Convex optimization mcgill

  • Convexity plays a vital role in the design of optimization algorithms.
    This is largely due to the fact that it is much easier to analyze and test algorithms in such a context.
  • Key Takeaways.
    Convexity is used to measure a portfolio's exposure to market risk.
    Convexity is the curvature in the relationship between bond prices and bond yields.
    Convexity demonstrates how the duration of a bond changes as the interest rate changes.

What are the best books on convex analysis?

Journal of Convex Analysis 2(1–2), 1995, pp

173–183 S

Lewis:Convex analysis on the hermitian Matrices SIAM Journal on Optimimization 6(1), 1996, pp

164–177 T

Pennanen: Graph-Convex Mappings and K-Convex Functions

Journal of Convex Analysis 6(2), 1999, pp

235–266

Categories

Convex optimization mathematica
Convex optimization matrix positive semidefinite
Convex optimization mooc
Convex optimization medium
Convex optimization midterm
Convex optimization mosek
Convex optimization monotone operators
Convex optimization matlab example
Convex optimization manual
Convex optimization nptel
Convex optimization notes
Convex optimization nus
Convex optimization nesterov
Convex optimization neural network
Convex optimization newton method
Convex optimization nyu
Convex optimization nonlinear
Convex optimization nonconvex function
Convex optimization number of solutions
Convex optimization nonlinear least squares