Advanced Topic 37 of 51 Math for Computing
Lagrange Multipliers
Optimizing subject to equality constraints.
How it works
To optimize f subject to g(x)=0, solve ∇f = λ∇g; λ is the sensitivity of the optimum to the constraint; KKT conditions extend this to inequalities.
Where it is used
Constrained optimization, SVM duality.
Common pitfall
Critical points may be maxima, minima or saddles.
Where it fits in the course
Lagrange Multipliers is topic 37 of 51 in Math for Computing, rated Advanced. It follows Convex Optimization. The next topic is Linear Programming.
Practice and explore
- Open the interactive lesson, with a mini quiz.
- Take the Math for Computing quiz.
How to study this topic
- Read the explanation above and restate the definition in your own words.
- Open the interactive lesson and answer its mini quiz.
- Read the common pitfall, then write a short example that avoids it.
- Check your understanding in the Math for Computing quiz, and revisit the topic if you miss a question.
Frequently asked questions
What is Lagrange Multipliers?
Optimizing subject to equality constraints. It belongs to Math for Computing and is an advanced-level topic.
Where do people use Lagrange Multipliers?
Constrained optimization, SVM duality.
What is the most common mistake with Lagrange Multipliers?
Critical points may be maxima, minima or saddles.
What should I learn before this topic?
Start with Convex Optimization, the topic just before it in Math for Computing.
How can I practice it?
Answer the mini quiz in the interactive lesson, then take the Math for Computing quiz.
Is Lagrange Multipliers hard to learn?
It is an Advanced topic. Expect to read the explanation more than once and to connect it with earlier topics. Working through a small example by hand helps most.
How long does it take to learn Lagrange Multipliers?
Most learners can read the explanation and finish the interactive lesson in under an hour. Fluency comes from applying the idea, so plan a few short practice sessions with the quiz.
Which topics are related to Lagrange Multipliers?
Convex Optimization, Linear Programming, Optimization are closely connected. The full list is in the related topics section of this page.
What should I study after Lagrange Multipliers?
Continue with Linear Programming, the next topic in Math for Computing.
Related topics
- Convex Optimization (Math for Computing)
- Linear Programming (Math for Computing)
- Optimization (Math for Computing)
- Simplex Method (Math for Computing)