نتایج جستجو برای: linear optimization
تعداد نتایج: 767084 فیلتر نتایج به سال:
This note establishes a limiting formula for the conic Lagrangian dual of convex infinite optimization problem, correcting classical version Karney [Math. Programming 27 (1983) 75-82] semi-infinite programs. A reformulation problem with single constraint leads to corresponding dual, called sup-dual, and also primal in case when strong Slater condition holds, which entails sup-duality.
Optimization of non-linear performance functionals subject to constraints is a typical problem in control and there exist many diierent optimization methods. These methods, however, can take a long time to converge to optimal solutions. This paper presents a modiied interior point algorithm that optimizes a class of performance func-tionals possessing both linear and non-linear characteristics....
In this paper the problem of obtaining the degree of flexibility that maximizes the total profit in an existing process flowsheet is addressed. Assuming a linear model for the process and given probability distribution functions for the uncertain parameters, the curve relating the expected revenue to the flexibility index is generated. An efficient stochastic optimization method is developed fo...
We present in this paper a linear programming framework to address freeway control applications such as ramp metering. After showing the equivalence between the LWR model and a linear optimization problem, several extensions are introduced to model the ramp queues and the capacity drop phenomenon. A wide range of objective functions which are relevant in traffic engineering are then introduced ...
Tempelmeier and Hilger (2015) study the stochastic dynamic lot sizing problem with multiple items and limited capacity. They propose a linear optimization formulation for the problem based on a piece-wise linear approximation of the nonlinear functions for the expected backorders and the expected inventory position. Our work builds on Tempelmeier and Hilger (2015). We correct an erroneous deriv...
Most research in robust optimization has so far been focused on inequality-only, convex conic programming with simple linear models for uncertain parameters. Many practical optimization problems, however, are nonlinear and non-convex. Even in linear programming, coefficients may still be nonlinear functions of uncertain parameters. In this paper, we propose robust formulations (see (1) versus (...
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