nevergrad/optimization/optimizerlib.py [2777:2793]:
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            and p.helpers.Normalizer(self.parametrization).fully_bounded
        ):
            if (
                self.budget > 5000 * self.dimension
            ):  # Asymptotically let us trust NGOpt36 and its subtle restart.
                return NGOpt36
            if self.dimension < 5:  # Low dimension: let us hit the bounds.
                return NGOpt21
            if self.dimension < 10:  # Moderate dimension: reasonable restart + bet and run.
                num = 1 + int(np.sqrt(8.0 * (8 * self.budget) // (self.dimension * 1000)))
                return ConfPortfolio(optimizers=[NGOpt14] * num, warmup_ratio=0.7)
            if self.dimension < 20:  # Nobody knows why this seems to be so good.
                num = self.budget // (500 * self.dimension)
                return ConfPortfolio(
                    optimizers=[Rescaled(base_optimizer=NGOpt14, scale=1.3 ** i) for i in range(num)],
                    warmup_ratio=0.5,
                )
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nevergrad/optimization/optimizerlib.py [2849:2865]:
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            and p.helpers.Normalizer(self.parametrization).fully_bounded
        ):
            if (
                self.budget > 5000 * self.dimension
            ):  # Asymptotically let us trust NGOpt36 and its subtle restart.
                return NGOpt36
            if self.dimension < 5:  # Low dimension: let us hit the bounds.
                return NGOpt21
            if self.dimension < 10:  # Moderate dimension: reasonable restart + bet and run.
                num = 1 + int(np.sqrt(8.0 * (8 * self.budget) // (self.dimension * 1000)))
                return ConfPortfolio(optimizers=[NGOpt14] * num, warmup_ratio=0.7)
            if self.dimension < 20:  # Nobody knows why this seems to be so good.
                num = self.budget // (500 * self.dimension)
                return ConfPortfolio(
                    optimizers=[Rescaled(base_optimizer=NGOpt14, scale=1.3 ** i) for i in range(num)],
                    warmup_ratio=0.5,
                )
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