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If you have any questions, you are always welcome to contact us. We'll get back to you as soon as possible, withing 24 hours on weekdays.
Customer service
All questions about your order, return and delivery must be sent to our customer service team by e-mail at yourstore@yourdomain.com
Sale & Press
If you are interested in selling our products, need more information about our brand or wish to make a collaboration, please contact us at press@yourdomain.com
Random Constraint Satisfaction Problems (CSPs) are ubiquitous in computer science and everyday life, including examples ranging from Sudokus to optimal digital board design. A CSP involves many discrete variables interacting through random constraints. When the number of competing conditions gets large, the optimization of a CSP instance can become extraordinarily hard. The Survey Propagation algorithm, based on the iterative exchange of simple probabilistic messages along the edges of a factor graph, succeeds to optimize even very hard random instances, whereas more standard algorithms fail dramatically. After a thorough discussion of the typicalcase complexity of the random KSatisfiability Problem and of its relation with Statistical Physics, the Survey Propagation algorithm is introduced and explained in detail, together with some of its powerful variants and distributed implementations. Beyond optimization, lossy data compression based on the selective targeting and retrieval of specific solutions is discussed, thus showing how Survey Propagation can be used to turn the complexity itself of hard CSPs resolution into a computational resource of a novel kind.
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birth defects, or other reproductive harm.
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⚠️ California Proposition 65 Warning: Some products sold on this website may expose you to chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm. For more information, visit www.P65Warnings.ca.gov.
