Algorithmic approaches to avoiding bad local minima in nonconvex inconsistent feasibility
Authors:
Thi Lan Dinh,
Wiebke Bennecke,
G. S. Matthijs Jansen,
D. Russell Luke,
Stefan Mathias
Abstract:
We report on the use of algorithms to avoid or move away from ``bad'' local minima in nonconvex optimization. Our study is phenomenological and empirical, focusing on the performance of cyclic projections, the cyclic relaxed Douglas-Rachford algorithm, and relaxed Douglas-Rachford splitting on the product space for orbital tomographic imaging from angle-resolved photon emission spectroscopy (ARPES…
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We report on the use of algorithms to avoid or move away from ``bad'' local minima in nonconvex optimization. Our study is phenomenological and empirical, focusing on the performance of cyclic projections, the cyclic relaxed Douglas-Rachford algorithm, and relaxed Douglas-Rachford splitting on the product space for orbital tomographic imaging from angle-resolved photon emission spectroscopy (ARPES) measurements, with both synthetic and laboratory data. Cyclic projections and Douglas-Rachford on the product space are both well-known methods, but cyclic relaxed Douglas-Rachford was only recently fully characterized in a companion paper to the present study. Only one other study of note has investigated the performance of all three of these algorithms for inconsistent nonconvex feasibility. We show that the relaxed Douglas-Rachford algorithm on the product space, while exhibiting very poor convergence rates, can be used to filter out bad local minima from all cyclic algorithms. Our numerical experiments lead to the following recommendation: run cyclic projections to find some fixed point, and from this fixed point run relaxed Douglas-Rachford algorithm on the product space with as large a relaxation parameter as is numerically stable in order to escape poor local minima. This advice runs counter to the current practice for phase retrieval, where a Douglas-Rachford-type algorithm is run for several iterations, and then cyclic projections is used to ``clean up'' the images.
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Submitted 20 August, 2026; v1 submitted 26 February, 2025;
originally announced February 2025.
A minimalist approach to 3D photoemission orbital tomography: algorithms and data requirements
Authors:
Thi Lan Dinh,
G. S. Matthijs Jansen,
D. Russell Luke,
Wiebke Bennecke,
Stefan Mathias
Abstract:
Photoemission orbital tomography provides direct access from laboratory measurements to the real-space molecular orbitals of well-ordered organic semiconductor layers. Specifically, the application of phase retrieval algorithms to photon-energy- and angle-resolved photoemission data enables the direct reconstruction of full 3D molecular orbitals without the need for simulations using density funct…
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Photoemission orbital tomography provides direct access from laboratory measurements to the real-space molecular orbitals of well-ordered organic semiconductor layers. Specifically, the application of phase retrieval algorithms to photon-energy- and angle-resolved photoemission data enables the direct reconstruction of full 3D molecular orbitals without the need for simulations using density functional theory or the like. A major limitation for the direct approach has been the need for densely-sampled, well-calibrated 3D photoemission patterns. Here, we present an iterative projection algorithm that completely eliminates this challenge: for the benchmark case of the pentacene frontier orbitals, we demonstrate the reconstruction of the full orbital based on a dataset containing only four simulated photoemission momentum measurements. We discuss the algorithm performance, sampling requirements with respect to the photon energy, optimal measurement strategies, and the accuracy of orbital images that can be achieved.
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Submitted 31 January, 2024;
originally announced February 2024.