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Found 17 papers, 2 papers with code
Stochastic Online Optimization for Cyber-Physical and Robotic Systems
We propose a novel gradient-based online optimization framework for solving stochastic programming problems that frequently arise in the context of cyber-physical and robotic systems.
Primal Methods for Variational Inequality Problems with Functional Constraints
In this work, we propose a simple primal method, termed Constrained Gradient Method (CGM), for addressing functional constrained variational inequality problems, without necessitating any information on the optimal Lagrange multipliers.
Balancing a 3D Inverted Pendulum using Remote Magnetic Manipulation
This greatly expands the range of potential medical applications and includes even dynamic environments as encountered in cardiovascular interventions.
Towards a Systems Theory of Algorithms
Traditionally, numerical algorithms are seen as isolated pieces of code confined to an {\em in silico} existence.
Deep Backtracking Counterfactuals for Causally Compliant Explanations
Counterfactuals answer questions of what would have been observed under altered circumstances and can therefore offer valuable insights.
Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators
We study causal effect estimation from a mixture of observational and interventional data in a confounded linear regression model with multivariate treatments.
Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts
Our gray-box approach builds on a physical model.
Adaptive Decision-Making with Constraints and Dependent Losses: Performance Guarantees and Applications to Online and Nonlinear Identification
We consider adaptive decision-making problems where an agent optimizes a cumulative performance objective by repeatedly choosing among a finite set of options.
Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold
We consider the problem of minimizing a non-convex function over a smooth manifold $\mathcal{M}$.
Accelerated First-Order Optimization under Nonlinear Constraints
We exploit analogies between first-order algorithms for constrained optimization and non-smooth dynamical systems to design a new class of accelerated first-order algorithms for constrained optimization.
Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees -- Technical Report
In this work, we represent a recurrent neural network as a linear time-invariant system with nonlinear disturbances.
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization
We obtain tight convergence rates for RR and SO and demonstrate that these strategies lead to faster convergence than uniform sampling.
On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems
We introduce a class of first-order methods for smooth constrained optimization that are based on an analogy to non-smooth dynamical systems.
Optimization with Momentum: Dynamical, Control-Theoretic, and Symplectic Perspectives
We analyze the convergence rate of various momentum-based optimization algorithms from a dynamical systems point of view.
Continuous-time Lower Bounds for Gradient-based Algorithms
This article derives lower bounds on the convergence rate of continuous-time gradient-based optimization algorithms.
Optimization and Control Systems and Control Systems and Control
Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction
In addition to providing high-profile successes in computer vision and natural language processing, neural networks also provide an emerging set of techniques for scientific problems.
A Dynamical Systems Perspective on Nesterov Acceleration
We present a dynamical system framework for understanding Nesterov's accelerated gradient method.
