Webinar - Revision history

Kohn: /* Upcoming Talks */

2022-10-19T16:18:42Z

Upcoming Talks

←Older revision		Revision as of 16:18, 19 October 2022
Line 32:		Line 32:
	== Upcoming Talks ==		== Upcoming Talks ==

-	All seminars take place on the 2nd Tuesday of every month. However, currently we are on summer break until September 2022. <br> Fill out the [https://siam.zoom.us/webinar/register/WN_nMdM3GXHTTuZyjn5fGf4jA SAGA registration] to get a personalized Zoom link for each seminar.	+	All seminars take place on the 2nd Tuesday of every month. However, currently we are on summer break until the end of the year. <br> Fill out the [https://siam.zoom.us/webinar/register/WN_nMdM3GXHTTuZyjn5fGf4jA SAGA registration] to get a personalized Zoom link for each seminar.

	==Past Talks==		==Past Talks==

Kohn: /* Upcoming Talks */

2022-07-07T13:56:53Z

Upcoming Talks

←Older revision		Revision as of 13:56, 7 July 2022
Line 32:		Line 32:
	== Upcoming Talks ==		== Upcoming Talks ==

-	All seminars take place on the 2nd Tuesday of every month. However, currently we are on summer break until September 2022. <br>	+	All seminars take place on the 2nd Tuesday of every month. However, currently we are on summer break until September 2022. <br> Fill out the [https://siam.zoom.us/webinar/register/WN_nMdM3GXHTTuZyjn5fGf4jA SAGA registration] to get a personalized Zoom link for each seminar.
-	Fill out the [https://siam.zoom.us/webinar/register/WN_nMdM3GXHTTuZyjn5fGf4jA SAGA registration] to get a personalized Zoom link for each seminar.	+

	==Past Talks==		==Past Talks==

Kohn: /* Upcoming Talks */

2022-07-07T13:56:42Z

Upcoming Talks

←Older revision		Revision as of 13:56, 7 July 2022
Line 32:		Line 32:
	== Upcoming Talks ==		== Upcoming Talks ==

-	All seminars take place on the 2nd Tuesday of every month. <br> Fill out the [https://siam.zoom.us/webinar/register/WN_nMdM3GXHTTuZyjn5fGf4jA SAGA registration] to get a personalized Zoom link for each seminar.	+	All seminars take place on the 2nd Tuesday of every month. However, currently we are on summer break until September 2022. <br>
-		+	Fill out the [https://siam.zoom.us/webinar/register/WN_nMdM3GXHTTuZyjn5fGf4jA SAGA registration] to get a personalized Zoom link for each seminar.
-		+
-		+
-		+
-		+
-		+

	==Past Talks==		==Past Talks==

Kohn: /* Past Talks */

2022-07-07T13:55:27Z

Past Talks

←Older revision		Revision as of 13:55, 7 July 2022
Line 41:		Line 41:

	==Past Talks==		==Past Talks==
-	These seminars already happened. But you can fill out the [https://siam.zoom.us/webinar/register/WN_nMdM3GXHTTuZyjn5fGf4jA SAGA registration] to get a personalized Zoom link for future seminars.	+	These seminars already happened, but you can re-watch them in the '''[https://www.youtube.com/playlist?list=PLf_ipOSbWC84HloBwtq3vVJKYE4rwfeSs SAGA playlist on SIAM’s YouTube account]'''. <br>
		+	Fill out the [https://siam.zoom.us/webinar/register/WN_nMdM3GXHTTuZyjn5fGf4jA SAGA registration] to get a personalized Zoom link for future seminars!
	* Tuesday, November 10, 2020, 5pm Central European Time (UTC+1): <br> '''Rekha R. Thomas''' (University of Washington) <br> '''''When Two Cameras Meet a Cubic Surface''''' <br> The set of images captured by an arrangement of pinhole cameras is usually modeled by the multiview variety. The true set is in fact a semialgebraic subset of this variety, arising from the physical restriction that cameras can only image points in front of them. For a pair of cameras, the minimal problem in this semialgebraic setting is given by 5 point pairs, which even in general position, can fail to have a "chiral" reconstruction. I will describe how the combinatorics and arithmetic information of this minimal case is carried by a cubic surface with 27 real lines. <br> Joint work with Sameer Agarwal, Andrew Pryhuber and Rainer Sinn.		* Tuesday, November 10, 2020, 5pm Central European Time (UTC+1): <br> '''Rekha R. Thomas''' (University of Washington) <br> '''''When Two Cameras Meet a Cubic Surface''''' <br> The set of images captured by an arrangement of pinhole cameras is usually modeled by the multiview variety. The true set is in fact a semialgebraic subset of this variety, arising from the physical restriction that cameras can only image points in front of them. For a pair of cameras, the minimal problem in this semialgebraic setting is given by 5 point pairs, which even in general position, can fail to have a "chiral" reconstruction. I will describe how the combinatorics and arithmetic information of this minimal case is carried by a cubic surface with 27 real lines. <br> Joint work with Sameer Agarwal, Andrew Pryhuber and Rainer Sinn.

Kohn: /* March 2022 */

2022-07-07T13:52:41Z

March 2022

←Older revision		Revision as of 13:52, 7 July 2022
Line 37:		Line 37:


-
-	=== March 2022===
-	* Tuesday, March 8, 5pm Central European Time (UTC+1): <br> '''Dustin Mixon''' (THE Ohio State) [https://www.timeanddate.com/worldclock/converter.html?iso=20220308T160000&p1=25&p2=270&p3=3999 Time Zones]
-	* ''Packing points in projective spaces''
-	* ''Abstract'': Given a compact metric space, it is natural to ask how to arrange a given number of points so that the minimum distance is maximized. For example, the setting of the 2-sphere prompted a dispute between Newton and Gregory in 1694, and the setting of Hamming space forms the foundation of error correction in modern digital communication. In this talk, we consider the setting of real and complex projective spaces. We briefly discuss applications before surveying the state of the art and identifying key open problems.

Kohn: /* February 2022 */

2022-07-07T13:52:34Z

February 2022

←Older revision		Revision as of 13:52, 7 July 2022
Line 36:		Line 36:


-	=== February 2022===	+
-	* Tuesday, February 8, 5pm Central European Time (UTC+1): <br> '''Heather Harrington''' (Oxford) [https://www.timeanddate.com/worldclock/converter.html?iso=20220208T160000&p1=25&p2=270&p3=3999 Time Zones]	+
-	* ''Algebra and topology for biological systems''	+
-	* ''Abstract'': Signalling pathways in molecular biology can be modeled by polynomial dynamical systems. I will present mathematical models describing two biological systems involved in development and cancer. I will overview approaches to analyse these models with data using computational algebraic geometry, differential algebra and statistics. I will also present how topological data analysis can provide additional information to distinguish wild-type and mutant molecules in one pathway. These case studies showcase how computational algebra, geometry, topology and dynamics can provide new insights to better understand model parameter values as well as biological systems with time course data, specifically how changes at the molecular scale (e.g. molecular mutations) result in kinetic differences that are observed as phenotypic changes (e.g. mutations in fruit fly wings).	+

	=== March 2022===		=== March 2022===

Kohn: /* April 2022 */

2022-07-07T13:52:26Z

April 2022

←Older revision		Revision as of 13:52, 7 July 2022
Line 45:		Line 45:
	* ''Packing points in projective spaces''		* ''Packing points in projective spaces''
	* ''Abstract'': Given a compact metric space, it is natural to ask how to arrange a given number of points so that the minimum distance is maximized. For example, the setting of the 2-sphere prompted a dispute between Newton and Gregory in 1694, and the setting of Hamming space forms the foundation of error correction in modern digital communication. In this talk, we consider the setting of real and complex projective spaces. We briefly discuss applications before surveying the state of the art and identifying key open problems.		* ''Abstract'': Given a compact metric space, it is natural to ask how to arrange a given number of points so that the minimum distance is maximized. For example, the setting of the 2-sphere prompted a dispute between Newton and Gregory in 1694, and the setting of Hamming space forms the foundation of error correction in modern digital communication. In this talk, we consider the setting of real and complex projective spaces. We briefly discuss applications before surveying the state of the art and identifying key open problems.
-
-	=== April 2022===
-	* Tuesday, April 12, 5pm Central European Summer Time:
-	*'''Jesús A. De Loera''' (U.C. Davis) [https://www.timeanddate.com/worldclock/converter.html?iso=20220412T150000&p1=3736&p2=25&p3=270&p4=3999 Time Zones]<br>
-	*''The geometry of the space of ALL pivot rules of a Linear Optimization problem''
-	* Abstract<br> Linear optimization is a simple yet fundamental building block in modern computing (e.g., without linear programs and branch-and-cut methods one would not have solvers for integer optimization). Dantzig's Simplex Method is one of the most popular algorithms for linear optimization. It was even named as one of the top ten algorithms in the 20th century. But despite all its success and popularity for the last 80 years we still do not fully understand it. There are still a number of open questions about its behavior and this talk is about how to choose a pivot rule? <br><br> Purely geometrically, a linear program (LP) is a polyhedron together with an orientation of its graph. A simplex method selects a directed path from an initial vertex to the sink (optimum) and thus determines an arborescence (of shortest monotone paths). The engine of any simplex method is a pivot rule that selects the outgoing arc for a current vertex. Pivot rules come in many forms and types but we are still ignorant whether there is one that can make the simplex method run in polynomial time. Somehow we need ways to grasp the set of all possible pivot rules. In this talk we show this set has a very natural polyhedral structure. <br><br> Classic results in parametric linear optimization explain the changes of objective function, e.g., the orientation and sink and for a pivot rule its associated arborescence. I present a new parametric analysis for all pivot rules belonging to a certain class, memoryless pivot rules. It turns out there exist lovely polytopes, the pivot rule polytopes, that parametrize all memoryless pivot rules of a given LP. This is an attempt to get a geometric/topological picture of the “ space of all pivot rules of an LP”. <br><br> I will present this entertaining story without assuming prior knowledge of linear programming or the simplex method. The story is related to performance of pivot rules, fiber polytopes, parametric linear programs, shadow-vertex-rules, and several classic polytopes make cameo appearances. This is joint work with Alex Black (UC Davis), Niklas Lu ̈tjeharms (U. Frankfurt), and Raman Sanyal (U. Frankfurt).

Kohn: /* May 2022 */

2022-07-07T13:52:19Z

May 2022

←Older revision		Revision as of 13:52, 7 July 2022
Line 51:		Line 51:
	*''The geometry of the space of ALL pivot rules of a Linear Optimization problem''		*''The geometry of the space of ALL pivot rules of a Linear Optimization problem''
	* Abstract<br> Linear optimization is a simple yet fundamental building block in modern computing (e.g., without linear programs and branch-and-cut methods one would not have solvers for integer optimization). Dantzig's Simplex Method is one of the most popular algorithms for linear optimization. It was even named as one of the top ten algorithms in the 20th century. But despite all its success and popularity for the last 80 years we still do not fully understand it. There are still a number of open questions about its behavior and this talk is about how to choose a pivot rule? <br><br> Purely geometrically, a linear program (LP) is a polyhedron together with an orientation of its graph. A simplex method selects a directed path from an initial vertex to the sink (optimum) and thus determines an arborescence (of shortest monotone paths). The engine of any simplex method is a pivot rule that selects the outgoing arc for a current vertex. Pivot rules come in many forms and types but we are still ignorant whether there is one that can make the simplex method run in polynomial time. Somehow we need ways to grasp the set of all possible pivot rules. In this talk we show this set has a very natural polyhedral structure. <br><br> Classic results in parametric linear optimization explain the changes of objective function, e.g., the orientation and sink and for a pivot rule its associated arborescence. I present a new parametric analysis for all pivot rules belonging to a certain class, memoryless pivot rules. It turns out there exist lovely polytopes, the pivot rule polytopes, that parametrize all memoryless pivot rules of a given LP. This is an attempt to get a geometric/topological picture of the “ space of all pivot rules of an LP”. <br><br> I will present this entertaining story without assuming prior knowledge of linear programming or the simplex method. The story is related to performance of pivot rules, fiber polytopes, parametric linear programs, shadow-vertex-rules, and several classic polytopes make cameo appearances. This is joint work with Alex Black (UC Davis), Niklas Lu ̈tjeharms (U. Frankfurt), and Raman Sanyal (U. Frankfurt).		* Abstract<br> Linear optimization is a simple yet fundamental building block in modern computing (e.g., without linear programs and branch-and-cut methods one would not have solvers for integer optimization). Dantzig's Simplex Method is one of the most popular algorithms for linear optimization. It was even named as one of the top ten algorithms in the 20th century. But despite all its success and popularity for the last 80 years we still do not fully understand it. There are still a number of open questions about its behavior and this talk is about how to choose a pivot rule? <br><br> Purely geometrically, a linear program (LP) is a polyhedron together with an orientation of its graph. A simplex method selects a directed path from an initial vertex to the sink (optimum) and thus determines an arborescence (of shortest monotone paths). The engine of any simplex method is a pivot rule that selects the outgoing arc for a current vertex. Pivot rules come in many forms and types but we are still ignorant whether there is one that can make the simplex method run in polynomial time. Somehow we need ways to grasp the set of all possible pivot rules. In this talk we show this set has a very natural polyhedral structure. <br><br> Classic results in parametric linear optimization explain the changes of objective function, e.g., the orientation and sink and for a pivot rule its associated arborescence. I present a new parametric analysis for all pivot rules belonging to a certain class, memoryless pivot rules. It turns out there exist lovely polytopes, the pivot rule polytopes, that parametrize all memoryless pivot rules of a given LP. This is an attempt to get a geometric/topological picture of the “ space of all pivot rules of an LP”. <br><br> I will present this entertaining story without assuming prior knowledge of linear programming or the simplex method. The story is related to performance of pivot rules, fiber polytopes, parametric linear programs, shadow-vertex-rules, and several classic polytopes make cameo appearances. This is joint work with Alex Black (UC Davis), Niklas Lu ̈tjeharms (U. Frankfurt), and Raman Sanyal (U. Frankfurt).
-
-	=== May 2022===
-	* Tuesday, May 10, 5pm Central European Summer Time (UTC+2): <br> '''Gretchen Matthews ''' (Virginia Tech) [https://www.timeanddate.com/worldclock/converter.html?iso=20220510T150000&p1=25&p2=270&p3=3999 Time Zones]
-	*''Multivariate Goppa codes''
-	* Abstract<br> Goppa codes were introduced in 1971 by V. D. Goppa using a univariate polynomial g(x), called a generator polynomial, over a finite field. Properties of the Goppa code are tied to those of the generator polynomial. Goppa codes are utilized in the McEliece cryptosystem and can be viewed from several different perspectives, each giving a window into their capabilities. In this talk, we introduce multivariate Goppa codes, which are defined via a multivariate polynomial and contain, as a particular case, the well-known classical Goppa codes. Various descriptions are given to provide insight into the codes, their parameters, and their duals. Moreover, the multivariate Goppa codes can be used to obtain entanglement-assisted quantum error-correcting codes and to build families of long LCD, self-dual, or self-orthogonal codes. (joint work with Hiram Lopez)

Kohn: /* June 2022 */

2022-07-07T13:52:10Z

June 2022

←Older revision		Revision as of 13:52, 7 July 2022
Line 57:		Line 57:
	* Abstract<br> Goppa codes were introduced in 1971 by V. D. Goppa using a univariate polynomial g(x), called a generator polynomial, over a finite field. Properties of the Goppa code are tied to those of the generator polynomial. Goppa codes are utilized in the McEliece cryptosystem and can be viewed from several different perspectives, each giving a window into their capabilities. In this talk, we introduce multivariate Goppa codes, which are defined via a multivariate polynomial and contain, as a particular case, the well-known classical Goppa codes. Various descriptions are given to provide insight into the codes, their parameters, and their duals. Moreover, the multivariate Goppa codes can be used to obtain entanglement-assisted quantum error-correcting codes and to build families of long LCD, self-dual, or self-orthogonal codes. (joint work with Hiram Lopez)		* Abstract<br> Goppa codes were introduced in 1971 by V. D. Goppa using a univariate polynomial g(x), called a generator polynomial, over a finite field. Properties of the Goppa code are tied to those of the generator polynomial. Goppa codes are utilized in the McEliece cryptosystem and can be viewed from several different perspectives, each giving a window into their capabilities. In this talk, we introduce multivariate Goppa codes, which are defined via a multivariate polynomial and contain, as a particular case, the well-known classical Goppa codes. Various descriptions are given to provide insight into the codes, their parameters, and their duals. Moreover, the multivariate Goppa codes can be used to obtain entanglement-assisted quantum error-correcting codes and to build families of long LCD, self-dual, or self-orthogonal codes. (joint work with Hiram Lopez)

-	=== June 2022===	+
-	* Tuesday, June 14, 5pm Central European Summer Time (UTC+2): <br> '''TBD''' (TBA) [https://www.timeanddate.com/worldclock/converter.html?iso=20220614T150000&p1=25&p2=270&p3=3999 Time Zones]	+

	==Past Talks==		==Past Talks==

Kohn: /* Past Talks */

2022-07-07T13:51:33Z

Past Talks

(Difference between revisions)