Convex optimization Simplified (No equations!)
In plain English, this video shows why convex problems have one global minimum and why non-convex landscapes create tricky local minima(jagged mountains). I use a cookie-baking analogy, then tie it to practical modeling like least squares.
You’ll learn:
Convex vs non-convex intuition (bowl vs bumps)
Why convexity ⇒ consistent, reproducible solutions
How local minima trap optimizers
Where convexity shows up (e.g., squared-error/least squares)
Common Optimization methods used in quant finance:
• Must know Optimization methods & applicati...
.
.
.
Disclaimer:
The views expressed here are my own and do not reflect the opinions or endorsements of my employer or any institutions I am affiliated with.