MC Optimization

Recursive Integration

Monte Carlo Maximization

EM Algorithm

Simulated Annealing

Fundamental idea: A change of scale, called **temperature**, allows for faster moves on the surface of the function of **energy**.

$h$

to maximize, whose negative is called It is important to note that if the larger

$T$

is, accepting one decreasing is more likely.Example: To maximize

$h(x)=[\cos(50x)+\sin(20x)]^2$

.We can use the following Julia code to solve this problem:

r = 0.5

function T(t)

return 1/log(t)

end

# target function

function h(x)

return (cos(50x) + sin(20x))^2

end

N = 2500

x = ones(N)

y = ones(N)

for t = 1:(N-1)

# step 1

at = max(x[t]-r, 0)

bt = min(x[t]+r, 1)

u = rand() * (bt - at) + at

# step 2

rho = min(exp( (h(u) - h(x[t])) / T(t) ), 1)

if rand() < rho

x[t+1] = u

y[t+1] = h(u)

else

x[t+1] = x[t]

y[t+1] = y[t]

end

end

The trajectory of 2500 pairs

$(x^{(t)}, y^{(t)})$

isLast modified 3yr ago