From 1164628003f5a7603068bc8c70151fa8d994cfe7 Mon Sep 17 00:00:00 2001 From: pancetta <7158893+pancetta@users.noreply.github.com> Date: Thu, 26 Mar 2026 06:43:57 +0000 Subject: [PATCH 1/2] updated pint.bib using bibbot --- _bibliography/pint.bib | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/_bibliography/pint.bib b/_bibliography/pint.bib index 37ac55a7..5929d25e 100644 --- a/_bibliography/pint.bib +++ b/_bibliography/pint.bib @@ -8509,6 +8509,15 @@ @article{AluthgeEtAl2026 year = {2026}, } +@unpublished{AraújoEtAl2026, + abstract = {Standard gradient-based iteration algorithms for optimization, such as gradient descent and its various proximal-based extensions to nonsmooth problems, are known to converge slowly for ill-conditioned problems, sometimes requiring many tens of thousands of iterations in practice. Since these iterations are computed sequentially, they may present a computational bottleneck in large-scale parallel simulations. In this work, we present a "parallel-in-iteration" framework that allows one to parallelize across these iterations using multiple processors with the objective of reducing the wall-clock time needed to solve the underlying optimization problem. Our methodology is based on re-purposing parallel time integration algorithms for time-dependent differential equations, motivated by the fact that optimization algorithms often have interpretations as discretizations of time-dependent differential equations (such as gradient flow). Specifically in this work, we use the parallel-in-time method of multigrid reduction-in-time (MGRIT), but note that our approach permits in principle the use of any other parallel-in-time method. We numerically demonstrate the efficacy of our approach on two different model problems, including a standard convex quadratic problem and the nonsmooth elastic obstacle problem in one and two spatial dimensions. For our model problems, we observe fast MGRIT convergence analogous to its prototypical performance on partial differential equations of diffusion type. Some theory is presented to connect the convergence of MGRIT to the convergence of the underlying optimization algorithm. Theoretically predicted parallel speedup results are also provided.}, + author = {G. H. M. Araújo and O. A. Krzysik and H. De Sterck}, + howpublished = {arXiv:2603.20879v1 [math.NA]}, + title = {Parallel-in-iteration optimization using multigrid reduction-in-time}, + url = {https://arxiv.org/abs/2603.20879v1}, + year = {2026}, +} + @article{BonteEtAl2026, author = {Bonte, Corentin and Bouillon, Arne and Samaey, Giovanni and Meerbergen, Karl}, doi = {10.1016/j.cam.2026.117339}, From cedc5e036365d2ce37a8b89346801fe2029bd83a Mon Sep 17 00:00:00 2001 From: Robert Speck Date: Thu, 26 Mar 2026 07:45:03 +0100 Subject: [PATCH 2/2] Fix author name formatting in bibliography entry --- _bibliography/pint.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_bibliography/pint.bib b/_bibliography/pint.bib index 5929d25e..0605cc57 100644 --- a/_bibliography/pint.bib +++ b/_bibliography/pint.bib @@ -8509,7 +8509,7 @@ @article{AluthgeEtAl2026 year = {2026}, } -@unpublished{AraújoEtAl2026, +@unpublished{AraujoEtAl2026, abstract = {Standard gradient-based iteration algorithms for optimization, such as gradient descent and its various proximal-based extensions to nonsmooth problems, are known to converge slowly for ill-conditioned problems, sometimes requiring many tens of thousands of iterations in practice. Since these iterations are computed sequentially, they may present a computational bottleneck in large-scale parallel simulations. In this work, we present a "parallel-in-iteration" framework that allows one to parallelize across these iterations using multiple processors with the objective of reducing the wall-clock time needed to solve the underlying optimization problem. Our methodology is based on re-purposing parallel time integration algorithms for time-dependent differential equations, motivated by the fact that optimization algorithms often have interpretations as discretizations of time-dependent differential equations (such as gradient flow). Specifically in this work, we use the parallel-in-time method of multigrid reduction-in-time (MGRIT), but note that our approach permits in principle the use of any other parallel-in-time method. We numerically demonstrate the efficacy of our approach on two different model problems, including a standard convex quadratic problem and the nonsmooth elastic obstacle problem in one and two spatial dimensions. For our model problems, we observe fast MGRIT convergence analogous to its prototypical performance on partial differential equations of diffusion type. Some theory is presented to connect the convergence of MGRIT to the convergence of the underlying optimization algorithm. Theoretically predicted parallel speedup results are also provided.}, author = {G. H. M. Araújo and O. A. Krzysik and H. De Sterck}, howpublished = {arXiv:2603.20879v1 [math.NA]},