Computational Design of Deployable Structures

Related lectures (31)

Page 3 of 4

Covers methods for solving nonlinear optimization problems, including direct search, Newton-Raphson, and branch and bound.

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Covers recursion, dynamic programming, and algorithm design using divide and conquer strategies.

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Delves into applying the GAMP algorithm to simplify the lasso problem and analyze optimization challenges in neural networks.

Covers line search, Newton's method, BFGS, and conjugate gradient in nonlinear optimization.

Explores the Extra-Gradient Method for primal-dual optimization in min-max problems and its convergence properties.

Explains the properties of the shortest path algorithm and how to find the shortest path in a network.

Explores implicit bias, gradient descent, stability in optimization algorithms, and generalization bounds in machine learning.

Explores the role of computation in data mathematics, focusing on iterative methods, optimization, estimators, and descent principles.