Engineering Optimization Chapter 3 : Functions of Several Variables (Part 1)

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Engineering Optimization Chapter 3 : Functions of Several Variables (Part 1). Presented by: Rajesh Roy Networks Research Lab, University of California, Davis June 18, 2010. Introduction. x is a vector of design variables of dimension N No constraints on x

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## Engineering Optimization Chapter 3 : Functions of Several Variables (Part 1)

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### Engineering OptimizationChapter 3 : Functions of Several Variables (Part 1)

Presented by:

Rajesh Roy

Networks Research Lab,

University of California, Davis

June 18, 2010

Introduction
• x is a vector of design variables of dimension N
• No constraints on x
• ƒ is a scalar objective function
• ƒ and its derivatives exist and are continuous everywhere
• We will be satisfied to identify local optima x*

Static Question

Test candidate points to see whether they are (or are not) minima, maxima, saddlepoints, or none of the above.

Dynamic Question

Given x(0), a point that does not satisfy the above-mentioned optimality criteria, what is a better estimate x(1) of the solution x*?

Introduction

The nonlinear objective ƒ will typically not be convex and therefore will be multimodal.

Example:

Optimality Criteria

We examine optimality criteria for basically two reasons:

(1) because they are necessary to recognize solutions

(2) because they provide motivation for most of the useful methods

Consider the Taylor expansion of a function of several variables:

Necessary and Sufficient Conditions

Necessary Conditions:

Sufficient Conditions:

Dynamic Question : Searching x*

The methods can be classified into three broad categories:

• Direct-search methods, which use only function values
• The S2 (Simplex Search) Method
• Hooke–Jeeves Pattern Search Method
• Powell’s Conjugate Direction Method
• 2. Gradient methods, which require estimates of the first derivative of ƒ(x)
• Cauchy’s Method
• 3. Second-order methods, which require estimates of the first and second derivatives of ƒ(x)
• Newton’s Method
• …….

Motivation behind different methods:

• Available computer storage is limited
• Function evaluations are very time consuming
• Great accuracy in the final solution is desired
• Sometimes its either impossible or else very time consuming to obtain analytical expressions for derivatives
The S2 (Simplex Search) Method

Set up a regular simplex* in the space of the independent variables and evaluate the function at each vertex.

The vertex with highest functional value is located.

This ‘‘worst’’ vertex is then reflected through the centroid to generate a new point, which is used to complete the next simplex

Reflection:

Suppose x(j) is the point to be reflected. Then the centroid of the

remaining N points is

All points on the line from x( j) through xc are given by

New Vertex Point:

*In N dimensions, a regular simplex is a polyhedron composed of N+1 equidistant points, which form its vertices.

Hooke-Jeeves Pattern Search Method

Exploratory Moves:

Given a specified step size the exploration proceeds from an initial point by the specified step size in each coordinate direction.

If the function value does not increase, the step is considered successful.

Otherwise, the step is retracted and replaced by a step in the opposite direction, which in turn is retained depending upon whether it succeeds or fails.

Pattern Moves:

Single step from the present base point along the line from the previous to the current base point.

Powell’s Conjugate Direction Method
• Given a quadratic function q(x), two arbitrary but distinct points x(1) and x(2), and a direction d, if y(1) is the solution to min q(x(1), d) and y(2) is the solution to min q(x(2), d), then the direction y(2)–y(1) is C conjugate to d.

Theorem :

If a quadratic function in N variables can be transformed so that it is just the sum of perfect

squares, then the optimum can be found after exactly N single-variable searches, one with respect to each of the transformed variables.

All of the methods considered here employ a similar iteration procedure:

Cauchy’s Method

Taylor expansion of the objective about x:

The greatest negative scalar product results from the choice

This is the motivation for the simple gradient method:

Example:

Newton’s Method

Consider again the Taylor expansion of the objective:

We form a quadratic approximation to ƒ(x) by dropping terms of order 3

forcing x(k1), the next point in the sequence, to be a point where the gradient of the approximation is zero. Therefore,

So according to Newton’s optimization method: