# convex functions

Published articles for convex functions.

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## A Reminder of Lagrange Multipliers for Optimization Problems

DevFeed: [A Reminder of Lagrange Multipliers for Optimization Problems](<https://devfeed.tech/articles/lagrangians-for-the-amnesiac-40334.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2013/11/30/lagrangians-for-the-amnesiac/>)

Published: 2013-11-30T09:00:50Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Optimization](<https://devfeed.tech/topics/optimization.md>), [function](<https://devfeed.tech/topics/function.md>), [Variable](<https://devfeed.tech/topics/variable.md>)

Tags: [convex-functions](<https://devfeed.tech/tags/convex-functions.md>), [function](<https://devfeed.tech/tags/function.md>), [gradient-descent](<https://devfeed.tech/tags/gradient-descent.md>), [lagrange](<https://devfeed.tech/tags/lagrange.md>), [lagrange-multipliers](<https://devfeed.tech/tags/lagrange-multipliers.md>), [linear-algebra](<https://devfeed.tech/tags/linear-algebra.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [problems](<https://devfeed.tech/tags/problems.md>), [variable](<https://devfeed.tech/tags/variable.md>), [vectors](<https://devfeed.tech/tags/vectors.md>)

### AI overview

A tutorial-style reminder of Lagrange multipliers for optimization problems. It reviews gradients, partial derivatives, dot products, and directional change for multivariable functions.

### Source excerpt

For a while I've been meaning to do some more advanced posts on optimization problems of all flavors. One technique that comes up over and over again is Lagrange multipliers, so this post is going to be a leisurely reminder of that technique. I often forget how to do these basic calculus-type things, so it's good practice. We will assume something about the reader's knowledge, but it's a short list: know how to operate with vectors and the dot product, know how to take a partial derivative, and know that in single-variable calculus the local maxima and minima of a differentiable function $ f(x)$ occur when the derivative $ f'(x)$ vanishes.