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Optimisasi dengan batasan persamaan
   (Optimization with equality constraints)

Mengapa batasan relevan dalam kajian
ekonomi?
  Masalah ekonomi timbul karena kelangkaan
  (scarcity).
  Kelangkaan menyebabkan keputusan ekonomi
  (termasuk optimisasi) tidal dilakukan dalam
  kondisi tidak terbatas.
  Dengan kata lain, constrained optimization
  merupakan pembahasan pokok dalam ekonomi

                                           slide 0
Lagrange Multiplier

Merupakan suatu metode matematika yang
dapat menyatakan suatu persoalan nilai ekstrim
(maksimum atau minimum) yang mempunyai
batasan (constrained-extremum) dalam bentuk
yang bisa diselesaikan dengan menggunakan
First-Order condition (FOC)




                                            slide 1
Iso-cost lines

                                     Draw set of points where
z2                                  cost of input is c, a constant
                                     Repeat for a higher value
                                    of the constant
                                     Imposes direction on the
                                    diagram...
           w1z1 + w2z2 = c"


           w1z1 + w2z2 = c'


           w 1z 1 + w 2z 2 =
           c                                       Use this to
                               z1                  derive
                                                   optimum

                                                           slide 2
Cost-minimisation

                         The firm minimises cost...
z2
 q                       Subject to output constraint
                         Defines the stage 1 problem.
                         Solution to the problem


                         minimise
                         m
                         Σ wizi
                         i=1

                         subject to φ(z) ≥ q
        z*                But the solution depends
                         on the shape of the input-
                         requirement set Z.
                    z1
                          What would happen in
                         other cases?
                                             slide 3
Convex, but not strictly
convex Z
z2




         Any z in this set is
         cost-minimising



                                     An interval of solutions

                                z1

                                                       slide 4
Convex Z, touching axis

z2




                      Here MRTS21 > w1 / w2
                    at the solution.
               z1    Input 2 is “too
       z*
                    expensive” and so isn’t
                    used: z2*=0.       slide 5
Non-convex Z

z2




                      There could be multiple
     z*              solutions.

                      But note that there’s no
                     solution point between z*
          z**        and z**.
                z1

                                        slide 6
Aplikasi 1: Optimalisasi kepuasan
                konsumen
                The primal problem
             objective function         Tujuan konsumen adalah
      x2                               memaksimalkan utilitas
                                        Batasannya adalah budget




                                       max U(x) subject to
                                        n
Constraint                             Σ pixi ≤ y
set                                    i=1

                        x*              Cara lain memandang
                                       persoalan ini adalah...


                                  x1

                                                          slide 7
The dual problem
                                 Konsumen bertujuan
x2
z2
 υ
 q                             meminimalkan pengeluaran
            Constraint           Untuk mencapai utilitas
                               tertentu
            set



                               minimise
                                n
                               Σ pixi
                               i=1

                               subject to U(x) ≥ υ
              x*
              z*

     Contours of          x1
                          z1
     objective function
                                                   slide 8
The Primal and the Dual…

 There’s an attractive
symmetry about the two          n
approaches to the problem      Σ pixi+   λ[υ – U(x)]
                               i=1

 In both cases the ps are
given and you choose the xs.                     n
But…                                      [
                               U(x) + µ y – Σ pi xi       ]
 …constraint in the primal                      i=1
becomes objective in the
dual…
  …and vice versa.


                                                     slide 9
A neat connection

                             Compare the primal problem
                           of the consumer...
                            ...with the dual problem




x2              x2υ                     The two are
                                       equivalent

                                         So we can link up
                                       their solution
                                       functions and
                                       response functions
          x*
                      x*
                                          Run through
                x1                x1      the primal

                                                 slide 10
Utilitas dan Pengeluaran
  Maksimisasi utilitas dan minimisasi pengeluaran pada
  dasarnya merupakan persoalan yang sama yang dilihat
  dari sudut pandang berbeda
  Dengan demikian, solusinya sangat terkait satu sama
  lainnya

            Primal                      Dual
                              n              n
                          [
Problem: max U(x) + µ y – Σ pixi    ]   min Σ pixi + λ[υ – U(x)]
                                        x    i=1
             x                i=1
 Solution
function:
            V(p, y)                     C(p, υ)
Response x * = Di(p, y)                 xi* = Hi(p, υ)
 function: i                                             slide 11
Bentuk Umum

Objective Function
                        z = f ( x, y )

Constraint
                c = g ( x, y )

Lagrangian
              L = f ( x, y ) + λ [c − g ( x, y )]


                                                    slide 12
Penyelesaian (FOC)

Necessary Conditions

            Lλ = c − g ( x, y ) = 0

            Lx = f x − λg x = 0

            L y = f y − λg y = 0


                                      slide 13
Aplikasi 1: Maksimisasi utilitas dengan
                pendapatan terbatas

Utility Function
                       U = x1 x2 + 2x1

Budget Constraint
                         4 x1 + 2 x2 = 60

Lagrangian
             L = x1 x2 + 2 x1 + λ [60 − 4 x1 − 2 x2 ]


                                                        slide 14
Necessary Conditions

        ∂L
           = 60 − 4 x1 − 2 x2 = 0
        ∂λ
        ∂L
            = x2 + 2 − 4λ = 0
        ∂x1

        ∂L
            = x1 − 2λ = 0
        ∂x1
Tentukan nilai x1 dan x2
                                    slide 15
Teorema Envelope


Teorema yang membahas perubahan nilai
optimal suatu fungsi dengan berubahnya salah
satu parameter dalam fungsi tersebut




                                           slide 16
The Envelope Theorem

Substituting into the original objective function
yields an expression for the optimal value of y (y*)
         y* = f [x1*(a), x2*(a),…,xn*(a),a]
Differentiating yields


dy * ∂f dx1 ∂f dx 2           ∂f dxn ∂f
    =    ⋅  +    ⋅   + ... +     ⋅  +
 da   ∂x1 da ∂x 2 da         ∂xn da ∂a


                                                slide 17
Marshallian Demand

The derivation of
an ordinary
demand curve.
Budget lines B1,
B2 and B3 show
different prices of
apples but the
same income and
price of oranges.
DM is the ordinary
(Marshallian)
demand curve.

                         slide 18
Hicksian Demand

The derivation of an
income-adjusted demand
curve. Budget lines B1, B2
and B3 show different
combinations of prices
and income
corresponding to the
same real income. DH is
the resulting income-
adjusted (Hicksian)
demand curve.

                             slide 19

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Matematika ekonomi slide_optimasi_dengan_batasan_persamaan

  • 1. Optimisasi dengan batasan persamaan (Optimization with equality constraints) Mengapa batasan relevan dalam kajian ekonomi? Masalah ekonomi timbul karena kelangkaan (scarcity). Kelangkaan menyebabkan keputusan ekonomi (termasuk optimisasi) tidal dilakukan dalam kondisi tidak terbatas. Dengan kata lain, constrained optimization merupakan pembahasan pokok dalam ekonomi slide 0
  • 2. Lagrange Multiplier Merupakan suatu metode matematika yang dapat menyatakan suatu persoalan nilai ekstrim (maksimum atau minimum) yang mempunyai batasan (constrained-extremum) dalam bentuk yang bisa diselesaikan dengan menggunakan First-Order condition (FOC) slide 1
  • 3. Iso-cost lines Draw set of points where z2 cost of input is c, a constant Repeat for a higher value of the constant Imposes direction on the diagram... w1z1 + w2z2 = c" w1z1 + w2z2 = c' w 1z 1 + w 2z 2 = c Use this to z1 derive optimum slide 2
  • 4. Cost-minimisation The firm minimises cost... z2 q Subject to output constraint Defines the stage 1 problem. Solution to the problem minimise m Σ wizi i=1 subject to φ(z) ≥ q z* But the solution depends on the shape of the input- requirement set Z. z1 What would happen in other cases? slide 3
  • 5. Convex, but not strictly convex Z z2 Any z in this set is cost-minimising An interval of solutions z1 slide 4
  • 6. Convex Z, touching axis z2 Here MRTS21 > w1 / w2 at the solution. z1 Input 2 is “too z* expensive” and so isn’t used: z2*=0. slide 5
  • 7. Non-convex Z z2 There could be multiple z* solutions. But note that there’s no solution point between z* z** and z**. z1 slide 6
  • 8. Aplikasi 1: Optimalisasi kepuasan konsumen The primal problem objective function Tujuan konsumen adalah x2 memaksimalkan utilitas Batasannya adalah budget max U(x) subject to n Constraint Σ pixi ≤ y set i=1 x* Cara lain memandang persoalan ini adalah... x1 slide 7
  • 9. The dual problem Konsumen bertujuan x2 z2 υ q meminimalkan pengeluaran Constraint Untuk mencapai utilitas tertentu set minimise n Σ pixi i=1 subject to U(x) ≥ υ x* z* Contours of x1 z1 objective function slide 8
  • 10. The Primal and the Dual… There’s an attractive symmetry about the two n approaches to the problem Σ pixi+ λ[υ – U(x)] i=1 In both cases the ps are given and you choose the xs. n But… [ U(x) + µ y – Σ pi xi ] …constraint in the primal i=1 becomes objective in the dual… …and vice versa. slide 9
  • 11. A neat connection Compare the primal problem of the consumer... ...with the dual problem x2 x2υ The two are equivalent So we can link up their solution functions and response functions x* x* Run through x1 x1 the primal slide 10
  • 12. Utilitas dan Pengeluaran Maksimisasi utilitas dan minimisasi pengeluaran pada dasarnya merupakan persoalan yang sama yang dilihat dari sudut pandang berbeda Dengan demikian, solusinya sangat terkait satu sama lainnya Primal Dual n n [ Problem: max U(x) + µ y – Σ pixi ] min Σ pixi + λ[υ – U(x)] x i=1 x i=1 Solution function: V(p, y) C(p, υ) Response x * = Di(p, y) xi* = Hi(p, υ) function: i slide 11
  • 13. Bentuk Umum Objective Function z = f ( x, y ) Constraint c = g ( x, y ) Lagrangian L = f ( x, y ) + λ [c − g ( x, y )] slide 12
  • 14. Penyelesaian (FOC) Necessary Conditions Lλ = c − g ( x, y ) = 0 Lx = f x − λg x = 0 L y = f y − λg y = 0 slide 13
  • 15. Aplikasi 1: Maksimisasi utilitas dengan pendapatan terbatas Utility Function U = x1 x2 + 2x1 Budget Constraint 4 x1 + 2 x2 = 60 Lagrangian L = x1 x2 + 2 x1 + λ [60 − 4 x1 − 2 x2 ] slide 14
  • 16. Necessary Conditions ∂L = 60 − 4 x1 − 2 x2 = 0 ∂λ ∂L = x2 + 2 − 4λ = 0 ∂x1 ∂L = x1 − 2λ = 0 ∂x1 Tentukan nilai x1 dan x2 slide 15
  • 17. Teorema Envelope Teorema yang membahas perubahan nilai optimal suatu fungsi dengan berubahnya salah satu parameter dalam fungsi tersebut slide 16
  • 18. The Envelope Theorem Substituting into the original objective function yields an expression for the optimal value of y (y*) y* = f [x1*(a), x2*(a),…,xn*(a),a] Differentiating yields dy * ∂f dx1 ∂f dx 2 ∂f dxn ∂f = ⋅ + ⋅ + ... + ⋅ + da ∂x1 da ∂x 2 da ∂xn da ∂a slide 17
  • 19. Marshallian Demand The derivation of an ordinary demand curve. Budget lines B1, B2 and B3 show different prices of apples but the same income and price of oranges. DM is the ordinary (Marshallian) demand curve. slide 18
  • 20. Hicksian Demand The derivation of an income-adjusted demand curve. Budget lines B1, B2 and B3 show different combinations of prices and income corresponding to the same real income. DH is the resulting income- adjusted (Hicksian) demand curve. slide 19