Numerical Optimization – Details, episodes & analysis

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Numerical Optimization

Numerical Optimization

Typal Academy

Science

Frequency: 1 episode/10d. Total Eps: 2

Spotify for Podcasters
Interviews with experts in various optimization specialties.
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Apple Podcasts

  • 🇺🇸 USA - mathematics

    14/08/2025
    #95
  • 🇺🇸 USA - mathematics

    13/08/2025
    #94
  • 🇺🇸 USA - mathematics

    12/08/2025
    #94
  • 🇺🇸 USA - mathematics

    11/08/2025
    #94
  • 🇺🇸 USA - mathematics

    10/08/2025
    #93
  • 🇺🇸 USA - mathematics

    09/08/2025
    #90
  • 🇺🇸 USA - mathematics

    08/08/2025
    #90
  • 🇺🇸 USA - mathematics

    07/08/2025
    #91
  • 🇺🇸 USA - mathematics

    06/08/2025
    #90
  • 🇺🇸 USA - mathematics

    05/08/2025
    #87

Spotify

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Score global : 43%


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Latest published episodes

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Welcome to Numerical Optimization

vendredi 15 novembre 2024Duration 01:00

Our mission is to inspire the development of new math research aimed at solving real-world problems. We do this by sharing fun stories behind math formulas and the places they show up.

#1 — Stanley Osher

Episode 1

lundi 25 novembre 2024Duration 30:27

Stanley Osher is a mathematician at University of California Los Angeles.

Subscribe for updates and related optimization articles at

https://www.typalacademy.com


Show Notes:

  • Here is the original paper on total variation for denoising.

  • Here is a talk from 2003 where Stan describes and shows images from the attack on the truck driver Reginald Denny during the riots in LA (skip to 11:00 for the story).

  • Here is the paper on the level set method.

  • The company Stan cofounded, Luminescent Technologies, Inc, used the level set method for inverse lithography technology.

  • Here is a paper by Candes, Romberg and Tao on compressed sensing, providing rigorous theory for use of the L1 norm.

  • An example of "thinking continuously rather than discretely" is the analysis of Su, Boyd, and Candes in providing a short and simple proof for Nesterov acceleration in the continuous setting via a continuous ODE (see Theorem 3 in this ⁠paper⁠).


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