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With application of convex reformulation and piecewise wise relaxation, the problem can be reformulated as a convex MINLP model, in which the objective function is convex and all constraints are linear.
Our problem can be reformulated as follows.
The considered problem can be reformulated as follows.
This problem can be reformulated in the abstract setting (1).
Also, by flipping the sign, the minimization problem can be reformulated to a maximization problem.
The classical direction finding problem can be reformulated as a sparse representation problem.
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It has long been known that variational inequality problems can be reformulated as nonsmooth equations.
Base on these results, all interval inequality constraints of these problems can be reformulated into stochastic equivalent forms.
These optimal design problems can be reformulated as convex optimization form as the second-order cone programming and solved efficiently via the well-established interior point method.
A point (xin X) such that (x=Tx) is called a fixed point of T. Many problems can be reformulated to the problem of finding a fixed point of a certain mapping.
Construction of fixed points of nonlinear mappings is a classical and active area of nonlinear functional analysis due to the fact that many nonlinear problems can be reformulated as fixed point equations of nonlinear mappings.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com