optimization

What is Optimization?

Optimization is an iterative process by which a desired solution

(max/min) of the problem can be found while satisfying all its

constraint or bounded conditions.

Optimization problem could be linear or non-linear.

Non –linear optimization is accomplished by numerical ‘Search

Methods’.

Search methods are used iteratively before a solution is achieved.

The search procedure is termed as algorithm.

Figure 2: Optimum solution is found

while satisfying its constraint (derivative

must be zero at optimum).

Linear problem – solved by Simplex or Graphical methods.

The solution of the linear problem lies on boundaries of the feasible

region.

Non-linear problem solution lies within and on the boundaries of the

feasible region.

Figure 3: Solution of linear problem Figure 4: Three dimensional solution of

non-linear problem

What is Optimization?(Cont.)

Constraints

• Inequality

• Equality

Fundamentals of Non-Linear Optimization

Single Objective function f(x)

• Maximization

• Minimization

Design Variables, xi , i=0,1,2,3…..

Figure 5: Example of design variables and

constraints used in non-linear optimization.

Maximize X1 + 1.5 X2

Subject to:

X1 + X2 ≤ 150

0.25 X1 + 0.5 X2 ≤ 50

X1 ≥ 50

X2 ≥ 25

X1 ≥0, X2 ≥0

Optimal points

• Local minima/maxima points: A point or Solution x* is at local point

if there is no other x in its Neighborhood less than x*

• Global minima/maxima points: A point or Solution x** is at global

point if there is no other x in entire search space less than x**

Figure 6: Global versus local optimization. Figure 7: Local point is equal to global point if

the function is convex.

Fundamentals of Non-Linear Optimization (Cont.)

Function f is convex if f(Xa) is less than value of the corresponding

point joining f(X1) and f(X2).

Convexity condition – Hessian 2nd order derivative) matrix of

function f must be positive semi definite ( eigen values +ve or zero).

Fundamentals of Non-Linear Optimization (Cont.)

Figure 8: Convex and nonconvex set Figure 9: Convex function

Mathematical Background

Slop or gradient of the objective function f – represent the

direction in which the function will decrease/increase most rapidly

x

f

x

xfxxf

dx

df

xx 00lim

)()(lim

.......)(!2

1)()( 2

2

2

xdx

fdx

dx

dfxxf

pp xx

p

z

g

y

g

x

g

z

f

y

f

x

f

J

Taylor series expansion

Jacobian – matrix of gradient of f with respect to several variables

Hessian – Second derivative of f of several variables

Second order condition (SOC)

• Eigen values of H(X*) are all positive

• Determinants of all lower order of H(X*) are +ve

2

22

2

2

2

y

f

yx

f

xy

f

x

f

H

First order Condition (FOC)

0*)(Xf

Mathematical Background (Cont.)

Optimization Algorithm

Deterministic - specific rules to move from one iteration to next ,

gradient, Hessian

Stochastic – probalistic rules are used for subsequent iteration

Optimal Design – Engineering Design based on

optimization algorithm

Lagrangian method – sum of objective function and linear

combination of the constraints.

Multivariable Techniques ( Make use of Single variable Techniques

specially Golden Section)

Optimization Methods

Deterministic

• Direct Search – Use Objective function values to locate minimum

• Gradient Based – first or second order of objective function.

• Minimization objective function f(x) is used with –ve sign –

f(x) for maximization problem.

Single Variable

• Newton – Raphson is Gradient based technique (FOC)

• Golden Search – step size reducing iterative method

• Unconstrained Optimizationa.) Powell Method – Quadratic (degree 2) objective function polynomial is

non-gradient based.

b.) Gradient Based – Steepest Descent (FOC) or Least Square minimum

(LMS)

c.) Hessian Based -Conjugate Gradient (FOC) and BFGS (SOC)

• Constrained Optimizationa.) Indirect approach – by transforming into unconstrained

problem.

b.) Exterior Penalty Function (EPF) and Augmented Lagrange

Multiplier

c.) Direct Method Sequential Linear Programming (SLP), SQP and

Steepest Generalized Reduced Gradient Method (GRG)

Figure 10: Descent Gradient or LMS

Optimization Methods …Constrained

Global Optimization – Stochastic techniques

• Simulated Annealing (SA) method – minimum

energy principle of cooling metal crystalline structure

• Genetic Algorithm (GA) – Survival of the fittest

principle based upon evolutionary theory

Optimization Methods (Cont.)

Optimization Methods (Example)

Multivariable Gradient based optimization

J is the cost function to be minimized in two

dimension

The contours of the J paraboloid shrinks as it is

decrease

function retval = Example6_1(x)

% example 6.1

retval = 3 + (x(1) - 1.5*x(2))^2 + (x(2) - 2)^2;

>> SteepestDescent('Example6_1', [0.5 0.5], 20,

0.0001, 0, 1, 20)

Where

[0.5 0.5] -initial guess value

20 -No. of iteration

0.001 -Golden search tol.

0 -initial step size

1 -step interval

20 -scanning step

>> ans

2.7585 1.8960

Figure 11: Multivariable Gradient based optimization

Figure 12: Steepest Descent

MATLAB Optimization Toolbox

PART II

Presentation Outline

Introduction

– Function Optimization

– Optimization Toolbox

– Routines / Algorithms available

Minimization Problems

– Unconstrained

– Constrained Example

The Algorithm Description

Multiobjective Optimization

– Optimal PID Control Example

Function Optimization

Optimization concerns the minimization or maximization of

functions

Standard Optimization Problem:

~~

minx

f x

~0jg x

~0ih x

L U

k k kx x x

Equality ConstraintsSubject to:

Inequality Constraints

Side Constraints

~f x is the objective function, which measure and evaluate the performance of a

system. In a standard problem, we are minimizing the function. For

maximization, it is equivalent to minimization of the –ve of the objective

function.

Where:

~x is a column vector of design variables, which can

affect the performance of the system.

Function Optimization (Cont.)

~0ih x

L U

k k kx x x

Equality Constraints

Inequality Constraints

Side Constraints

~0jg x

Most algorithm require less than!!!

Constraints – Limitation to the design space. Can be linear or

nonlinear, explicit or implicit functions

Optimization Toolbox

Is a collection of functions that extend the capability of MATLAB.

The toolbox includes routines for:

• Unconstrained optimization

• Constrained nonlinear optimization, including goal attainment

problems, minimax problems, and semi-infinite minimization

problems

• Quadratic and linear programming

• Nonlinear least squares and curve fitting

• Nonlinear systems of equations solving

• Constrained linear least squares

• Specialized algorithms for large scale problems

Minimization Algorithm

Minimization Algorithm (Cont.)

Equation Solving Algorithms

Least-Squares Algorithms

Implementing Opt. Toolbox

Most of these optimization routines require the definition of an M-

file containing the function, f, to be minimized.

Maximization is achieved by supplying the routines with –f.

Optimization options passed to the routines change optimization

parameters.

Default optimization parameters can be changed through an

options structure.

Unconstrained Minimization

Consider the problem of finding a set of values [x1 x2]T that

solves

1

~

2 2

1 2 1 2 2~

min 4 2 4 2 1x

xf x e x x x x x

1 2~

Tx x x

Steps:

• Create an M-file that returns the function value (Objective Function). Call it objfun.m

• Then, invoke the unconstrained minimization routine. Use fminunc

Step 1 – Obj. Function

function f = objfun(x)

f=exp(x(1))*(4*x(1)^2+2*x(2)^2+4*x(1)*x(2)+2*x(2)+1);

1 2~

Tx x x

Objective function

Step 2 – Invoke Routine

x0 = [-1,1];

options = optimset(‘LargeScale’,’off’);

[xmin,feval,exitflag,output]=

fminunc(‘objfun’,x0,options);

Output argumentsInput arguments

Starting with a guess

Optimization parameters settings

• xmin =

• 0.5000 -1.0000

• feval =

• 1.3028e-010

• exitflag =

• 1

• output =

• iterations: 7

• funcCount: 40

• stepsize: 1

• firstorderopt: 8.1998e-004

• algorithm: 'medium-scale: Quasi-Newton line

search'

Minimum point of design variables

Objective function value

Exitflag tells if the algorithm is converged.If exitflag > 0, then local minimum is found

Some other information

Results

[xmin,feval,exitflag,output,grad,hessian]=

fminunc(fun,x0,options,P1,P2,…)

fun : Return a function of objective function.

x0 : Starts with an initial guess. The guess must be

a vector

of size of number of design variables.

Option : To set some of the optimization parameters.

(More after few slides)

P1,P2,… : To pass additional parameters.

More on fminunc – Input

[xmin,feval,exitflag,output,grad,hessian]=

fminunc(fun,x0,options,P1,P2,…)

xmin : Vector of the minimum point (optimal point).

The size is the number of design variables.

feval : The objective function value of at the optimal

point.

exitflag : A value shows whether the optimization routine

is terminated successfully. (converged if >0)

Output : This structure gives more details about the

optimization

grad : The gradient value at the optimal point.

hessian : The hessian value of at the optimal point

More on fminunc – Output

Options =

optimset(‘param1’,value1, ‘param2’,value2,…)

Options Setting – optimset

The routines in Optimization Toolbox has a set of default

optimization parameters.

However, the toolbox allows you to alter some of those

parameters, for example: the tolerance, the step size, the gradient

or hessian values, the max. number of iterations etc.

There are also a list of features available, for example: displaying

the values at each iterations, compare the user supply gradient or

hessian, etc.

You can also choose the algorithm you wish to use.

Options =

optimset(‘param1’,value1, ‘param2’,value2,…)

LargeScale - Use large-scale algorithm if

possible [ {on} | off ]

The default is with { }

Parameter (param1)

Value (value1)

Options Setting (Cont.)

Type help optimset in command window, a list of options

setting available will be displayed.

How to read? For example:

LargeScale - Use large-scale algorithm if

possible [ {on} | off ]

Since the default is on, if we would like to turn off, we just type:

Options = optimset(‘LargeScale’, ‘off’)

Options =

optimset(‘param1’,value1, ‘param2’,value2,…)

and pass to the input of fminunc.

Options Setting (Cont.)

Display - Level of display [ off | iter | notify | final ]

MaxIter - Maximum number of iterations allowed [ positive integer ]

TolCon - Termination tolerance on the constraint violation [

positive scalar ]

TolFun - Termination tolerance on the function value [ positive

scalar ]

TolX - Termination tolerance on X [ positive scalar ]

Highly recommended to use!!!

Useful Option Settings

fminunc and fminsearch

fminunc uses algorithm with gradient and hessian information.

Two modes:

• Large-Scale: interior-reflective Newton

• Medium-Scale: quasi-Newton (BFGS)

Not preferred in solving highly discontinuous functions.

This function may only give local solutions..

fminsearch is generally less efficient than fminunc for

problems of order greater than two. However, when the problem is highly discontinuous, fminsearch may be more robust.

This is a direct search method that does not use numerical or analytic gradients as in fminunc.

This function may only give local solutions.

[xmin,feval,exitflag,output,lambda,grad,hessian]

=

fmincon(fun,x0,A,B,Aeq,Beq,LB,UB,NONLCON,options,

P1,P2,…)

Vector of Lagrange

Multiplier at optimal

point

Constrained Minimization

~

1 2 3~

minx

f x x x x

2

1 22 0x x

1 2 3

1 2 3

2 2 0

2 2 72

x x x

x x x

1 2 30 , , 30x x x

Subject to:

1 2 2 0,

1 2 2 72A B

0 30

0 , 30

0 30

LB UB

function f = myfun(x)

f=-x(1)*x(2)*x(3);

Example

2

1 22 0x xFor

Create a function call nonlcon which returns 2 constraint vectors [C,Ceq]

function [C,Ceq]=nonlcon(x)

C=2*x(1)^2+x(2);

Ceq=[];Remember to return a nullMatrix if the constraint doesnot apply

Example (Cont.)

x0=[10;10;10];

A=[-1 -2 -2;1 2 2];

B=[0 72]';

LB = [0 0 0]';

UB = [30 30 30]';

[x,feval]=fmincon(@myfun,x0,A,B,[],[],LB,UB,@nonlcon)

1 2 2 0,

1 2 2 72A B

Initial guess (3 design variables)

CAREFUL!!!

fmincon(fun,x0,A,B,Aeq,Beq,LB,UB,NONLCON,options,P1,P2,…)

0 30

0 , 30

0 30

LB UB

Example (Cont.)

Warning: Large-scale (trust region) method does not currently solve this type of problem, switching to medium-scale (line search).

> In D:\Programs\MATLAB6p1\toolbox\optim\fmincon.m at line 213

In D:\usr\CHINTANG\OptToolbox\min_con.m at line 6

Optimization terminated successfully:

Magnitude of directional derivative in search direction less than 2*options.TolFun and maximum constraint violation is less than options.TolCon

Active Constraints:

2

9

x =

0.00050378663220

0.00000000000000

30.00000000000000

feval =

-4.657237250542452e-035

2

1 22 0x x

1 2 3

1 2 3

2 2 0

2 2 72

x x x

x x x

1

2

3

0 30

0 30

0 30

x

x

x

Const. 1

Const. 2

Const. 3

Const. 4

Const. 5

Const. 6

Const. 7

Sequence: A,B,Aeq,Beq,LB,UB,C,Ceq

Const. 8

Const. 9

Example (Cont.)