Empirically comparing the finite-time performance of simulation-optimization algorithms

2017 ◽  
Author(s):  
Naijia Anna Dong ◽  
David J. Eckman ◽  
Xueqi Zhao ◽  
Shane G. Henderson ◽  
Matthias Poloczek
Keyword(s):  
Finite Time ◽  
Simulation Optimization ◽  
Optimization Algorithms ◽  
Time Performance

On the finite time performance of model predictive control

2014 ◽  
Vol 70 ◽  
pp. 60-68 ◽  
Author(s):  
X. Cai ◽  
Y. Tan ◽  
S.Y. Li ◽  
I. Mareels
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Finite Time ◽  
Time Performance

scholarly journals Active‐Set Newton Methods and Partial Smoothness

2020 ◽  
Author(s):  
Adrian S. Lewis ◽  
Calvin Wylie
Keyword(s):  
Nonlinear Programming ◽  
Variational Inequalities ◽  
Finite Time ◽  
Optimization Algorithms ◽  
Newton Methods ◽  
Active Set ◽  
Local Algorithms ◽  
Partial Smoothness ◽  
Analogous Behavior ◽  
Partly Smooth

Diverse optimization algorithms correctly identify, in finite time, intrinsic constraints that must be active at optimality. Analogous behavior extends beyond optimization to systems involving partly smooth operators, and in particular to variational inequalities over partly smooth sets. As in classical nonlinear programming, such active‐set structure underlies the design of accelerated local algorithms of Newton type. We formalize this idea in broad generality as a simple linearization scheme for two intersecting manifolds.

Practical Nonparametric Sampling Strategies for Quantile-Based Ordinal Optimization

2021 ◽  
Author(s):  
Dongwook Shin ◽  
Mark Broadie ◽  
Assaf Zeevi
Keyword(s):  
Finite Time ◽  
Stochastic Systems ◽  
Probability Distributions ◽  
Sequential Estimation ◽  
Parametric Representation ◽  
Rate Function ◽  
Probability Of Error ◽  
Time Performance ◽  
Large Deviations Theory ◽  
The One

Given a finite number of stochastic systems, the goal of our problem is to dynamically allocate a finite sampling budget to maximize the probability of selecting the “best” system. Systems are encoded with the probability distributions that govern sample observations, which are unknown and only assumed to belong to a broad family of distributions that need not admit any parametric representation. The best system is defined as the one with the highest quantile value. The objective of maximizing the probability of selecting this best system is not analytically tractable. In lieu of that, we use the rate function for the probability of error relying on large deviations theory. Our point of departure is an algorithm that naively combines sequential estimation and myopic optimization. This algorithm is shown to be asymptotically optimal; however, it exhibits poor finite-time performance and does not lead itself to implementation in settings with a large number of systems. To address this, we propose practically implementable variants that retain the asymptotic performance of the former while dramatically improving its finite-time performance.

scholarly journals Finite-Time Performance Guaranteed Event-Triggered Adaptive  Control for  Nonlinear Systems with Unknown Control Direction

2021 ◽  
Author(s):  
Min Wang ◽  
Lixue Wang
Keyword(s):  
Nonlinear Systems ◽  
Finite Time ◽  
Tracking Error ◽  
Tracking Performance ◽  
Performance Function ◽  
Time Performance ◽  
Unknown Control ◽  
Unknown Control Direction ◽  
Control Direction ◽  
Event Triggered

Abstract This paper studies the issue of finite-time performance guaranteed event-triggered (ET) adaptive neural tracking control for strict-feedback nonlinear systems with unknown control direction. A novel finite-time performance function is first constructed to describe the prescribed tracking performance, and then a new lemma is given to show the differentiability and boundedness for the performance function, which is important for the verification of the closed-loop stability. Furthermore, with the help of the error transformation technique, the origin constrained tracking error is transformed into an equivalent unconstrained one. By utilizing the first-order sliding mode differentiator, the issue of ``explosion of complexity'' caused by the backstepping design is adequately addressed. Subsequently, an ingenious adaptive updated law is given to co-design the controller and the ET mechanism by the combination of the Nussbaum-type function, thus effectively handling the influences of the measurement error resulted from the ET mechanism and the challenge of the controller design caused by the unknown control direction. The presented event-triggered control scheme can not only guarantee the prescribed tracking performance, but also alleviate the communication burden simultaneously. Finally, numerical and practical examples are provided to demonstrate the validity of the proposed control strategy.

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