Won Seo, Solmaz S. Kia
This paper proposes a novel adaptive sample space-based Viterbi algorithm for target localization in an online manner. The method relies on discretizing the target's motion space i…
Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization
Jianan Zhu, Solmaz S. Kia
In cooperative localization, communicating mobile agents use inter-agent relative measurements to improve their dead-reckoning-based global localization. Measurement scheduling ena…