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Partly Convex and Convex-Monotonic Optimization Problems
2005A class of nonconvex optimization problems is studied that exhibits partial convexity combined with partial monotonicity. To exploit this particular hybrid structure a natural approach is to use a branch and bound scheme with branching performed on the nonconvex variables and bounds computed by lagrangian or convex relaxation.
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2009
Optimization is a central theme of applied mathematics that involves minimizing or maximizing various quantities. This is an important application of the derivative tests in calculus. In addition to the first and second derivative tests of one-variable calculus, there is the powerful technique of Lagrange multipliers in several variables.
Kenneth R. Davidson, Allan P. Donsig
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Optimization is a central theme of applied mathematics that involves minimizing or maximizing various quantities. This is an important application of the derivative tests in calculus. In addition to the first and second derivative tests of one-variable calculus, there is the powerful technique of Lagrange multipliers in several variables.
Kenneth R. Davidson, Allan P. Donsig
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Lectures on modern convex optimization - analysis, algorithms, and engineering applications
MPS-SIAM series on optimization, 2001A. Ben-Tal +1 more
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Online Learning and Online Convex Optimization
Found. Trends Mach. Learn., 2012S. Shalev-Shwartz
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Learning Convex Optimization Models
IEEE/CAA Journal of Automatica Sinica, 2021Akshay Agrawal +2 more
exaly
IEEE Transactions on Neural Networks and Learning Systems, 2020
Yanling Zheng, Jianquan Lu, Jinde Cao
exaly
Yanling Zheng, Jianquan Lu, Jinde Cao
exaly
Optimization of Convex Risk Functions
Mathematics of Operations Research, 2006Andrzej Ruszczynski, Alexander Shapiro
exaly