Posterior constraint optimal selection techniques (COSTs) are developed for nonnegative linear programming problems (NNLPs), and a geometric interpretation is provided. The posterior approach is used in both a dynamic...Posterior constraint optimal selection techniques (COSTs) are developed for nonnegative linear programming problems (NNLPs), and a geometric interpretation is provided. The posterior approach is used in both a dynamic and non-dynamic active-set framework. The computational performance of these methods is compared with the CPLEX standard linear programming algorithms, with two most-violated constraint approaches, and with previously developed COST algorithms for large-scale problems.展开更多
A scalar equilibrium (SE) is defined for n-person prescriptive games in normal form. When a decision criterion (notion of rationality) is either agreed upon by the players or prescribed by an external arbiter, the res...A scalar equilibrium (SE) is defined for n-person prescriptive games in normal form. When a decision criterion (notion of rationality) is either agreed upon by the players or prescribed by an external arbiter, the resulting decision process is modeled by a suitable scalar transformation (utility function). Each n-tuple of von Neumann-Morgenstern utilities is transformed into a nonnegative scalar value between 0 and 1. Any n-tuple yielding a largest scalar value determines an SE, which is always a pure strategy profile. SEs can be computed much faster than Nash equilibria, for example;and the decision criterion need not be based on the players’ selfishness. To illustrate the SE, we define a compromise equilibrium, establish its Pareto optimality, and present examples comparing it to other solution concepts.展开更多
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
We present here an alternative definition of the P-value for statistical hypothesis test of a real-valued parameter for a continuous random variable X. Our approach uses neither the notion of Type I error nor the assu...We present here an alternative definition of the P-value for statistical hypothesis test of a real-valued parameter for a continuous random variable X. Our approach uses neither the notion of Type I error nor the assumption that null hypothesis is true. Instead, the new P-value involves the maximum likelihood estimator, which is usually available for a parameter such as the mean μ or standard deviation σ of a random variable X with a common distribution.展开更多
文摘Posterior constraint optimal selection techniques (COSTs) are developed for nonnegative linear programming problems (NNLPs), and a geometric interpretation is provided. The posterior approach is used in both a dynamic and non-dynamic active-set framework. The computational performance of these methods is compared with the CPLEX standard linear programming algorithms, with two most-violated constraint approaches, and with previously developed COST algorithms for large-scale problems.
文摘A scalar equilibrium (SE) is defined for n-person prescriptive games in normal form. When a decision criterion (notion of rationality) is either agreed upon by the players or prescribed by an external arbiter, the resulting decision process is modeled by a suitable scalar transformation (utility function). Each n-tuple of von Neumann-Morgenstern utilities is transformed into a nonnegative scalar value between 0 and 1. Any n-tuple yielding a largest scalar value determines an SE, which is always a pure strategy profile. SEs can be computed much faster than Nash equilibria, for example;and the decision criterion need not be based on the players’ selfishness. To illustrate the SE, we define a compromise equilibrium, establish its Pareto optimality, and present examples comparing it to other solution concepts.
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.
文摘We present here an alternative definition of the P-value for statistical hypothesis test of a real-valued parameter for a continuous random variable X. Our approach uses neither the notion of Type I error nor the assumption that null hypothesis is true. Instead, the new P-value involves the maximum likelihood estimator, which is usually available for a parameter such as the mean μ or standard deviation σ of a random variable X with a common distribution.
