Mastering Gradient Descent: A Practical Guide to Numerical Optimization

This article explains why algebraic methods often fail to locate critical points in real‑world problems and introduces the steepest descent (gradient descent) algorithm as a simple iterative numerical technique, detailing its initialization, update rules, step‑size considerations, and a concrete example.

critical pointsgradient descentnumerical optimization

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Mastering Gradient Descent: A Practical Guide to Numerical Optimization

Model Perspective

May 20, 2022 · Fundamentals

How to Find Global and Local Optima Using Derivatives: A Step-by-Step Guide

This article explains how derivatives can be used to locate global and local minima or maxima, defines critical points—including saddle points—and demonstrates the process with quadratic and cubic function examples, illustrating each concept with clear diagrams.

Derivativescalculuscritical points

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How to Find Global and Local Optima Using Derivatives: A Step-by-Step Guide

Model Perspective

May 11, 2022 · Fundamentals

How Derivatives Find Global and Local Optima (Minima & Maxima)

This article explains how to use derivatives to locate a function’s global and local minimum or maximum points, defines critical points, distinguishes between global and local extrema, and demonstrates the process with quadratic and cubic examples illustrated by diagrams.

Derivativescalculuscritical points

0 likes · 4 min read

How Derivatives Find Global and Local Optima (Minima & Maxima)