In quantitative decision analysis, an analyst applies mathematical models to make decisions. Frequently these models involve an optimization problem to determine the values of the decision variables, a system </spa...In quantitative decision analysis, an analyst applies mathematical models to make decisions. Frequently these models involve an optimization problem to determine the values of the decision variables, a system </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> of possibly non</span></span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">li</span><span style="font-family:Verdana;">near inequalities and equalities to restrict these variables, or both. In this</span><span style="font-family:""><span style="font-family:Verdana;"> note, </span><span style="font-family:Verdana;">we relate a general nonlinear programming problem to such a system </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> in</span><span style="font-family:Verdana;"> such </span><span style="font-family:Verdana;">a way as to provide a solution of either by solving the other—with certain l</span><span style="font-family:Verdana;">imitations. We first start with </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> and generalize phase 1 of the two-phase simplex method to either solve </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> or establish that a solution does not exist. A conclusion is reached by trying to solve </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> by minimizing a sum of artificial variables subject to the system </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> as constraints. Using examples, we illustrate </span><span style="font-family:Verdana;">how this approach can give the core of a cooperative game and an equili</span><span style="font-family:Verdana;">brium for a noncooperative game, as well as solve both linear and nonlinear goal programming problems. Similarly, we start with a general nonlinear programming problem and present an algorithm to solve it as a series of systems </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> by generalizing the </span></span><span style="font-family:Verdana;">“</span><span style="font-family:Verdana;">sliding objective</span><span style="font-family:Verdana;"> function </span><span style="font-family:Verdana;">method</span><span style="font-family:Verdana;">”</span><span style="font-family:Verdana;"> for</span><span style="font-family:Verdana;"> two-dimensional linear programming. An example is presented to illustrate the geometrical nature of this approach.展开更多
文摘In quantitative decision analysis, an analyst applies mathematical models to make decisions. Frequently these models involve an optimization problem to determine the values of the decision variables, a system </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> of possibly non</span></span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">li</span><span style="font-family:Verdana;">near inequalities and equalities to restrict these variables, or both. In this</span><span style="font-family:""><span style="font-family:Verdana;"> note, </span><span style="font-family:Verdana;">we relate a general nonlinear programming problem to such a system </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> in</span><span style="font-family:Verdana;"> such </span><span style="font-family:Verdana;">a way as to provide a solution of either by solving the other—with certain l</span><span style="font-family:Verdana;">imitations. We first start with </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> and generalize phase 1 of the two-phase simplex method to either solve </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> or establish that a solution does not exist. A conclusion is reached by trying to solve </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> by minimizing a sum of artificial variables subject to the system </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> as constraints. Using examples, we illustrate </span><span style="font-family:Verdana;">how this approach can give the core of a cooperative game and an equili</span><span style="font-family:Verdana;">brium for a noncooperative game, as well as solve both linear and nonlinear goal programming problems. Similarly, we start with a general nonlinear programming problem and present an algorithm to solve it as a series of systems </span><i><span style="font-family:Verdana;">S</span></i><span style="font-family:Verdana;"> by generalizing the </span></span><span style="font-family:Verdana;">“</span><span style="font-family:Verdana;">sliding objective</span><span style="font-family:Verdana;"> function </span><span style="font-family:Verdana;">method</span><span style="font-family:Verdana;">”</span><span style="font-family:Verdana;"> for</span><span style="font-family:Verdana;"> two-dimensional linear programming. An example is presented to illustrate the geometrical nature of this approach.