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Found 14 papers, 2 papers with code
Model Predictive Planning: Towards Real-Time Multi-Trajectory Planning Around Obstacles
This paper presents a motion planning scheme we call Model Predictive Planning (MPP), designed to optimize trajectories through obstacle-laden environments.
Guaranteed Stability Margins for Decentralized Linear Quadratic Regulators
We show that when sub-controller input costs are decoupled (but there is possible coupling between sub-controller state costs), the decentralized LQR compensator enjoys similar guaranteed stability margins to classical LQR.
Information-theoretic multi-time-scale partially observable systems with inspiration from leukemia treatment
We study a partially observable nonlinear stochastic system with unknown parameters, where the given time scales of the states and measurements may be distinct.
Toward a Scalable Upper Bound for a CVaR-LQ Problem
We study a linear-quadratic, optimal control problem on a discrete, finite time horizon with distributional ambiguity, in which the cost is assessed via Conditional Value-at-Risk (CVaR).
Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization
Smooth minimax games often proceed by simultaneous or alternating gradient updates.
A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints
The theory of integral quadratic constraints (IQCs) allows the certification of exponential convergence of interconnected systems containing nonlinear or uncertain elements.
Online Data Poisoning Attacks
We study data poisoning attacks in the online learning setting, where training data arrive sequentially, and the attacker is eavesdropping the data stream and has the ability to contaminate the current data point to affect the online learning process.
Online Data Poisoning Attack
We study data poisoning attacks in the online setting where training items arrive sequentially, and the attacker may perturb the current item to manipulate online learning.
An Optimal Control Approach to Sequential Machine Teaching
Our key insight is to formulate sequential machine teaching as a time-optimal control problem.
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Our combination of perspectives leads to a better understanding of accelerated variance-reduced stochastic methods for finite-sum problems.
Analysis of Biased Stochastic Gradient Descent Using Sequential Semidefinite Programs
We present a convergence rate analysis for biased stochastic gradient descent (SGD), where individual gradient updates are corrupted by computation errors.
A Robust Accelerated Optimization Algorithm for Strongly Convex Functions
This work proposes an accelerated first-order algorithm we call the Robust Momentum Method for optimizing smooth strongly convex functions.
Optimization and Control Systems and Control
Enhancing human color vision by breaking binocular redundancy
To see color, the human visual system combines the response of three types of cone cells in the retina--a compressive process that discards a significant amount of spectral information.
A General Analysis of the Convergence of ADMM
We provide a new proof of the linear convergence of the alternating direction method of multipliers (ADMM) when one of the objective terms is strongly convex.
Optimization and Control Numerical Analysis
