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# chapter2 - 2. UNCONSTRAINED OPTIMIZATION In this chapter,...

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Unformatted text preview: 2. UNCONSTRAINED OPTIMIZATION In this chapter, we develop principles for identifying and validating candidates for opti- mality in problems with no explicit constraints: min f ( x ) over all x ∈ S, where S is typically the whole space R n or some simply described subset, such as an interval or neighborhood of a given point. To lay the foundation for these developments, we first review and refine the one-dimensional optimization principles from Calculus. These are then extended in a natural way to higher dimensions, where they are made practical by incorporating ideas from Linear Algebra. 2.1 First-Order Necessary Conditions for Optimality We start with the one-dimensional case of optimizing a function on an interval in R . The following theorem, due to Fermat, is the best known result in optimization theory. Theorem 2.1.1 (Necessary condition for one-dimensional optimality) . Suppose ¯ x is a local minimizer (or maximizer) of f on the interval I ⊆ R . If ¯ x is not an endpoint of I and f is differentiable at ¯ x , then f (¯ x ) = 0 . Definition 2.1.2 (Critical point) . We say that ¯ x is a critical point for f if f (¯ x ) = 0. Observation 2.1.3 (Identifying candidates via necessary conditions) . Note that The- orem 2.1.1 provides us with candidates for optimality. If ¯ x is a local minimizer for f on I ⊆ R , then one of the following conditions must hold: (a) ¯ x is an endpoint of I ; (b) f is not differentiable at ¯ x ; or (c) ¯ x is a critical point for f . We may discard any points that do not satisfy at least one of these three conditions. Any rule for selecting candidates in this way is called a necessary condition for optimality. Proof of Theorem 2.1.1. Recall that f (¯ x ) = lim x → ¯ x f ( x )- f (¯ x ) x- ¯ x . Since ¯ x is a local minimizer on I and not an endpoint of I , there must exist δ > 0 so that f (¯ x ) ≤ f ( x ) for all x ∈ (¯ x- δ, ¯ x + δ ). In other words, f ( x )- f (¯ x ) ≥ 0 for all x close to ¯ x , so f ( x )- f (¯ x ) x- ¯ x ≤ , if x ∈ (¯ x- δ, ¯ x ) 13 and f ( x )- f (¯ x ) x- ¯ x ≥ , if x ∈ (¯ x, ¯ x + δ ) . Thus, lim x → ¯ x- f ( x )- f (¯ x ) x- ¯ x ≤ and lim x → ¯ x + f ( x )- f (¯ x ) x- ¯ x ≥ , so f (¯ x ) = 0. By considering one-dimensional derivatives along the coordinate axes, we can extend this result to higher dimensions. Theorem 2.1.4 (Necessary condition for multidimensional optimality) . Consider a set S ⊆ R n and a function f : S → R n . Suppose that ¯ x is a local minimizer for f on S . If ¯ x is an interior point of S and ∇ f (¯ x ) exists, then ∇ f (¯ x ) = 0 . Proof. Given a coordinate i , we need to show that ∂f (¯ x ) /∂x i = 0. Define ϕ i ( t ) = f (¯ x 1 ,..., ¯ x i- 1 ,t, ¯ x i +1 ,..., ¯ x n ) ....
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## This note was uploaded on 03/18/2012 for the course MTH 432 taught by Professor Douglasward during the Spring '12 term at Miami University.

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chapter2 - 2. UNCONSTRAINED OPTIMIZATION In this chapter,...

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