On the solution of convex QPQC problems with elliptic and other separable constraints with strong curvature. - Jirí Bouchala; Zdenek Dostál; Tomás Kozubek; Lukás Pospísil; Petr Vodstrcil (Appl. Math. Comput., 2014)

Solving Contact Mechanics Problems with PERMON. - Vaclav Hapla; David Horák; Lukás Pospísil; Martin Cermák; Alena Vasatová; Radim Sojka (HPCSE, 2015)

Computation of Kamada-Kawai Algorithm Using Barzilai-Borwein Method. - Lukás Pospísil; Martin Hasal; Jana Nowaková; Jan Platos (INCoS, 2015)

Minimizing quadratic functions with semidefinite Hessian subject to bound constraints. - Zdenek Dostál; Lukás Pospísil (Comput. Math. Appl., 2015)

Optimal iterative QP and QPQC algorithms. - Zdenek Dostál; Lukás Pospísil (Ann. Oper. Res., 2016)

Conjugate gradients for symmetric positive semidefinite least-squares problems. - Zdenek Dostál; Lukás Pospísil (Int. J. Comput. Math., 2018)

The projected Barzilai-Borwein method with fall-back for strictly convex QCQP problems with separable constraints. - Lukás Pospísil; Zdenek Dostál (Math. Comput. Simul., 2018)

Active set expansion strategies in MPRGP algorithm. - Jakub Kruzík; David Horák; Martin Cermák; Lukás Pospísil; Marek Pecha (Adv. Eng. Softw., 2020)

Co-Inference of Data Mislabelings Reveals Improved Models in Genomics and Breast Cancer Diagnostics. - Susanne Gerber; Lukás Pospísil; Stanislav Sys; Charlotte Hewel; Ali Torkamani; Illia Horenko (Frontiers Artif. Intell., 2021)

Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography. - Illia Horenko; Lukás Pospísil; Edoardo Vecchi; Steffen Albrecht; Alexander Gerber; Beate Rehbock; Albrecht Stroh; Susanne Gerber (J. Imaging, 2022)

eSPA+: Scalable Entropy-Optimal Machine Learning Classification for Small Data Problems. - Edoardo Vecchi; Lukás Pospísil; Steffen Albrecht; Terence J. O'Kane; Illia Horenko (Neural Comput., 2022)

Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction. - Illia Horenko; Lukás Pospísil (CoRR, 2023)

Gauge-optimal approximate learning for small data classification problems. - Edoardo Vecchi; Davide Bassetti; Fabio Graziato; Lukás Pospísil; Illia Horenko (CoRR, 2023)

Gauge-Optimal Approximate Learning for Small Data Classification. - Edoardo Vecchi; Davide Bassetti; Fabio Graziato; Lukás Pospísil; Illia Horenko (Neural Comput., 2024)

On Entropic Learning from Noisy Time Series in the Small Data Regime. - Davide Bassetti; Lukás Pospísil; Illia Horenko (Entropy, 2024)

Fast and close Shannon entropy approximation. - Illia Horenko; Davide Bassetti; Lukás Pospísil (CoRR, 2025)

An entropy-optimal path to humble AI. - Davide Bassetti; Lukás Pospísil; Michael Groom; Terence J. O'Kane; Illia Horenko (CoRR, 2025)