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Classical Optimization Methods and Properties
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absolute maximum algorithm approximating problem assume basic feasible solution basic solution basic variables beginning of period Chapter columns components computational concave function Consider control variables convex function convex set decision problems demand Denote determine discussed dual dynamic programming e-neighborhood enter the basis equations exists expected cost extreme point finite number fj(xj given global maximum global optimum gradient method gradient projection method hence hyperplane integer integer linear programming inventory Lagrange multiplier linear programming problem machine matrix maximize minimize minimum nonlinear programming problems Note number of steps objective function obtained optimal solution optimal value parameters positive possible quadratic programming problem quantity random variables relative maximum saddle point Section sequential decision set of feasible simplex method solution to 8-1 solve the problem stochastic programming strict equality Suppose surplus variables tableau technique units vector x'Dx yield zero