CHAPTER 6 STOCHASTIC APPROXIMATION AND THE FINITEDIFFERENCE METHOD

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CHAPTER 6STOCHASTIC APPROXIMATION AND THE
FINITE-DIFFERENCE METHOD
Slides for Introduction to Stochastic Search and
Optimization (ISSO) by J. C. Spall

• Organization of chapter in ISSO
• Contrast of gradient-based and gradient-free
  algorithms
• Motivating examples
• Finite-difference algorithm
• Convergence theory
• Asymptotic normality
• Selection of gain sequences
• Numerical examples
• Extensions and segue to SPSA in Chapter 7

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Motivation for AlgorithmsNot Requiring Gradient
of Loss Function

• Primary interest here is in optimization problems
  for which we cannot obtain direct measurements of
  ?L/?q
• cannot use techniques such as Robbins-Monro SA,
  steepest descent, etc.
• can (in principle) use techniques such as Kiefer
  and Wolfowitz SA (Chapter 6), genetic algorithms
  (Chapters 910),
• Many such gradient-free problems arise in
  practice
• Generic difficult parameter estimation
• Model-free feedback control
• Simulation-based optimization
• Experimental design sensor configuration

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Model-Free Control Setup (Example 6.2 in ISSO)
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Finite Difference SA (FDSA) Method

• FDSA has standard first-order form of
  root-finding (Robbins-Monro) SA
• Finite difference approximation replaces direct
  gradient measurement (Chap. 5)
• Resulting algorithm sometimes called
  Kiefer-Wolfowitz SA
• Let denote FD estimate of g(?) at kth
  iteration (next slide)
• Let denote estimate for ? at kth iteration
• FDSA algorithm has form
• where ak is nonnegative gain value
• Under conditions, ? ?? in stochastic sense
  (a.s.)

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Finite Difference Gradient Approximation

• Classical method for approximating gradients in
  Kiefer-Wolfowitz SA is by finite differences
• FD gradient approximation used in SA recursion as
  gradient measurement (previous slide)
• Standard two-sided gradient approximation at
  iteration k is
• where ?j is p-dimensional with 1 in j?th
  entry, 0 elsewhere
• Each computation of FD approximation takes 2p
  measurements y()

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Example Wastewater Treatment Problem (Example
6.5 in ISSO)

• Small-scale problem with p 2
• Aim is to optimize water cleanliness and methane
  gas byproduct
• Evaluated algorithms with 50 realizations of N
  2000 measurements
• Used FDSA with gains ak a/(1 k) and ck 1/(1
  k)1/6
• Asymptotically optimal decay rates found best
• Gain tuning chooses a naïve gain sets a 1
• Also compared with random search algorithm B from
  Chapter 2
• Algorithms use noisy loss measurements (same
  level as in Example 2.7 in ISSO)

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Mean values of ? L(??) with 95
Confidence Intervals
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Example Skewed-Quartic Loss Function(Examples
6.6 and 6.7 in ISSO)

• Larger-scale problem with p 10
• (?)i is the i th component of B?, and pB is an
  upper triangular matrix of ones
• Used N 1000 measurements 50 replications
• Used FDSA with gains ak a/(1kA)? and ck
  c/(1k)?
• Semi-automatic and manual gain tuning
• Also compared with random search algorithm B

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Algorithm Comparison with Skewed-Quartic Loss
Function (p 10) (Example 6.6 in ISSO)
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Example with Skewed-Quartic Loss Mean
Terminal Values and 95 Confidence


Intervals for