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The VMP is then reformulated as a linear constrained regulation problem with additive disturbances (DLCRP).

Such a scheme is then reformulated as a quadratic program (QP).

The scheme is finally reformulated as a quadratic-programming (QP) problem.

The control problem can now be reformulated as a linear matrix inequality (LMI) problem.

A design of the fault-tolerant compensation controller is reformulated as a linear matrix inequality problem.

The optimality conditions for the problem are reformulated as a local design rule.

The control problem can then be reformulated as a linear matrix inequality (LMI) problem.

The discrete-time variant of this task is commonly reformulated as a regression problem.

Consequently, this objective function can be reformulated as a convex function using the function.

The nonlinear Schrödinger equation (1) can be reformulated as a multi-symplectic Hamiltonian system [9].

Through an application of Karush-Kuhn-Tucker conditions, the problem is reformulated as a convex one.

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