An unconstrained convex programming dual approach to a class of linearly-constrained entropy maximization problem with a quadric cost and its applications to transportation planning problems
Institute of Transportation Studies, University of California at Berkeley, 1993 - Mathematics - 38 pages
2 pages matching gradient vector in this book
Results 1-2 of 2
What people are saying - Write a review
We haven't found any reviews in the usual places.
achieves a finite Applications to Transportation coefficients computational experience constraint matrix curved search method define destinations diagonal element diagonal matrix djhj(w djhj(w)+n dratic scale dual optimal solution efficient encompasses many existing Entropy Maximization Problem entropy term equality constraints equation 9 fifth test problems full row-rank global convergence gradient vector Hessian matrix Institute of Transportation interior feasible solution iterations tends linear cost linear scale Linearly-Constrained Entropy Maximization magnitude matrix inversion Moreover North Carolina number of iterations optimization model paper parameters performance with respect primal optimal solution problem size problem with quadratic Program PM pure entropy optimization quadratic cost quadratic rate quadratic scale quadratic term rate of convergence robustness scale goes sensitivity solving stop the algorithm strictly concave strong duality theorem Supercomputing tends to increase Transportation Planning Problems Transportation Studies trip distribution problem ulnxj unconstrained convex optimization Unconstrained Convex Programming unconstrained dual University of California w e Rm