## Information-based global optimization of f’, a quasi-Newton local optimization of f.

## Choose initial point x1

## Locally optimize x1 to x2, f’(x1)=f’(x2)=f(x2)

## Iterate:

- Pick next point x1 according to g and previous f’ points
- Locally optimize x1 to x2, f’(x1)=f’(x2)=f(x2)

## Complexity of g still grows with each iteration

## How to limit point taken into consideration?

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