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s Chapter 1 Introduction to the Formulation of Optimization Problems
Constrained Problems by Unconstrained Minimization
2 other sections not shown
active constraints algorithm analysis apply approach approximation assume basic feasible solution block Chapter components conjugate gradient method consider constraint functions constraint surfaces contours convergence convex criterion cycle defined derivatives design problem design variables direction-finding problem discussed eigenvalue equations example feasible region Figure finite difference flow diagram formula function evaluations given idea inequality interior penalty function interpolation iteration Kuhn-Tucker conditions linear programming matrix method of feasible mini minimization methods minimizing step minimum point negative nonlinear nonlinear programming Note objective function obtain optimization problem optimum design penalty function method pivot positive definite possible post office parcel Powell's method produce programming problem projection matrix quadratic quantities relative minima requires satisfied scalar shown in Fig simple situation slack variables solve starting point steepest descent straints stress structure Taylor series technique tion trapezoidal rule unconstrained minimization vector Xq+i zero