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The issue of learning a hash function can be formulated as a task of learning l binary classifiers.
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Thus the search for altitude functions can be formulated as a convex programming problem [55].
By these means, the demand utility function can be formulated as the sum of the products of some binary variables and a bounded continuous variable.
The objective function can be formulated as.
Its R-D function can be formulated as follows: (1).
the global energy minimization function can be formulated as (3).
To achieve competitive fairness and high efficiency, the utility function can be formulated as (10).
The fitting function can be formulated as: l ( t ) = η ∗ t ε, (5) Figure 2 The growth of populated locations.
The pooling problem (unbalanced version) under our first cost function can be formulated as the well-known max n-cut problem as follows.
The procedure can be formulated as follows: (8).
The primal problem can be formulated as follows by incorporating the discriminant function (3): (5) s.t.
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