1 Lines 2 Linear systems of equations
... Existence of a Solution Consider a linear programming problem with the set R of feasible points and objective function z = Ax + By. 1. If R is bounded, then z has both a maximum and a minimum value on R. 2. If R is unbounded and A ≥ 0, B ≥ 0, and the constraints include x ≥ 0 and y ≥ 0, then z has ...
... Existence of a Solution Consider a linear programming problem with the set R of feasible points and objective function z = Ax + By. 1. If R is bounded, then z has both a maximum and a minimum value on R. 2. If R is unbounded and A ≥ 0, B ≥ 0, and the constraints include x ≥ 0 and y ≥ 0, then z has ...
Data mining and decision support
... • Desired properties of search methods: – high probability of finding near-optimal solutions (effectiveness) – short processing time (efficiency) • They are usually conflicting; a compromise is offered by stochastic techniques where certain steps are based on random choice • Many stochastic search t ...
... • Desired properties of search methods: – high probability of finding near-optimal solutions (effectiveness) – short processing time (efficiency) • They are usually conflicting; a compromise is offered by stochastic techniques where certain steps are based on random choice • Many stochastic search t ...
Satisfied with Physics - Cornell Computer Science
... of constraints to variables (3). Kso introduce a novel strategy for finding SAT problems with a small α valsolutions to this problem. Phase change in 3-SAT. Plotted is the probability that a Satisfiability (SAT) is a logical reason- 3-SAT problem has at least one satisfying assignment as ue almost a ...
... of constraints to variables (3). Kso introduce a novel strategy for finding SAT problems with a small α valsolutions to this problem. Phase change in 3-SAT. Plotted is the probability that a Satisfiability (SAT) is a logical reason- 3-SAT problem has at least one satisfying assignment as ue almost a ...
Hidden Markov Models
... probability of observation sequence Viterbi algorithm - inductive algorithm that keeps the best state sequence at each instance ...
... probability of observation sequence Viterbi algorithm - inductive algorithm that keeps the best state sequence at each instance ...
Multiple-criteria decision analysis
Multiple-criteria decision-making or multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly considers multiple criteria in decision-making environments. Whether in our daily lives or in professional settings, there are typically multiple conflicting criteria that need to be evaluated in making decisions. Cost or price is usually one of the main criteria. Some measure of quality is typically another criterion that is in conflict with the cost. In purchasing a car, cost, comfort, safety, and fuel economy may be some of the main criteria we consider. It is unusual that the cheapest car is the most comfortable and the safest one. In portfolio management, we are interested in getting high returns but at the same time reducing our risks. Again, the stocks that have the potential of bringing high returns typically also carry high risks of losing money. In a service industry, customer satisfaction and the cost of providing service are two conflicting criteria that would be useful to consider.In our daily lives, we usually weigh multiple criteria implicitly and we may be comfortable with the consequences of such decisions that are made based on only intuition. On the other hand, when stakes are high, it is important to properly structure the problem and explicitly evaluate multiple criteria. In making the decision of whether to build a nuclear power plant or not, and where to build it, there are not only very complex issues involving multiple criteria, but there are also multiple parties who are deeply affected from the consequences.Structuring complex problems well and considering multiple criteria explicitly leads to more informed and better decisions. There have been important advances in this field since the start of the modern multiple-criteria decision-making discipline in the early 1960s. A variety of approaches and methods, many implemented by specialized decision-making software, have been developed for their application in an array of disciplines, ranging from politics and business to the environment and energy.