cs b553: a lgorithms for o ptimization and l earning univariate optimization
TRANSCRIPT
CS B553: ALGORITHMS FOR OPTIMIZATION AND LEARNINGUnivariate optimization
x
f(x)
KEY IDEAS
Critical points Direct methods
Exhaustive search Golden section search
Root finding algorithms Bisection [More next time]
Local vs. global optimization Analyzing errors, convergence rates
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f(x)Local maxima
Local minimaInflection point
Figure 1
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f(x)
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Figure 2a
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f(x)
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Find critical points, apply 2nd derivative test
Figure 2b
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f(x)
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Figure 2b
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f(x)
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Global minimum must be one of these points
Figure 2c
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f(x)
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Exhaustive grid search
Figure 3
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Exhaustive grid search
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f(x)
Two types of errors
x* xt
f(xt)
f(x*)
Geometric error
Analy
tica
l err
or
Figure 4
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f(x)
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Does exhaustive grid search achieve e/2 geometric error?
e
x*
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f(x)
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Does exhaustive grid searchachieve e/2 geometric error?
Not necessarily for multi-modal objective functions
Error
x*
LIPSCHITZ CONTINUITYSlope +K
Slope -K
|f(x)-f(y)| K|x-y|
Figure 5
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f(x)
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Exhaustive grid search achieves Ke/2 analytical error in worst case
e
Figure 6
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f(x)
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Golden section search
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Bracket [a,b]Intermediate point m with f(m) < f(a),f(b)
Figure 7a
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f(x)
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Golden section search
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Candidate bracket 1 [a,m]
c
Candidate bracket 2 [c,b]
Figure 7b
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f(x)
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Golden section search
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Figure 7b
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f(x)
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Golden section search
mc
Figure 7b
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f(x)
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Golden section search
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Figure 7b
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f(x)
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Optimal choice: based on golden ratio
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Choose c so that (c-a)/(m-c) = , where is the golden ratio=> Bracket reduced by a factor of -1 at each step
c
NOTES
Exhaustive search is a global optimization: error bound is for finding the true optimum
GSS is a local optimization: error bound holds only for finding a local minimum
Convergence rate is linear: with xn = sequence of bracket midpoints
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f(x)
Root finding: find x-value where f’(x) crosses 0
f’(x)
Figure 8
Bisection
g(x)
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Bracket [a,b]Invariant: sign(f(a)) != sign(f(b))
Figure 9a
Bisection
g(x)
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Bracket [a,b]Invariant: sign(f(a)) != sign(f(b))
m
Figure 9
Bisection
g(x)
a b
Bracket [a,b]Invariant: sign(f(a)) != sign(f(b))
Figure 9
Bisection
g(x)
a b
Bracket [a,b]Invariant: sign(f(a)) != sign(f(b))
m
Figure 9
Bisection
g(x)
a b
Bracket [a,b]Invariant: sign(f(a)) != sign(f(b))
Figure 9
Bisection
g(x)
a b
Bracket [a,b]Invariant: sign(f(a)) != sign(f(b))
m
Figure 9
Bisection
g(x)
a b
Bracket [a,b]Invariant: sign(f(a)) != sign(f(b))
Linear convergence: Bracket size is reduced by factor of 0.5 at each iteration
Figure 9
NEXT TIME
Root finding methods with superlinear convergence
Practical issues