# CS计算机代考程序代写 chain Constrained NL Optimization (NLP)

Constrained NL Optimization (NLP)
Goals:
• Optimality conditions for linear constraints
• Lagrange multipliers and Lagrangian
• Optimality conditions for nonlinear constraints • Duality
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General Statement
A minimization problem with constraints is often written as:
min f (x)
subjectto gi(x)0, iE
gj(x)0, jI where f and g are C2.

Essential Difference Between LP and NLP
For solvable LP problems, optimal solution occurs at an extreme point.
For NLP problems, the optimal solution may not be at an extreme point or even on the boundary of the feasible set.
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Example 1: max P  KL,
subject to 4K  L  8,
Example 2:
max f(x)sinx,
K, L  0 3/21/2020
subject to 0  x  .
Examples
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Case 1: Linear Equality Constraints Consider the optimization problem:
min f (x)
subjectto Axb, Amn
with rank(A)  m  n.
Idea: transform the constrained problem into an unconstrained optimization problem.
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A Motivating Example
Solve the optimization problem with a linear equality constraint:
min f(x)x2 2x x2 x2 4x 11233
subjectto x x 2x 2. 123

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A Motivating Example (cont’d)
The optimization problem can be made “unconstrained” as follows:
min f(x)2×2 3×2 4x x 2x 23232
using x 2x 2x. 123

A strict local minimizer to the unconstrained problem
is:
x2  1.5, x3  1. Thus, for the original problem,
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x (2.5, 1.5, 1), *
f x 1.5. *

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An Exercise
Solve the following optimization problem:
max f (x)  x x x 123
subject to 123
x  x  x 1 aaa
123 withai 0,xi 0,i1,2,3.

Observation
Indeed, any optimization problem with linear equality constraints Ax=b
can be transformed into an unconstrained problem!
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Some Terms from Matrix Theory
To generalize, let’s recall the following notions.
The null space of a matrix Amn , with m  n, is: NApn: Ap0.
 
T
The range space of A is defined as:
RAT qn:qAT,forsomem .  
NARA. nT
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Null‐space Matrix Z: AZ=0
A matrix Z nr is a null-space matrix of A if any vector in N ( A) can be expressed as
a linear combination of the columns of Z.
If r  nm, then Z is called a basis matrix for
the null space of A (as the columns of Z are linearly
independent!).
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Representation of the Null Space
Since the null space can be represented as
N(A) p: pZvforsomev ,  r
it follows that
N(A)  R(Z).
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Comment
If Amn is a matrix of full (row) rank m,
then without loss of any generality, we can assume
AB N, Bmm fullrank,Nm(nm). As it can be directly checked,
Z B1Nn(nm). I
 nm
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As a consequence, we have:
If x is any point satisfying Ax  b, then all other points can be written as
x  xZv, forsomevectorv Particular non-homogeneous solution
because x  x  N ( A).
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General homogenous solutions

Optimization with Linear Equality Constraints
min f (x) subjectto Axb

m i nr  ( v )  f ( x  Z v ) . v
The function  is called the reduced function.
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Comment
If Z is a basis matrix for the null space of A, then

is a function of n‐ m variables, i.e., r = n ‐ m. In other words, the transformed unconstrained
problem involves less variables.
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Motivating Example revisited
min f(x)x2 2x x2 x2 4x 11233
subjectto x x 2x 2. 123
1 2 B1N
TakeZ1 0,forAB N 
and x200.
 1112 0 1  I 
   22  T
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Motivating Example revisited
2 1 2v  xxZv01 01.
as before.
 v2 0 0 1
 
min(v)2v2 3v2 4vv 2v 12121

Optimality Conditions
minf(x) min(v)  f(xZv) subjectto Axb vr

Using the chain rule,
(v)  ZT f (x  Zv)  ZT f (x) 2(v)ZT2 f(xZv)ZZT2 f(x)Z
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Necessary Conditions
Ifx isalocalminimizeroff over x:Axb
*
and Z is a null-space matrix for A, then
 ZTf(x)0. (x:”stationarypoint”) *
 ZT2 f (x )Z is positive semi-definite. *
That is, the reduced gradient is zero and the reduced Hessian matrix is positive semidefinite.
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

A point at which the reduced gradient is zero is

called a stationary point.
The second‐order condition implies:
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pT2f(x)p0, pN(A). *
However, this condition does not require that 2 f (x ) be positive semidefinite!
*

 Ax b, *
Sufficient Conditions
If x satisfies: *
 ZTf(x)0,and *
 ZT2f(x)Zispositivedefinite, *
where Z n(nm) is a basis matrix for N(A), then, x is a strict local minimizer of
*
fover x:Axb.

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Motivating Example revisited
min f(x)x2 2x x2 x2 4x 11233
subjectto x x 2x 2. 123
1 2 B1N TakeZ1 0 ,
I 0 1
 
forA B N  1 1 2 . 
N

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T Sincef(x)2x 2, 2x , 2x 4 ,
123
ZTf(x)(0, 0)T atthefeasiblepoint *
x 2.5, 1.5, 1. *
ZT2f(x)Z 0 2 01 0
1 1 02 0 01 2 * 201  
 4 4 positivedefinite 4 6
 0 0 20 1   

However, 2 f (x ) is not positive definite!!
*

Lagrange Multipliers
Fact : At a local minimizer x , *
f (x )  AT  **
where*m iscalled”Lagrangemultipliers”.
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Proof
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Let Z be any n  r null-space matrix for A. Then,vr,m s.t.
 Zv 0. *
**
f(x)Zv AT ***
 ZTZv 0, usingZT AT (AZ)T 0 *

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A Geometric Demonstration
aT x  b
x*
f (x ) *
a
f (x)
x
isovalue contours

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Motivating Example revisited
min f(x)x2 2x x2 x2 4x 11233
subjectto x x 2x 2. 123
2x 2  1  1 
2×2 1 usingthe1st-ordern.c. 2x 4 2 
3
and x  x  2x  2 the equality constraint
123
  3, x  2.5, 1.5, 1 .
**

T

Lagrangian Function
For the problem: min f (x), subject to Ax  b, Amn with rank(A)  m,
we can define the Lagrangian function:
L(x,λ)f(x)m aTxbf(x)T(Axb)
note :
aiT stands for the ith row of A.
iii i1
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First‐Order Optimality Conditions
Lx ,  0, **
because
 L(x, )f(x)A ,
T L(x, )bAx.
x

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A Perturbed Problem
Assumethatf(x )min f(x), subjecttoAxb. *
Then, for the perturbed problem:
f ( x )  min f ( x), subject to Ax  b  .
Indeed, for small ,
f (x)  f x x  x  f (x )
***
T f(x)xx A
***
 f(x )T **
T
T

Exercise
Consider min f(x)x2 x2x2 2x x x4 8x 113 122 2
subjectto: 2x 5x x 3. 123
(a) Which of the following points are stationary: TT
T
(b) local minimizer? local maximizer? saddle point?
(0,0,2) ; 0,0,3 ; 1,0,1 .
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Case 2: Linear Inequality Constraints
Consider the optimization problem:
min f (x)
subjectto Axb, Amn.
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An Example
min f (x)  1 x2  1 x2 21 22
subjectto x 2x 2 12
x  x  1 12
x 3. 1

x2
x
*
The optimal solution x* reduces to a solution of the equality constrained problem
with the active constraints.
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x1

More specifically,
The minimizer x* for the original problem is the same as the simplified problem:
min1x2 1×2 21 22
24 2
 x* 5, 5,* 50.
subjectto  x2x2.

For this example, x  2x  2 is an active constraint
12 
Note :
at x* , while the other two are inactive.
12
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 m i n f ( x )
subjectto Axb, Amn 
Observations
 m i n f ( x )
subjectto Axb, alltheactiveconstraints
ˆˆ
For the inactive constraints, we associate with zero Lagrange multipliers:
aTxb0, i1,,m. ii*i
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Remark
It may happen that none of the constraints in NL programming are active, i.e., constrained NL programming may be the same as unconstrained. A sharp difference with linear programming!
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22 minf(x)x1 x2
subject to
4x x 0,

12 2x x 0,
12
x  0, x  0.
12
12

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Necessary Conditions
At a local minimizer x , then  s.t. **
 f(x)AT,(or,ZTf(x)0), ***
 * 0,
 T Ax b0, and
**
 ZT 2 f (x )Z is positive semi-definite, *
where Z is a null-space matrix for the matrix
ˆ
A of active constraints at x .
*

Comment 1
ˆ f(x)AT AT()
* * * active
because*i 0foranyinactiveithconstraint.
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Comment 2 (On the sign of Lagrange multipliers for the inequality constraints)
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By contradiction, find a stepsize s to decrease f at x : *
0 f(x s) f(x )f(x )T s. ***
On the other hand, for the active constraints
ˆˆ
atx , i.e., Ax b0, wewant
**
ˆˆˆˆˆ 0A x s bAx b  As
Equivalently,
**

0
T
{f(x) s0andAs0 f(x)A, 0,
ˆˆ **
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Aˆ s
Clearly, the shaded area is empty only when these two vectors are in the same direction!
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f x  *

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An Example
Check the necessary conditions at x  (1, 0)T *
of the constrained minimization problem: 24
subject to
minx1 1.5 x2 0.5 x
1x x 0, 12
1x x 0, 12
1x x 0, 12
1x x 0. 12