Bayesian and High-Dimensional Global Optimization

Name: Bayesian and High-Dimensional Global Optimization
Brand: Springer Nature
SKU: 9783030647117
Availability: InStock

Best Price:
Buy Bayesian and High-Dimensional Global Optimization for $54.99 at @ Link.springer.com
No coupon is required — this is the standard retail price.

Set a price drop alert to never miss an offer.

1 Offer Price Range: $54.99 - $54.99

BEST PRICE

Single Product Purchase

$54.99

@ Link.springer.com BUY Now

Price Comparison

Seller	Contact Seller	List Price	On Sale	Shipping	Best Promo	Final Price	Volume Discount	Financing	Availability	Seller's Page
BEST PRICE 1 Product Purchase @ Link.springer.com		$54.99	$54.99			$54.99		See Site	In stock	Visit Store

Product Details

Brand

Springer Nature

Manufacturer

N/A

Part Number

GTIN

9783030647117

Condition

New

Product Description

Accessible to a variety of readers, this book is of interest to specialists, graduate students and researchers in mathematics, optimization, computer science, operations research, management science, engineering and other applied areas interested in solving optimization problems. Basic principles, potential and boundaries of applicability of stochastic global optimization techniques are examined in this book. A variety of issues that face specialists in global optimization are explored, such as multidimensional spaces which are frequently ignored by researchers. The importance of precise interpretation of the mathematical results in assessments of optimization methods is demonstrated through examples of convergence in probability of random search. Methodological issues concerning construction and applicability of stochastic global optimization methods are discussed, including the one-step optimal average improvement method based on a statistical model of the objective function. A significant portion of this book is devoted to an analysis of high-dimensional global optimization problems and the so-called curse of dimensionality. An examination of the three different classes of high-dimensional optimization problems, the geometry of high-dimensional balls and cubes, very slow convergence of global random search algorithms in large-dimensional problems , and poor uniformity of the uniformly distributed sequences of points are included in this book.

Available Colors

N/A

Available Sizes

N/A