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A gradient projection method is described for incorporating multiple constraints in the problem.
Additionally, the new CMD algorithm has the ability to handle multiple constraints in both gain-constrained and power-constrained applications.
Multiple constraints in SPMs are considered a problem that can be solved in a nondeterministic polynomial time.
We first propose the general projection gradient method for solving energy functional minimization problem under multiple constraints, in which the energy functional takes real functions as independent variables.
The optimal search model mentioned in section 2.3 by (11) could be regarded as a single objective function with multiple constraints in essence.
For supporting the approach, we develop a novel algorithm that is designed to solve the data allocations under multiple constraints in a polynomial time.
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This topology optimization technique can handle multiple constraints easily in the framework of level set method and at the same time preserve the signed distance property of the level set functions.
However, multiple constraints exist in extrapolating these observations forwards to any biological significance.
As an optimization tool, the Taguchi method is adopted due to its simplicity and cost-effectiveness both in formulating an objective function and in satisfying multiple constraints simultaneously.
We present the development of a new adaptive filter algorithm that uses a penalty function formulation to place multiple constraints on the filter directly in the frequency domain.
Adaptive backstepping control combined with a novel dynamic control allocation scheme is developed for attitude tracking control of a rigid spacecraft, in which multiple constraints are simultaneously considered, such as actuator uncertainties including torque magnitude deviation and misalignment, actuator constraints like saturation, external disturbances and even constrained energy consumption.
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