An Online and Approximate Solver for POMDPs with Continuous
... then find the action that maximizes the mean value. Now, key to the aforementioned advances in solving POMDPs is the use of sampling to trade optimality with approximate optimality for speed. While sampling tends to work well for estimating the mean, finding a good estimate for the max (or min) is m ...
... then find the action that maximizes the mean value. Now, key to the aforementioned advances in solving POMDPs is the use of sampling to trade optimality with approximate optimality for speed. While sampling tends to work well for estimating the mean, finding a good estimate for the max (or min) is m ...
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[SE4] Integral simplex using decomposition for the set partitioning
... notation introduced by Balas and Padberg (1972, 1975), and Raymond et al. (2010). We discuss these results for ′ , the linear relaxation of the set partitioning problem, although they were developed for a general linear programming problem. The following notation is used (please note the I and J u ...
... notation introduced by Balas and Padberg (1972, 1975), and Raymond et al. (2010). We discuss these results for ′ , the linear relaxation of the set partitioning problem, although they were developed for a general linear programming problem. The following notation is used (please note the I and J u ...
IOSR Journal of Mathematics (IOSR-JM) e-ISSN: 2278-5728, p-ISSN:2319-765X.
... Expected discounted penalty function analyses the behaviour of the insurer‟s surplus. So it has always been of interest for years in mathematical insurance and many authors have been investigating on the different parameters of this function. The seminal paper by Gerber and Shiu in 1998 gave a detai ...
... Expected discounted penalty function analyses the behaviour of the insurer‟s surplus. So it has always been of interest for years in mathematical insurance and many authors have been investigating on the different parameters of this function. The seminal paper by Gerber and Shiu in 1998 gave a detai ...
Document
... To solve some problems in which we want to find two quantities, it is useful to perform the following five steps: Step 1: Define each variable. For each quantity that we are trying to find, we usually define a variable to be that unknown quantity. Step 2: Write a system of two equations. We find a s ...
... To solve some problems in which we want to find two quantities, it is useful to perform the following five steps: Step 1: Define each variable. For each quantity that we are trying to find, we usually define a variable to be that unknown quantity. Step 2: Write a system of two equations. We find a s ...
Design of Traffic Grooming Optical Virtual Private Networks Obeying
... were investigated in [4]. A new model for dynamic resource allocation was presented in [5]. The impact of the optical switches and amplifiers was also included in [6]. A new constraint-based routing, which includes the PMD and optical signal-to-noise ratio (OSNR), is presented in [7]. In a very rece ...
... were investigated in [4]. A new model for dynamic resource allocation was presented in [5]. The impact of the optical switches and amplifiers was also included in [6]. A new constraint-based routing, which includes the PMD and optical signal-to-noise ratio (OSNR), is presented in [7]. In a very rece ...
Mathematical optimization
In mathematics, computer science and operations research, mathematical optimization (alternatively, optimization or mathematical programming) is the selection of a best element (with regard to some criteria) from some set of available alternatives.In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. More generally, optimization includes finding ""best available"" values of some objective function given a defined domain (or a set of constraints), including a variety of different types of objective functions and different types of domains.