Introduction to Optimization

  • by Danil
  • Course level: Beginner


This session introduces the theory of optimization and demonstrates how it can be used to automate performance-driven design workflows. Participants will gain an intuitive understanding of several foundational optimization algorithms including Gradient Descent, Simulated Annealing, and the Genetic Algorithm. They will also get a hands-on introduction to Discover, an open-source optimization framework designed for Grasshopper.

About the instructors

Danil Nagy is a designer, developer, and entrepreneur focusing on applications of computational design and automation for the building industries. His expertise includes computational geometry, digital fabrication, simulation, optimization, machine learning, and data visualization. Danil teaches at the Graduate School of Architecture, Planning and Preservation (GSAPP) at Columbia University in New York, where his courses focus on architectural visualization, generative design, and applications of artificial intelligence. Danil was formerly a Principal Research Scientist at Autodesk Research. He is the founder of Colidescope, a consultancy focused on bringing digital transformation tools to the Architecture, Engineering, and Construction (AEC) industries.
4.67 (3 ratings)

16 Courses

147 students

Material Includes

  • Over two hours of on-demand video
  • Downloadable demo files to follow along with video tutorials