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Merton's portfolio problem is a problem in continuous-time finance and in particular intertemporal portfolio choice.An investor must choose how much to consume and must allocate their wealth between stocks and a risk-free asset so as to maximize expected utility.
In Computer Science, Optimal Computing Budget Allocation (OCBA) is a simulation optimization method designed to maximize the Probability of Correct Selection (PCS) while minimizing computational costs. First introduced by Dr. Chun-Hung Chen in the mid-1990s, OCBA determines how many simulation runs (or how much computational time) or the number ...
The function f is variously called an objective function, criterion function, loss function, cost function (minimization), [8] utility function or fitness function (maximization), or, in certain fields, an energy function or energy functional. A feasible solution that minimizes (or maximizes) the objective function is called an optimal solution.
Let Xk be an allocation in the k-replicated economy where all copies of the same agent receive the same bundle as the original agent in X. The allocation X is called sigma-optimal if for every k, the allocation Xk is Pareto-optimal. Lemma: [7]: 528 An allocation is sigma-optimal, if-and-only-if it is a competitive equilibrium.
An additive agent has a utility function that is an additive set function: for every additive agent i and item j, there is a value ,, such that () =, for every set Z of items. When all agents are additive, welfare maximization can be done by a simple polynomial-time algorithm: give each item j to an agent for whom v i , j {\displaystyle v_{i,j ...
Allocation efficiency occurs when there is an optimal distribution of goods and services, considering consumer's preference. When the price equals marginal cost of production, the allocation efficiency is at the output level. This is because the optimal distribution is achieved when the marginal utility of good equals the marginal cost.
Of particular use is the property that for any fixed set of ~ values, the optimal result to the Lagrangian relaxation problem will be no smaller than the optimal result to the original problem. To see this, let x ^ {\displaystyle {\hat {x}}} be the optimal solution to the original problem, and let x ¯ {\displaystyle {\bar {x}}} be the optimal ...
This diagram shows an example corner solution where the optimal bundle lies on the x-intercept at point (M,0). IC 1 is not a solution as it does not fully utilise the entire budget, IC 3 is unachievable as it exceeds the total amount of the budget. The optimal solution in this example is M units of good X and 0 units of good Y.