5 papers
Feasibility-based Fixed Point Networks
Howard Heaton, Samy Wu Fung, Aviv Gibali +1
Inverse problems consist of recovering a signal from a collection of noisy measurements. These problems can often be cast as feasibility problems; however, additional regularizatio…
Learning to Optimize: A Primer and A Benchmark
Tianlong Chen, Xiaohan Chen, Wuyang Chen +4
Learning to optimize (L2O) is an emerging approach that leverages machine learning to develop optimization methods, aiming at reducing the laborious iterations of hand engineering.…
Wasserstein-based Projections with Applications to Inverse Problems
Howard Heaton, Samy Wu Fung, Alex Tong Lin +2
Inverse problems consist of recovering a signal from a collection of noisy measurements. These are typically cast as optimization problems, with classic approaches using a data fid…
Asynchronous Sequential Inertial Iterations for Common Fixed Points Problems with an Application to Linear Systems
Howard Heaton, Yair Censor
The common fixed points problem requires finding a point in the intersection of fixed points sets of a finite collection of operators. Quickly solving problems of this sort is of g…
Derivative-free superiorization with component-wise perturbations
Yair Censor, Howard Heaton, Reinhard Schulte
Superiorization reduces, not necessarily minimizes, the value of a target function while seeking constraints-compatibility. This is done by taking a solely feasibility-seeking algo…