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Our proposed method is goal geometric programming with logarithmic deviational variables.
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One of the other assistance methods is goal assistance.
In Table 2, the four most often used individual level methods are: Goal Setting, Facilitation, Feedback, and Consciousness Raising.
The RL method is a goal-directed learning of a mapping from situations to actions without relying on exemplary supervision or complete models of the environment.
The exemplar NED-2 DSS developed using this method is a goal-driven DSS that integrates a sophisticated inventory system, treatment plan development, growth-and-yield models, wildlife models, fire risk models, knowledge based systems for goal satisfaction analysis, and a powerful report generation system.
Adding the coulomb potential (here for r < R C ; R C = spherical nucleus radius) and solution of the Schrodinger equation by Nikiforov Uvarov method is the main goal of the present work.
Goal attainment method is actually a variation of goal programming method intending to minimize the maximum weighted deviation from the goals.
The weighted infinite norm method is a reference-goal method that can conveniently determine a trade-off solution if the lower and upper bounds of each objective value are known in advance.
All steps for this method is same as the goal programming method, but in goal attainment method the distance between objective functions and ideal point (target point) is calculated in infinity norm distance.
However, our analytic method is presented with the goal of developing it to address unresovled problems in -dimensional Hilbert spaces which serve as a setting for spherical codes, erasure channel modeling, and other aspects of communications theory.
Number of iterations for goal attainment and goal programming method is 100. .
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