Integer Type Unknown Variables

The critical point of f (x, s) based on the variable x₁ is obtained as follows:

f (x1) = x1(q11x1+ q12x2+ q21x2+ q13s1+ q31s1+ q14s2+ q41s2+ q15s3+ q51s3+ r1) + c.

(2.72) The partial derivative with respect to the unknown variable x1 is:

∂f

∂x₁ = 2q₁₁x₁+ q₁₂x₂+ q₂₁x₂+ q₁₃s₁+ q₃₁s₁+ q₁₄s₂+ q₄₁s₂+ q₁₅s₃+ q₅₁s₃+ r₁. (2.73)

Since q₁₂= q₂₁, q₁₃= q₃₁, q₁₄= q₄₁, and q₁₅= q₅₁:

∂f

∂x₁ = 2q₁₁x₁+ 2q₁₂x₂+ 2q₁₃s₁+ 2q₁₄s₂+ 2q₁₅s₃+ r₁. (2.74)

The critical point x^?₁ for the variable x₁ is obtained by equating the partial derivative to zero:

∂f

∂x1

= 0 =⇒

2q₁₁x₁+ 2q₁₂x₂+ 2q₁₃s₁+ 2q₁₄s₂+ 2q₁₅s₃+ r₁ = 0, 2q₁₁x₁ = −(2q₁₂x₂+ 2q₁₃s₁+ 2q₁₄s₂+ 2q₁₅s₃+ r₁), x₁ = 2q₁₂x₂+ 2q₁₃s₁+ 2q₁₄s₂+ 2q₁₅s₃+ r₁

−2q₁₁ ,

or, x^?₁ = 2q₁₂x₂+ 2q₁₃s₁+ 2q₁₄s₂+ 2q₁₅s₃+ r₁

−2q₁₁ .

(2.75)

Substituting the original values from (2.70) in (2.75), we obtain:

x^?₁ = 2P (a^>₁a2)x2+ 2P a11s1+ 2P a21s2+ 2P a31s3+ (d1− 2P (b^>a1))

−2P (a^>₁a₁) , (2.76)

which is a critical point at which the function f (x, s) is at relative extremum (relative minimum for our minimization problem).

The change in f (x, s) due to change in x₁ is calculated as follows:

f (x₁) = x₁(q₁₁x₁+ q₁₂x₂+ q₁₃s₁+ q₁₄s₂+ q₁₅s₃ + q₂₁x₂+ q₃₁s₁+ q₄₁s₂+ q₅₁s₃+ r₁) + c.

(2.77) If x₁ changes to x⁰₁, then f (x⁰₁) is given by:

f (x⁰₁) = x⁰₁(q₁₁x⁰₁+ q₁₂x₂+ q₁₃s₁+ q₁₄s₂+ q₁₅s₃ + q₂₁x₂+ q₃₁s₁+ q₄₁s₂+ q₅₁s₃+ r₁) + c.

(2.78) The difference between (2.78) and (2.77) gives the change in value of function when x₁ changes to x⁰₁. Therefore,

∆f_x⁰

1 = f (x⁰₁) − f (x1),

= (x⁰₁− x₁)(q₁₁(x⁰₁+ x₁) + q₁₂x₂+ q₁₃s₁+ q₁₄s₂+ q₁₅s₃+ q₂₁x₂+ q₃₁s₁+ q₄₁s₂+ q₅₁s₃+ r₁),

= q₁₁x⁰₁²− q11x²₁+ (q₁₂x₂+ q₁₃s₁+ q₁₄s₂+ q₁₅s₃+ q₂₁x₂+ q₃₁s₁+ q₄₁s₂+ q₅₁s₃+ r₁)x⁰₁− (q12x2+ q13s1+ q14s2+ q15s3+ q21x2+ q31s1+ q41s2+ q51s3+ r1)x1.

(2.79)

Since q₁₂= q₂₁, q₁₃= q₃₁, q₁₄= q₄₁, and q₁₅= q₅₁:

∆f_x⁰

1 = q₁₁x⁰₁²− q₁₁x²₁+

(2q₁₂x₂+ 2q₁₃s₁+ 2q₁₄s₂+ 2q₁₅s₃+ r₁)x⁰₁− (2q₁₂x₂+ 2q₁₃s₁+ 2q₁₄s₂+ 2q₁₅s₃+ r₁)x₁.

(2.80)

Note that the partial derivative of (2.80) with respect to x⁰₁ is the same as (2.74):

∂∆f_x⁰

1

∂x⁰₁ = 2q₁₁x⁰₁ + 2q₁₂x₂+ 2q₁₃s₁+ 2q₁₄s₂+ 2q₁₅s₃+ r₁. (2.81)

If the value of x₁ is updated to x^?₁, we obtain ∆f due to change in x₁ to x^?₁ as follows:

Substituting the original values from (2.70) in (2.82), we obtain:

∆f_x^?

1 for a problem with n integer variables and m inequality constraints shown in Figure 2.2 is:

x^?_i = 2Pi−1

Substituting the original values from (2.70) in (2.85) we obtain:

In the previous section, we calculated the value of x^?_i which is the critical value of x_i at which the function f (x, s) equals zero, we then calculated ∆f_x⁰

i when the variable changes from x_i to x⁰_i, and finally we calculated ∆f_x^?

i which shows the change in f (x, s) if the variable changes from xi to x^?_i. Based on this information we will show that updating variable xi to a new value is based up on x_i and x^?_i, and using graphical representations we will show the feasible and infeasible regions for the new value of x_i.

Recall from the previous section that if x₁ changes to x⁰₁ then ∆f_x⁰

i is given by:

1 is equal to zero when one of the following condition is met:

∆f_x⁰

1 = 0 =⇒ (2.88a)

x⁰₁− x₁ = 0, (2.88b)

q₁₁(x⁰₁+ x₁) + q₁₂x₂+ q₁₃s₁+ q₁₄s₂+ q₁₅s₃+ q₂₁x₂+ q₃₁s₁+ q₄₁s₂+ q₅₁s₃+ r₁ = 0.

(2.88c)

From (2.88b), if x⁰₁− x₁ = 0 then the variable is not changing, since x⁰₁ = x₁, and ∆f_x⁰

1

is always 0 in this case. But, from (2.88c), we obtain the following:

q₁₁(x⁰₁+ x₁) + q₁₂x₂+ q₁₃s₁+ q₁₄s₂+ q₁₅s₃ + q₂₁x₂+ q₃₁s₁+ q₄₁s₂+ q₅₁s₃+ r₁ = 0, q11(x⁰₁) = −(q11(x1) + q12x2+ q13s1+ q14s2+ q15s3+ q21x2+ q31s1+ q41s2 + q51s3+ r1), x⁰₁ = −(q₁₁(x₁) + q₁₂x₂+ q₁₃s₁+ q₁₄s₂+ q₁₅s₃+ q₂₁x₂+ q₃₁s₁+ q₄₁s₂+ q₅₁s₃+ r₁)

q₁₁ ,

x⁰₁ = −(q₁₂x₂+ q₁₃s₁+ q₁₄s₂+ q₁₅s₃+ q₂₁x₂+ q₃₁s₁+ q₄₁s₂+ q₅₁s₃+ r₁) q11

− x₁, x⁰₁ = −(2q₁₂x₂ + 2q₁₃s₁ + 2q₁₄s₂ + 2q₁₅s₃+ r₁)

q₁₁ − x1,

x⁰₁ = 2x^?₁− x₁.

(2.89) From (2.89), if the value of variable is changed from x₁ to x⁰₁ where x⁰₁ = 2x^?₁− x₁, then

∆f_x⁰

1 = 0.

To generalize our findings, x^?_i represents the critical value at which the function f (x, s) is at relative extremum (relative minimum for our minimization problem). The value of

∆f_x⁰

i = 0 when:

• x⁰i = x_i (new value equals old value).

• x⁰_i = 2x^?_i − x_i (new value = 2x^?_i - old value).

The variable update procedure is simply based on x_i and x^?_i, and the four possible combina-tions of these two parameters give the following cases. Note that the figures shown in these cases are parabolic since ∆f_xi is quadratic in nature.

• Case 1: xi = 0 and x^?_i ≤ 0

∆f_x

x x_i

x < 0

x⁰_i =2x^?_i

∆f_x^?

i

Figure 2.3. Change in ∆fx when: xi= 0 and x^?_i ≤ 0.

Figure 2.3 represents the change in ∆f_x when x_i = 0 and x^?_i ≤ 0. Based on this figure, the best new value for xi is x^?_i where ∆fx^?_i is obtained, but since x^?_i < 0 the new value of x_i is kept at 0.

• Case 2: xi = 0 and x^?_i > 0

∆f_x

x x_i

∆f_x < 0 x⁰_i = 2x^?_i

∆fx^?_i

Figure 2.4. Change in ∆fx when: xi= 0 and x^?_i > 0.

Figure 2.4 represents the change in ∆f_x when x_i = 0 and x^?_i > 0. Based on this, the best new value for x_i is x^?_i, since maximum absolute value ∆f_x^?

i is obtained at x^?_i. If x^?_i is not integer then the best integer value based on ∆f_x that is closer to x^?_i is chosen.

Also, in an attempt to escape local optimality of the objective function, x_i can be updated to an integer value of x, where xi < x ≤ 2x^?_i.

• Case 3: xi > 0 and x^?_i ≤ 0

∆f_x

x x_i

x < 0

∆fx < 0 x⁰_i = 2x^?_i − x_i

∆fx^?_i

x^?_i = 0 x^?_i < 0

Figure 2.5. Change in ∆fx when: xi> 0 and x^?_i ≤ 0.

Figure 2.5 represents two different curves based on the value of x^?_i.

If x^?_i = 0, represented by the red curve, the best new value of x_i is x^?_i since the maximum absolute value ∆f_x^?

i is obtained at x^?_i. A new value for x_i can also be updated to an integer value of x, where 0 ≤ x < x_i to escape local optimality of the objective function value.

If x^?_i < 0, represented by the blue curve, the best new value of x_i is x^?_i since the maximum absolute value ∆f_x^?

i is obtained at x^?_i. Since x^?_i < 0, the new value of x_i is updated to 0, or to an integer value of x where 0 ≤ x < x_i.

• Case 4: xi > 0 and x^?_i > 0

∆f_x

x_i x

x < 0

∆f_x^?

i

x⁰_i = 2x^?_i − x_i

∆f_x< 0

(2x^?_i − x_i) < 0 0 ≤ (2x^?_i − x_i) < x_i (2x^?_i − x_i) > x_i

Figure 2.6. Change in ∆f_x when: x_i> 0 and x^?_i > 0.

Figure 2.6 represents three different curves based on the value of 2x^?_i − x_i.

If (2x^?_i − x_i) > x_i, represented by blue curve, the best new value for x_i is x^?_i since maximum absolute value ∆fx^?_i is obtained at x^?_i. If x^?_i is not integer then the best integer value based on ∆f_x that is closer to x^?_i is chosen. Also, in an attempt to escape local optimality of the objective function, x_i can be updated to an integer value of x, where x_i < x ≤ 2x^?_i − x_i.

If 0 ≤ (2x^?_i − xi) < xi, represented by red curve, the best new value for xi is x^?_i since maximum absolute value ∆f_x^?

i is obtained at x^?_i. If x^?_i is not integer then the best integer value based on ∆f_x that is closer to x^?_i is chosen. Also, in an attempt to escape

local optimality of the objective function, x_i can be updated to an integer value of x, where 2x^?_i − x_i ≤ x < x_i.

If (2x^?_i − x_i) < 0, represented by green curve, the best new value for x_i is x^?_i since maximum absolute value ∆fx^?_i is obtained at x^?_i. If x^?_i is not integer then the best integer value based on ∆f_x that is closer to x^?_i is chosen. Also, in an attempt to escape local optimality of the objective function, x_i can be updated to an integer value of x, where 0 ≤ x < x_i.

In Table 2.3, we summarize the update rules from the four cases. Here x_i represents the current value of the variable and x⁰_i represents the new value of the same variable. Note that an integer value is always selected for the new value of xi.

Case x_i x^?_i (2x^?_i − x_i) Best x⁰_i Acceptable x⁰_i

1 0 ≤ 0 ≤ 0 0 0

2 0 > 0 > 0 x^?_i xi < x ≤ 2x^?_i 3 > 0 0 < 0 0 0 ≤ x < x_i

> 0 < 0 < 0 0 0 ≤ x < x_i 4 > 0 > 0 > x_i x^?_i x_i < x ≤ 2x^?_i − x_i

> 0 > 0 ≥ 0 & < x_i x^?_i 2x^?_i − x_i ≤ x < x_i

> 0 > 0 < 0 x^?_i 0 ≤ x < x_i

Table 2.3: Updating integer type unknown variable based on critical value.