方法证据记录
Convex Optimization
Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets. Formalized and popularized by Stephen Boyd and Lieven Vandenberghe in their landmark 2004 textbook, the framework unifies a wide family of problems — including linear programming, quadratic programming, semidefinite programming, and second-order cone programming — under a single theoretical roof. Its defining property is that any locally optimal solution is also globally optimal, making it tractable and reliable for engineering, statistics, machine learning, and operations research.
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Convex Optimization
分类方法记录 · process-pipeline / optimization
- Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press. · ISBN 978-0-521-83378-3
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