Presentation is loading. Please wait.

Presentation is loading. Please wait.

Part 4 Nonlinear Programming

Similar presentations


Presentation on theme: "Part 4 Nonlinear Programming"— Presentation transcript:

1 Part 4 Nonlinear Programming
4.3 Successive Linear Programming

2 Approach 3 Cutting Plane Method

3 Basic Strategy We seek to devise an algorithm that will solve this problem by solving a sequence of intermediate problems constructed by starting out with a rough approximation to the feasible region and successively improving the approximation by adding constraint estimates updated at the intermediate solution.

4 Basic Strategy

5 Basic Strategy

6 Basic Strategy Case (ii) gives us an indication of the possible location of the optimum. In order to improve our approximation to F in the vicinity of x^(1), we will need to modify the boundaries of Z^0 near x^(1). This can be achieved by imposing additional constraints that will exclude from Z^0 the region in the vicinity of x^(1).

7 Example F x2 P1 P2 x1 F(x1,x2)=-x1-x2

8 Example

9 Example If the computations are continued in this fashion, and if with each set of cuts we can be sure that a nonempty remaining portion of Z^(0) is eliminated, then it seems reasonable that eventually a point will be reached that is feasible and that consequently will be the minimum of f(x) over F.

10 Basic Ideas of Kelley’s Algorithm

11 Nonlinear Objective Function

12 Generation of Cuts

13 Generation of Cuts

14 Generation of Cuts Kelley proposed that :
Only the linearization of the most violated constraint be used to construct a cut.

15 Kelley’s Algorithm

16 Kelley’s Algorithm – Step 1

17 Kelley’s Algorithm – Step 2

18 Kelley’s Algorithm – Step 3

19 Kelley’s Algorithm – Step 4

20 Advantages Any linearity or near linearity in the original problem is preserved and directly utilized. The sub-problem to be solved at each major iteration is one for which the powerful techniques of LP are applicable.

21 Disadvantages The algorithm generates a sequence of infeasible points. Thus, it cannot be terminated early with a “good” but perhaps not optimal point. The size of the LP problem grows continuously. The feasible region F has to be convex.

22 Requirement of Convexity

23 Remark

24 Cut-Deletion Procedure

25 Step 4a

26 Step 4b


Download ppt "Part 4 Nonlinear Programming"

Similar presentations


Ads by Google