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| | garylarizza.com
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| | This year at Puppetconf 2014, I presented a 1.5 hour talk entitled "The Refactor Dance" that comprised nearly EVERYTHING that I've ...
| | stevenygard.com
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| | I've added universal binary support to class-dump. It will choose the host architecture, if available, or the first architecture otherwise. ...
| | foodfightshow.org
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| | Show Date: 26 April 2013 This is another FoodShipShow Joint Production! Join us as we wrap-up #ChefConf 2013. You can now subscribe to the Food Fight ...
| | francisbach.com
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| [AI summary] The blog post discusses non-convex quadratic optimization problems and their solutions, including the use of strong duality, semidefinite programming (SDP) relaxations, and efficient algorithms. It highlights the importance of these problems in machine learning and optimization, particularly for non-convex problems where strong duality holds. The post also mentions the equivalence between certain non-convex problems and their convex relaxations, such as SDP, and provides examples of when these relaxations are tight or not. Key concepts include the role of eigenvalues in quadratic optimization, the use of Lagrange multipliers, and the application of methods like Newton-Raphson for solving these problems. The author also acknowledges contributions...