By Richard Dawkins, Lawrence M. Krauss

ISBN-10: 1451624476

ISBN-13: 9781451624472

Author note: Afterword through Richard Dawkins

Bestselling writer and acclaimed physicist Lawrence Krauss bargains a paradigm-shifting view of the way every thing that exists got here to be within the first place.

"Where did the universe come from? What was once there ahead of it? what is going to the longer term deliver? and eventually, why is there anything instead of nothing?"

One of the few popular scientists at the present time to have crossed the chasm among technology and pop culture, Krauss describes the staggeringly appealing experimental observations and mind-bending new theories that exhibit not just can whatever come up from not anything, whatever will regularly come up from not anything. With a brand new preface in regards to the value of the invention of the Higgs particle, A Universe from not anything makes use of Krauss's attribute wry humor and fantastically transparent causes to take us again to the start of the start, providing the latest proof for the way our universe evolved—and the consequences for a way it's going to end.

Provocative, hard, and delightfully readable, this can be a game-changing examine the main simple underpinning of lifestyles and a strong antidote to superseded philosophical, spiritual, and clinical pondering.

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Additional info for A Universe from Nothing: Why There is Something Rather than Nothing

Example text

The objective function is evaluated on a stencil determined by a set of directions that are suitable from geometric or algebraic point of view. The initial grid is subsequently modified (in particular, refined) during the optimization run. The overall concept of the pattern search approach (Kolda et al. 2003) is shown in Fig. 2, where—for the sake of illustration—the optimization process is restricted to the rectangular grid and thus explores a grid-restricted vicinity of the current design. If the operations performed on the current grid fail to lead to improvement of the objective function, the grid is refined to allow smaller steps.

Select parent individuals S from P. 4. Apply recombination operators to create a new population P from parent individuals S. 5. Apply mutation operators to introduce local perturbations in individuals of P. 6. If termination condition is not satisfied go to 2. 7. END. In most cases, the population is initialized randomly. The “fitness” value assigned to an individual in Step 2 is usually strictly related to the value of the objective function to be minimized. , only the best individuals are selected, ES) or partially random (with higher probability of being selected assigned to better individuals but nonzero even for the poor ones; GAs, EAs).

N hk ðxÞ ¼ 0, k ¼ 1, . . , M ð3:15Þ ð3:16Þ The set of points that satisfy the above constraints is referred to as a feasible region. 16) state that—at the local optimum x* of f—there exist constants μ1, . , μN, and λ1, λ2, . , λM, such that N M À Á X À Á X À Á ∇f x* þ μk ∇gk x* þ λk ∇hk x* ¼ 0 k¼1 ð3:17Þ k¼1 and gk(x*) 0, μkgk(x*) ¼ 0, where μk ! 0 for k ¼ 1, . , N, and provided that all the functions (both objective and constraints) are continuously differentiable. 17) is called the Lagrangian function, whereas coefficients μ and λ are the Lagrange multipliers.

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A Universe from Nothing: Why There is Something Rather than Nothing by Richard Dawkins, Lawrence M. Krauss

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