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These findings call for nontrivial constraints to be incorporated into learning rules, such as split constraints for ON- and OFF-center afferents, to allow learning models to develop simple-cell receptive fields [121].
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The proposed technique splits constraints of an optimization problem into "soft" (manageable) and "rigid" constraints and modifies the "soft" constraints in an interactive mode with the expert.
The demand mode splitting constraints may be defined by all or nothing mode assignment or like it is proposed by us in this paper by Logit distribution.
Differential evolution is employed as a solution approach due to its distinctive feature, and a new crossover method and a new mutation method are brought forward in the global search procedure, according to the job splitting constraint.
Applying Theorems 2.2 and 4.1, Lemma 4.1, we can find the unique minimum norm common solution of fixed point and split equilibrium constraints and the solution of mathematical programming with fixed point and split equilibrium constraints.
By Theorem 3.3, we obtain mathematical programming with fixed point and multiple sets split feasibility constraints.
Applying Theorems 2.2 and 4.1, Lemma 4.1, we can find the minimum norm solution of (MSEP) and mathematical programming with fixed point and multiple sets split equilibrium constraints.
This point x ¯ is also a unique solution of the mathematical programming with fixed point and split feasibility constraints: min q ∈ Fix ( T ) ∩ Ω h ( q ).
This point x ¯ is also a unique solution of the mathematical programming with fixed point and multiple sets split feasibility constraints: min q ∈ Fix ( T ) ∩ Ω h ( q ).
We can apply Theorem 4.1 to study the mathematical programming of a quadratic function with fixed point and multiple sets split feasibility constraints.
This point x ¯ is also a unique minimum norm common solution of fixed point and split equilibrium constraints: Find min q ∈ Fix ( T ) ∩ Ω ∥ q ∥.
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