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Exact(3)
Thus it is to maximize a concave function.
Let us consider an operation of a wireless data network with the objective to minimize a convex cost function (or to maximize a concave utility function).
On the other hand, the PRA problem can be formulated as a convex program because it has the same linear constraints as the MRA problem and the objective is to maximize a concave utility function.
Similar(57)
Since the sum and the minimum operations preserve concavity, the objective is concave, and maximizing a concave function yields a convex optimization problem.
Thus we are maximizing a concave function with the convex set.
The solutions that can be implemented by our game forms are those that maximize a monotonic and quasi-concave function of utilities after normalizing each agent's utility function so that the maximum utility is 1 and the utility of the disagreement outcome is 0.
The overall goal is to devise autonomous network control schemes which maximize an overall increasing concave utility function U. The utility function is defined as the sum of sector utility functions U m, which are in turn defined over average user rates X ̄ m.
Indeed, the saddle-point of L are characterized by (we assume all the functions we minimize are convex and all the functions we maximize are concave) begin{aligned} {left{ begin{array}{ll} Lambda Y=0& text{ optimality } text{ of } X), Lambda ^t X-nabla H(Y)=0 & text{ optimality } text{ of } Y),end{array}right.
Equivalently, we formulate a restless multi-armed bandit problem in which we seek to maximize concave functions of the time average reward vector.
The sufficient condition is as follows: The objective function must be convex for maximizing and concave for minimizing problems.
Talk about maximizing an opportunity.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com