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1 60

Section

Using Matrix Operations

Press

(T|

store

the

eigenvalues.

Enter

again

the

elements

the

original matrix into

Recall

the

desired eigenvalue

from

matrix

using

|RCL|[E].

Execute

the

eigenvector program

described above.

Repeat steps

4 and 5 for

each eigenvalue.

Optimization

Optimization describes

class

problems

which

the

object

is to

find

the

minimum

maximum value

of a

specified

function.

Often,

the

interest

focused

on the

behavior

of the

function

in a

particular region.

The

following

program uses

the

method

steepest descent

determine

local minimums

maximums

for a

real-valued

function

two or

more variables.

This

method

is an

iterative procedure

that

uses

the

gradient

of the

function

determine successive sample

points. Four input parameters control

the

sampling plan.

For the

function

f(-x.)=f(xl,x2,...,xn)

the

gradient

V/,

defined

df/dx2

V/(x)

df/dxn

The

critical points

of/(x)

are the

solutions

V/(x)

= 0. A

critical

point

may be a

local minimum,

local maximum,

or a

point

that

neither.

The

gradient

of/(x)

evaluated

at a

point

gives

the

direction

steepest

ascent—that

is, the way in

which

should

changed

order

cause

the

most rapid increase

/(x).

The

negative

gradient gives

the

direction

steepest descent.

The

direction

vector

-V/(x)

for

finding

minimum

V/(x)

for

finding

maximum.

Brand	HP
Model	HP-15C
Category	Calculator
Language	English

HP HP-15C Advanced Functions Handbook

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