Explanipedia

Hybrid Powertrain Optimization for Regional Aircraft Integrating Hydrogen Fuel Cells and Aluminum Air Batteries Open

Harshal D. Kaushik, Ali Mahboub Rad, Korebami Adebajo, Sobhan Badakhshan, Natalie Cooper , et al. · 2025

With the increasing demand for air travel and the urgency to reduce emissions, transitioning from fossil fuel-based propulsion systems is a critical step toward sustainable aviation. While batteries are widely used in urban air mobility, t…

On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds Open

Ming Jin, Vanshaj Khattar, Harshal D. Kaushik, Bilgehan Sel, Ruoxi Jia · 2023

We study the expressibility and learnability of solution functions of convex optimization and their multi-layer architectural extension. The main results are: (1) the class of solution functions of linear programming (LP) and quadratic pro…

Decision-Focused Learning for Inverse Noncooperative Games: Generalization Bounds and Convergence Analysis Open

Ahmad Al-Tawaha, Harshal D. Kaushik, Bilgehan Sel, Ruoxi Jia, Ming Jin · 2023

Finding the equilibrium strategy of agents is one of the central problems in game theory. Perhaps equally intriguing is the inverse of the above problem: from the available finite set of actions at equilibrium, how can we learn the utiliti…

On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds Open

Ming Jin, Vanshaj Khattar, Harshal D. Kaushik, Bilgehan Sel, Ruoxi Jia · 2022

We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension. The main results are: \emph{(1)} the class of solution functions of linear programming (LP) and quadratic…

Iterative Implicit Gradients for Nonconvex Optimization with Variational Inequality Constraints Open

Harshal D. Kaushik, Ming Jin · 2022

We propose an optimization proxy in terms of iterative implicit gradient methods for solving constrained optimization problems with nonconvex loss functions. This framework can be applied to a broad range of machine learning settings, incl…

Distributed Randomized Block Stochastic Gradient Tracking Method Open

Farzad Yousefian, Jayesh Yevale, Harshal D. Kaushik · 2021

We consider distributed optimization over networks where each agent is associated with a smooth and strongly convex local objective function. We assume that the agents only have access to unbiased estimators of the gradient of their object…

An Incremental Gradient Method for Optimization Problems with Variational Inequality Constraints Open

Harshal D. Kaushik, Farzad Yousefian · 2021

We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associated with a local monotone mapping. This problem finds an a…

An Incremental Gradient Method for Large-scale Distributed Nonlinearly Constrained Optimization Open

Harshal D. Kaushik, Farzad Yousefian · 2021

Motivated by applications arising from sensor networks and machine learning, we consider the problem of minimizing a finite sum of nondifferentiable convex functions where each component function is associated with an agent and a hard-to-p…

A Method with Convergence Rates for Optimization Problems with Variational Inequality Constraints Open

Harshal D. Kaushik, Farzad Yousefian · 2021

We consider a class of optimization problems with Cartesian variational inequality (CVI) constraints, where the objective function is convex and the CVI is associated with a monotone mapping and a convex Cartesian product set. This mathema…

A Method with Convergence Rates for Optimization Problems with\n Variational Inequality Constraints Open

Harshal D. Kaushik, Farzad Yousefian · 2020

We consider a class of optimization problems with Cartesian variational\ninequality (CVI) constraints, where the objective function is convex and the\nCVI is associated with a monotone mapping and a convex Cartesian product set.\nThis math…

A Projection-free Incremental Gradient Method for Large-scale Constrained Optimization Open

Harshal D. Kaushik, Farzad Yousefian · 2020

The problem of minimizing finite sums where each component function is associated with a block of dataset, is very popular in machine learning. Among well-known avenues to address this class of problems is the incremental gradient (IG) met…

An Incremental Gradient Method for Large-scale Distributed Nonlinearly\n Constrained Optimization Open

Harshal D. Kaushik, Farzad Yousefian · 2020

Motivated by applications arising from sensor networks and machine learning,\nwe consider the problem of minimizing a finite sum of nondifferentiable convex\nfunctions where each component function is associated with an agent and a\nhard-t…

A log-third order polynomial normal transformation approach for high-reliability estimation with scarce samples Open

Palaniappan Ramu, Harshal D. Kaushik · 2020

Normal transformations are often used in reliability analysis. A Third order Polynomial Normal Transformation (TPNT) approach is used in this work. The underlying idea is to approximate the Cumulative Distribution Function (CDF) of the res…

A Randomized Block Coordinate Iterative Regularized Gradient Method for High-dimensional Ill-posed Convex Optimization Open

Harshal D. Kaushik, Farzad Yousefian · 2018

Motivated by high-dimensional nonlinear optimization problems as well as ill-posed optimization problems arising in image processing, we consider a bilevel optimization model where we seek among the optimal solutions of the inner level pro…

Utilization of Wind Shear for Powering Unmanned Aerial Vehicles in Surveillance Application: A Numerical Optimization Study Open

Harshal D. Kaushik, Ranjith Mohan, K. Arul Prakash · 2016

Dynamic soaring is the rationale behind the prolonged flights of a seabird Albatross. It involves utilization of energy from the wind shear present near the earth surface. Small unmanned aerial vehicles (UAVs) can be kept loitering without…

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