Events
Department of Mathematics and Statistics
Texas Tech University
 | Wednesday
Apr. 22
|
| Algebra and Number Theory
No Seminar
|
Abstract: In this paper, a new semi-discrete version of the Carleman estimate-based convexification globally convergent numerical method is developed. It is used to provide a starting point for the training procedure of deep learning. An important feature of the continuous version of the convexification method is that its convergence to the true solution is independent of the availability of a good first guess. A new concept of hh-strong convexity is introduced, where hh is the grid step size in the semi-discrete version of the convexification method. The hh-strong convexity enables an {a priori} accuracy estimate of the starting point for the deep learning training step. This approach is demonstrated for a highly nonlinear problem of Electrical Impedance Tomography. Results of numerical experiments for complicated media structures demonstrate the computational feasibility of this procedure.
This talk is co-sponsored with the Analysis seminar group.
When: 4:00 pm (Lubbock's local time is GMT -5)
Where: room Math 011 (Math Basement)
ZOOM details:
- Choice #1: use this
Direct Link that embeds meeting and ID and passcode.
- Choice #2: Log into zoom, then join by manually entering the meeting ID and passcode ...
* Meeting ID: 949 9288 2213
* Passcode: Applied
TTU Math Circle Spring Flyer 6:30-7:30 PM Thursdays in the basement of Math, room 010
