A new perspective on parameter study of optimization problems

09/23/2022
by   Alen Alexanderian, et al.
0

We provide a new perspective on the study of parameterized optimization problems. Our approach combines methods for post-optimal sensitivity analysis and ordinary differential equations to quantify the uncertainty in the minimizer due to uncertain parameters in the optimization problem. We illustrate the proposed approach with a simple analytic example and an inverse problem governed by an advection diffusion equation.

READ FULL TEXT
research
10/23/2019

Sensitivity-based Heuristic for Guaranteed Global Optimization with Nonlinear Ordinary Differential Equations

We focus on interval algorithms for computing guaranteed enclosures of t...
research
07/12/2020

Optimal Experimental Design for Uncertain Systems Based on Coupled Differential Equations

We consider the optimal experimental design problem for an uncertain Kur...
research
05/15/2022

A comparison of PINN approaches for drift-diffusion equations on metric graphs

In this paper we focus on comparing machine learning approaches for quan...
research
08/26/2022

Improving the Efficiency of Gradient Descent Algorithms Applied to Optimization Problems with Dynamical Constraints

We introduce two block coordinate descent algorithms for solving optimiz...
research
02/25/2021

An Advection-Diffusion based Filter for Machinable Designs in Topology Optimization

This paper introduces a simple formulation for topology optimization pro...
research
05/13/2021

Good and Bad Optimization Models: Insights from Rockafellians

A basic requirement for a mathematical model is often that its solution ...
research
04/22/2020

Tracing locally Pareto optimal points by numerical integration

We suggest a novel approach for the efficient and reliable approximation...

Please sign up or login with your details

Forgot password? Click here to reset