Ben Grimmer
@profgrimmer.bsky.social
Assistant Professor @JohnsHopkinsAMS, Works in Mathematical Optimization,
Mostly here to share pretty maths/3D prints, sometimes sharing my research
Mostly here to share pretty maths/3D prints, sometimes sharing my research
Enjoyed being part of the Brin Mathematical Research Center's summer school on Scientific Machine Learning last week. Many very good talks and always nice to visit UMD!
August 14, 2025 at 8:29 PM
Enjoyed being part of the Brin Mathematical Research Center's summer school on Scientific Machine Learning last week. Many very good talks and always nice to visit UMD!
📢 Excited to share a new paper with PhD student Thabo Samakhoana. Nonsmooth optimization often uses smoothings, nearby smooth functions or sets. Often chosen in an ad hoc fashion.
We do away with ad hoc, characterizing optimal smoothings for convex cones and sublinear functions
We do away with ad hoc, characterizing optimal smoothings for convex cones and sublinear functions
August 12, 2025 at 2:40 PM
📢 Excited to share a new paper with PhD student Thabo Samakhoana. Nonsmooth optimization often uses smoothings, nearby smooth functions or sets. Often chosen in an ad hoc fashion.
We do away with ad hoc, characterizing optimal smoothings for convex cones and sublinear functions
We do away with ad hoc, characterizing optimal smoothings for convex cones and sublinear functions
Reposted by Ben Grimmer
Oh, that’s so satisfying! I stopped at the 4-norm ball thinking I had the solution as it fits the hole like a pot lid (has a perfect circle as an intersection).
August 5, 2025 at 7:36 PM
Oh, that’s so satisfying! I stopped at the 4-norm ball thinking I had the solution as it fits the hole like a pot lid (has a perfect circle as an intersection).
Yesterday I posted a maths puzzle that AIs all failed at (thanks for running the premium versions @xy-han.bsky.social and Ernest Ryu). The puzzle just needs elementary reasoning about p-norm balls (third row on my shelf below).
This thread gives the puzzle, solution, and a 3D printed demo :)
This thread gives the puzzle, solution, and a 3D printed demo :)
August 5, 2025 at 2:37 PM
Yesterday I posted a maths puzzle that AIs all failed at (thanks for running the premium versions @xy-han.bsky.social and Ernest Ryu). The puzzle just needs elementary reasoning about p-norm balls (third row on my shelf below).
This thread gives the puzzle, solution, and a 3D printed demo :)
This thread gives the puzzle, solution, and a 3D printed demo :)
I've invented a simple, lovely math puzzle I expect every AI fails:
Suppose you're a mathematical sailor at sea on a boat that has a perfectly cylindrical hole in the floor. All you brought is a collection of every p norm ball except p=2 (drat!). What do you do to cork the hole and save yourself?
Suppose you're a mathematical sailor at sea on a boat that has a perfectly cylindrical hole in the floor. All you brought is a collection of every p norm ball except p=2 (drat!). What do you do to cork the hole and save yourself?
August 4, 2025 at 11:57 PM
I've invented a simple, lovely math puzzle I expect every AI fails:
Suppose you're a mathematical sailor at sea on a boat that has a perfectly cylindrical hole in the floor. All you brought is a collection of every p norm ball except p=2 (drat!). What do you do to cork the hole and save yourself?
Suppose you're a mathematical sailor at sea on a boat that has a perfectly cylindrical hole in the floor. All you brought is a collection of every p norm ball except p=2 (drat!). What do you do to cork the hole and save yourself?
Reposted by Ben Grimmer
very cool talk:
youtu.be/K_dhTP2I2uo?...
"Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm"
- Jason Altschuler
youtu.be/K_dhTP2I2uo?...
"Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm"
- Jason Altschuler
Jason Altschuler - Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm
YouTube video by Institute for Pure & Applied Mathematics (IPAM)
youtu.be
May 26, 2025 at 3:24 PM
very cool talk:
youtu.be/K_dhTP2I2uo?...
"Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm"
- Jason Altschuler
youtu.be/K_dhTP2I2uo?...
"Near-Linear Runtime for a Classical Matrix Preconditioning Algorithm"
- Jason Altschuler
Lots of great questions and engagement from Wisconsin folk! They were quick at turning around and getting it online. See below:
www.youtube.com/watch?v=QNfq...
www.youtube.com/watch?v=QNfq...
April 30, 2025 at 8:57 PM
Lots of great questions and engagement from Wisconsin folk! They were quick at turning around and getting it online. See below:
www.youtube.com/watch?v=QNfq...
www.youtube.com/watch?v=QNfq...
Just landed in Madison! Tomorrow, I'll be sharing my work optimizing optimization methods, to and beyond minimax optimality in their SILO seminar.
Will share a link to the talk on YouTube after
Will share a link to the talk on YouTube after
April 30, 2025 at 3:33 AM
Just landed in Madison! Tomorrow, I'll be sharing my work optimizing optimization methods, to and beyond minimax optimality in their SILO seminar.
Will share a link to the talk on YouTube after
Will share a link to the talk on YouTube after
Reposted by Ben Grimmer
The optimization technique of gradient descent is like feeling your way down a mountain in the dark. You may not be able to see the way, but you’ll eventually reach the lowest point in the area. (From the archive)
www.quantamagazine.org/risky-giant-...
www.quantamagazine.org/risky-giant-...
March 29, 2025 at 8:04 PM
The optimization technique of gradient descent is like feeling your way down a mountain in the dark. You may not be able to see the way, but you’ll eventually reach the lowest point in the area. (From the archive)
www.quantamagazine.org/risky-giant-...
www.quantamagazine.org/risky-giant-...
My PhD students are awesome. They gave my fiancee(wife) and I this gorgeous cherry blossom card for our wedding and soon honeymoon in Japan <3
March 15, 2025 at 12:54 PM
My PhD students are awesome. They gave my fiancee(wife) and I this gorgeous cherry blossom card for our wedding and soon honeymoon in Japan <3
As an early wedding present (happening this Saturday!), my dad made me a custom shelf to hold my collection of unit norm balls!
Rockafellar+Wets's thick textbook is included for reference.
Rockafellar+Wets's thick textbook is included for reference.
March 12, 2025 at 7:52 PM
As an early wedding present (happening this Saturday!), my dad made me a custom shelf to hold my collection of unit norm balls!
Rockafellar+Wets's thick textbook is included for reference.
Rockafellar+Wets's thick textbook is included for reference.
New (first) paper with my student Aaron Zoll :)
We consider first-order methods for a ridiculously general model: minimizing a convex composition of functions g_j(x) that vary heterogeneously in whether they are smooth, nonsmooth, convex, strongly convex or anything in between.
We consider first-order methods for a ridiculously general model: minimizing a convex composition of functions g_j(x) that vary heterogeneously in whether they are smooth, nonsmooth, convex, strongly convex or anything in between.
March 11, 2025 at 2:00 PM
New (first) paper with my student Aaron Zoll :)
We consider first-order methods for a ridiculously general model: minimizing a convex composition of functions g_j(x) that vary heterogeneously in whether they are smooth, nonsmooth, convex, strongly convex or anything in between.
We consider first-order methods for a ridiculously general model: minimizing a convex composition of functions g_j(x) that vary heterogeneously in whether they are smooth, nonsmooth, convex, strongly convex or anything in between.
Reposted by Ben Grimmer
🎉Congrats to the 126 early-career scientists who have been awarded a Sloan Research Fellowship this year! These exceptional scholars are drawn from 51 institutions across the US and Canada, and represent the next generation of groundbreaking researchers. sloan.org/fellowships/...
February 18, 2025 at 3:14 PM
🎉Congrats to the 126 early-career scientists who have been awarded a Sloan Research Fellowship this year! These exceptional scholars are drawn from 51 institutions across the US and Canada, and represent the next generation of groundbreaking researchers. sloan.org/fellowships/...
PhD students set up arts and crafts to make Valentine's mailboxes and collect cards. They (slide) rule :)
February 15, 2025 at 9:01 PM
PhD students set up arts and crafts to make Valentine's mailboxes and collect cards. They (slide) rule :)
Newest office addition might be the biggest computer in my department! (Assuming compute is measured by length)
January 29, 2025 at 12:17 PM
Newest office addition might be the biggest computer in my department! (Assuming compute is measured by length)
Reposted by Ben Grimmer
Postdoctoral position at @cornelluniversity.bsky.social, "from all areas of research that advance the state of the art in data science and the health sciences, extending the reach of data-driven research into novel medical application domains"
Happy to chat!
academicjobsonline.org/ajo/jobs/29529
Happy to chat!
academicjobsonline.org/ajo/jobs/29529
Cornell University, Center for Data Science for Enterprise and Society
Full service online faculty recruitment and application management system for academic institutions worldwide. We offer unique solutions tailored for academic communities.
academicjobsonline.org
January 25, 2025 at 1:21 PM
Postdoctoral position at @cornelluniversity.bsky.social, "from all areas of research that advance the state of the art in data science and the health sciences, extending the reach of data-driven research into novel medical application domains"
Happy to chat!
academicjobsonline.org/ajo/jobs/29529
Happy to chat!
academicjobsonline.org/ajo/jobs/29529
NeurIPS has released recordings of talks!
I had the privilege to speak in the OPT-ML Workshop: neurips.cc/virtual/2024...
My talk presents a solution to the ''minimization game'' for smooth convex problems (ie a subgame perfect method that optimally adapts to any gradients seen)
I had the privilege to speak in the OPT-ML Workshop: neurips.cc/virtual/2024...
My talk presents a solution to the ''minimization game'' for smooth convex problems (ie a subgame perfect method that optimally adapts to any gradients seen)
Optimization for ML WorkshopNeurIPS 2024
neurips.cc
January 16, 2025 at 9:26 PM
NeurIPS has released recordings of talks!
I had the privilege to speak in the OPT-ML Workshop: neurips.cc/virtual/2024...
My talk presents a solution to the ''minimization game'' for smooth convex problems (ie a subgame perfect method that optimally adapts to any gradients seen)
I had the privilege to speak in the OPT-ML Workshop: neurips.cc/virtual/2024...
My talk presents a solution to the ''minimization game'' for smooth convex problems (ie a subgame perfect method that optimally adapts to any gradients seen)
Proud Advisor Moment: One of my first advisees, Danlin Li, now a PhD at Georgia Tech, just had our paper extending her Masters thesis accepted to Math Programming!
She gives a novel primal-dual way to understand the classic (primal) subgradient method
Check it out: arxiv.org/abs/2305.17323
She gives a novel primal-dual way to understand the classic (primal) subgradient method
Check it out: arxiv.org/abs/2305.17323
Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization
We consider (stochastic) subgradient methods for strongly convex but potentially nonsmooth non-Lipschitz optimization. We provide new equivalent dual descriptions (in the style of dual averaging) for ...
arxiv.org
January 16, 2025 at 2:48 PM
Proud Advisor Moment: One of my first advisees, Danlin Li, now a PhD at Georgia Tech, just had our paper extending her Masters thesis accepted to Math Programming!
She gives a novel primal-dual way to understand the classic (primal) subgradient method
Check it out: arxiv.org/abs/2305.17323
She gives a novel primal-dual way to understand the classic (primal) subgradient method
Check it out: arxiv.org/abs/2305.17323
Continuing to use January's freedom, some exposition on OWL Norms:
Their unit balls are all Catalan solids (every face is the same). So the dual balls are all Archimedean solids (every corner is the same)
www.ams.jhu.edu/~grimmer/OWL...
Files to make your own: www.printables.com/model/113805...
Their unit balls are all Catalan solids (every face is the same). So the dual balls are all Archimedean solids (every corner is the same)
www.ams.jhu.edu/~grimmer/OWL...
Files to make your own: www.printables.com/model/113805...
January 8, 2025 at 1:57 PM
Continuing to use January's freedom, some exposition on OWL Norms:
Their unit balls are all Catalan solids (every face is the same). So the dual balls are all Archimedean solids (every corner is the same)
www.ams.jhu.edu/~grimmer/OWL...
Files to make your own: www.printables.com/model/113805...
Their unit balls are all Catalan solids (every face is the same). So the dual balls are all Archimedean solids (every corner is the same)
www.ams.jhu.edu/~grimmer/OWL...
Files to make your own: www.printables.com/model/113805...
January has given me some great free time :)
First link gives some exposition on how this binary slide rule works.
Second link gives the design files if you want one. Many libraries have 3D printers to make this for you nowadays
www.ams.jhu.edu/~grimmer/Sli...
www.printables.com/model/113795...
First link gives some exposition on how this binary slide rule works.
Second link gives the design files if you want one. Many libraries have 3D printers to make this for you nowadays
www.ams.jhu.edu/~grimmer/Sli...
www.printables.com/model/113795...
January 6, 2025 at 10:51 PM
January has given me some great free time :)
First link gives some exposition on how this binary slide rule works.
Second link gives the design files if you want one. Many libraries have 3D printers to make this for you nowadays
www.ams.jhu.edu/~grimmer/Sli...
www.printables.com/model/113795...
First link gives some exposition on how this binary slide rule works.
Second link gives the design files if you want one. Many libraries have 3D printers to make this for you nowadays
www.ams.jhu.edu/~grimmer/Sli...
www.printables.com/model/113795...
The way computers store real numbers (floating point) is exactly the same as how our grandparents did math mechanically, slide rules and log scales.
I'm mass producing binary slide rules to give students on day one of my "Intro to Computational Math" this Spring :)
I'm mass producing binary slide rules to give students on day one of my "Intro to Computational Math" this Spring :)
December 29, 2024 at 9:04 PM
The way computers store real numbers (floating point) is exactly the same as how our grandparents did math mechanically, slide rules and log scales.
I'm mass producing binary slide rules to give students on day one of my "Intro to Computational Math" this Spring :)
I'm mass producing binary slide rules to give students on day one of my "Intro to Computational Math" this Spring :)
Yue Wu (a great PhD student) posted his first paper with me to arxiv, on stochastic nonconvex nonsmooth optimization.
We give unified analysis for variance-reduced prox-linear methods, identifying a neat Pareto frontier of state-of-the-art methods. Check it out:
arxiv.org/abs/2412.15008
We give unified analysis for variance-reduced prox-linear methods, identifying a neat Pareto frontier of state-of-the-art methods. Check it out:
arxiv.org/abs/2412.15008
Some Unified Theory for Variance Reduced Prox-Linear Methods
This work considers the nonconvex, nonsmooth problem of minimizing a composite objective of the form $f(g(x))+h(x)$ where the inner mapping $g$ is a smooth finite summation or expectation amenable to ...
arxiv.org
December 20, 2024 at 5:16 PM
Yue Wu (a great PhD student) posted his first paper with me to arxiv, on stochastic nonconvex nonsmooth optimization.
We give unified analysis for variance-reduced prox-linear methods, identifying a neat Pareto frontier of state-of-the-art methods. Check it out:
arxiv.org/abs/2412.15008
We give unified analysis for variance-reduced prox-linear methods, identifying a neat Pareto frontier of state-of-the-art methods. Check it out:
arxiv.org/abs/2412.15008
📢 #NeurIPS2024 Folk, if you like minimax optimal gradient methods, set your alarms for tomorrow morning: 9am in the West Ballroom A
I'll be giving a plenary at the OPT-ML workshop on optimizing GD steps and subgame perfect methods, all developed with Alex L Wang and Kevin Shu
I'll be giving a plenary at the OPT-ML workshop on optimizing GD steps and subgame perfect methods, all developed with Alex L Wang and Kevin Shu
December 14, 2024 at 9:35 PM
📢 #NeurIPS2024 Folk, if you like minimax optimal gradient methods, set your alarms for tomorrow morning: 9am in the West Ballroom A
I'll be giving a plenary at the OPT-ML workshop on optimizing GD steps and subgame perfect methods, all developed with Alex L Wang and Kevin Shu
I'll be giving a plenary at the OPT-ML workshop on optimizing GD steps and subgame perfect methods, all developed with Alex L Wang and Kevin Shu
My collaborator Alex L Wang wrote a nice blog post about the "Performance Estimation" view of Nesterov's Accelerated Gradient Method.
I think it's a nice read (glad Bluesky doesn't suppress posts for having a link): web.ics.purdue.edu/~wang5984/bl...
I think it's a nice read (glad Bluesky doesn't suppress posts for having a link): web.ics.purdue.edu/~wang5984/bl...
Alex L. Wang
– What is momentum? – A PEP view
web.ics.purdue.edu
December 11, 2024 at 2:28 PM
My collaborator Alex L Wang wrote a nice blog post about the "Performance Estimation" view of Nesterov's Accelerated Gradient Method.
I think it's a nice read (glad Bluesky doesn't suppress posts for having a link): web.ics.purdue.edu/~wang5984/bl...
I think it's a nice read (glad Bluesky doesn't suppress posts for having a link): web.ics.purdue.edu/~wang5984/bl...
New work out with Alex L Wang and Kevin Shu going beyond minimax optimal gradient method design!
Kim and Fessler designed an optimal method (OGM), with the best worst-case over all smooth convex problems. Alas, on easier problems, it may be slow, its worst case occurs on x^2!
Kim and Fessler designed an optimal method (OGM), with the best worst-case over all smooth convex problems. Alas, on easier problems, it may be slow, its worst case occurs on x^2!
December 10, 2024 at 3:02 PM
New work out with Alex L Wang and Kevin Shu going beyond minimax optimal gradient method design!
Kim and Fessler designed an optimal method (OGM), with the best worst-case over all smooth convex problems. Alas, on easier problems, it may be slow, its worst case occurs on x^2!
Kim and Fessler designed an optimal method (OGM), with the best worst-case over all smooth convex problems. Alas, on easier problems, it may be slow, its worst case occurs on x^2!