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{ "item_title" : "Exploiting Structure in Non-Convex Quadratic Optimization and Gas Network Planning Under Uncertainty", "item_author" : [" Jonas Schweiger "], "item_description" : "The amazing success of computational mathematical optimization over the last decades has been driven more by insights into mathematical structures than by the advance of computing technology. In this vein, Jonas Schweiger addresses applications, where nonconvexity in the model and uncertainty in the data pose principal difficulties. In the first part, he contributes strong relaxations for non-convex problems such as the non-convex quadratic programming and the Pooling Problem. In the second part, he contributes a robust model for gas transport network extension and a custom decomposition approach. All results are backed by extensive computational studies.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/83/254/667/3832546677_b.jpg", "price_data" : { "retail_price" : "59.00", "online_price" : "59.00", "our_price" : "59.00", "club_price" : "59.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Exploiting Structure in Non-Convex Quadratic Optimization and Gas Network Planning Under Uncertainty|Jonas Schweiger

Exploiting Structure in Non-Convex Quadratic Optimization and Gas Network Planning Under Uncertainty

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Overview

The amazing success of computational mathematical optimization over the last decades has been driven more by insights into mathematical structures than by the advance of computing technology. In this vein, Jonas Schweiger addresses applications, where nonconvexity in the model and uncertainty in the data pose principal difficulties. In the first part, he contributes strong relaxations for non-convex problems such as the non-convex quadratic programming and the Pooling Problem. In the second part, he contributes a robust model for gas transport network extension and a custom decomposition approach. All results are backed by extensive computational studies.

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Details

  • ISBN-13: 9783832546670
  • ISBN-10: 3832546677
  • Publisher: Logos Verlag Berlin
  • Publish Date: April 2018
  • Dimensions: 8.04 x 5.68 x 0.48 inches
  • Shipping Weight: 1.15 pounds
  • Page Count: 202

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