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Part 4 Nonlinear Programming
4.3 Successive Linear Programming
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Approach 3 Cutting Plane Method
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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.
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Basic Strategy
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Basic Strategy
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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).
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Example F x2 P1 P2 x1 F(x1,x2)=-x1-x2
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Example
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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.
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Basic Ideas of Kelley’s Algorithm
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Nonlinear Objective Function
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Generation of Cuts
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Generation of Cuts
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Generation of Cuts Kelley proposed that :
Only the linearization of the most violated constraint be used to construct a cut.
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Kelley’s Algorithm
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Kelley’s Algorithm – Step 1
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Kelley’s Algorithm – Step 2
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Kelley’s Algorithm – Step 3
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Kelley’s Algorithm – Step 4
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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.
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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.
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Requirement of Convexity
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Remark
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Cut-Deletion Procedure
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Step 4a
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Step 4b
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