13 citations · 29 across the 9 of their papers we have counts for
11 papers
A Feasible-Direction-Type Method for Nonconvex Optimization with Infeasible Starts
Danlin Li, Johannes Milz
We develop an infeasible-start extension of the Topkis--Veinott feasible-direction method for nonconvex composite optimization. At each main iteration, a strongly convex local mode…
Optimal Spectral Design with Prior Information
Anton J. Kleywegt, Johannes Milz, Mohit Singh +1
We study a class of spectral design problems in which a prior positive semidefinite information matrix is updated by a sum of rank-one matrices constructed from chosen design vecto…
Sample-Based Consistency in Infinite-Dimensional Conic-Constrained Stochastic Optimization
Caroline Geiersbach, Johannes Milz
This paper is concerned with a class of stochastic optimization problems defined on a Banach space with almost sure conic-type constraints. For this class of problems, we investiga…
Empirical risk minimization for risk-neutral composite optimal control with applications to bang-bang control
Johannes Milz, Daniel Walter
Nonsmooth composite optimization problems under uncertainty are prevalent in various scientific and engineering applications. We consider risk-neutral composite optimal control pro…
Criticality measure-based error estimates for infinite dimensional optimization
Danlin Li, Johannes Milz
Motivated by optimization with differential equations, we consider optimization problems with Hilbert spaces as decision spaces. As a consequence of their infinite dimensionality,…
Consistency of sample-based stationary points for infinite-dimensional stochastic optimization
Johannes Milz
We consider stochastic optimization problems with possibly nonsmooth integrands posed in Banach spaces and approximate these stochastic programs via a sample-based approaches. We e…
Asymptotic Consistency for Nonconvex Risk-Averse Stochastic Optimization with Infinite Dimensional Decision Spaces
Johannes Milz, Thomas M. Surowiec
Optimal values and solutions of empirical approximations of stochastic optimization problems can be viewed as statistical estimators of their true values. From this perspective, it…
Sample Size Estimates for Risk-Neutral Semilinear PDE-Constrained Optimization
Johannes Milz, Michael Ulbrich
The sample average approximation (SAA) approach is applied to risk-neutral optimization problems governed by semilinear elliptic partial differential equations with random inputs.…
Reliable Error Estimates for Optimal Control of Linear Elliptic PDEs with Random Inputs
Johannes Milz
We discretize a risk-neutral optimal control problem governed by a linear elliptic partial differential equation with random inputs using a Monte Carlo sample-based approximation a…
Consistency of Monte Carlo Estimators for Risk-Neutral PDE-Constrained Optimization
Johannes Milz
We apply the sample average approximation (SAA) method to risk-neutral optimization problems governed by nonlinear partial differential equations (PDEs) with random inputs. We anal…
Sample Average Approximations of Strongly Convex Stochastic Programs in Hilbert Spaces
Johannes Milz
We analyze the tail behavior of solutions to sample average approximations (SAAs) of stochastic programs posed in Hilbert spaces. We require that the integrand be strongly convex w…