A little about the essay

In the process we will also take a look at a normal line to a surface. When we introduced the gradient vector in the section on directional derivatives we gave the following fact. Actually, all we need here is the last part of this fact. This says that the gradient vector is always orthogonal, or normal , to the surface at a point. This is a much more general form of the equation of a tangent plane than the one that we derived in the previous section. Note however, that we can also get the equation from the previous section using this more general formula.

## Conjugate gradient method

## ALAFF Practical Conjugate Gradient Method algorithm

Concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems.

### Homework 2: Parallel Sparse Conjugate Gradients

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