Convex OptimizationFrom the publisher. Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interiorpoint methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics. 
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A good resource, intuitive, fluent and understandable...materials are in perfect logical order...written so cohesively that you can read all it at once.
Contents
II  1 
IV  4 
V  7 
VI  9 
VII  11 
VIII  14 
IX  16 
X  19 
LXII  397 
LXIII  402 
LXIV  405 
LXV  410 
LXVI  416 
LXVII  422 
LXVIII  432 
LXIX  438 
XI  21 
XII  27 
XIII  35 
XIV  43 
XV  46 
XVI  51 
XVII  59 
XVIII  60 
XIX  67 
XX  79 
XXI  90 
XXII  95 
XXIII  104 
XXIV  108 
XXV  112 
XXVI  113 
XXVII  127 
XXVIII  136 
XXIX  146 
XXX  152 
XXXI  160 
XXXII  167 
XXXIII  174 
XXXIV  188 
XXXV  189 
XXXVI  215 
XXXVII  223 
XXXVIII  232 
XXXIX  237 
XL  241 
XLI  249 
XLII  253 
XLIII  258 
XLIV  264 
XLV  272 
XLVI  273 
XLVII  289 
XLVIII  291 
XLIX  302 
L  305 
LI  318 
LII  324 
LIII  343 
LIV  344 
LV  351 
LVI  359 
LVII  364 
LVIII  374 
LIX  384 
LX  392 
LXI  393 
LXX  446 
LXXI  447 
LXXII  455 
LXXIII  457 
LXXIV  463 
LXXV  466 
LXXVI  475 
LXXVII  484 
LXXVIII  496 
LXXIX  508 
LXXX  513 
LXXXI  514 
LXXXII  521 
LXXXIII  525 
LXXXIV  531 
LXXXV  542 
LXXXVI  556 
LXXXVII  557 
LXXXVIII  561 
LXXXIX  562 
XC  568 
XCI  579 
XCII  585 
XCIII  596 
XCIV  609 
XCV  615 
XCVI  621 
XCVII  623 
XCVIII  631 
XCIX  633 
C  637 
CI  639 
CII  640 
CIII  645 
CIV  652 
CV  653 
CVI  655 
CVII  656 
CVIII  657 
659  
CX  661 
CXI  664 
CXII  668 
CXIII  672 
CXIV  681 
684  
685  
CXVII  697 
701  
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Common terms and phrases
affine function algorithm analytic center approximation problem assume Axnt backtracking line search barrier method Cholesky factorization concave concave function condition number cone consider constraint functions convergence convex function convex optimization problem convex set cost defined denote detector dual feasible dual function dual problem duality gap eigenvalue ellipsoid equality constraints Euclidean example expressed feasible point Figure flops G Rn geometric given gradient Hessian hyperplane inequality constraint infeasible start Newton interiorpoint methods interpretation iterations Lagrange dual leastsquares problem line search linear equations linear inequalities linear programming logconcave logarithm lower bound matrix maximize maximum Newton's method nonnegative nonzero norm number of Newton objective function optimal point optimal value parameter Pareto optimal penalty function polyhedron polynomial primal problem minimize proper cone quadratic quasiconvex quasiconvex function residual satisfies scalar Schur complement Show solution solve strong duality subject to Ax subject to fi(x sublevel sets Suppose zero