Convex Optimization, Part 1"The focus of the book is on recognizing and formulating convex optimization problems, and then solving them efficiently. It contains many worked examples and homework exercises and will appeal to students, researchers, and practitioners in fields such as engineering, computer science, mathematics, finance, and economics."BOOK JACKET. 
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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 
LXIV  397 
LXVI  402 
LXVII  405 
LXVIII  410 
LXIX  416 
LXX  422 
LXXI  432 
LXXII  438 
XI  21 
XIII  27 
XIV  35 
XV  43 
XVI  46 
XVII  51 
XVIII  59 
XIX  60 
XX  67 
XXI  79 
XXII  90 
XXIII  95 
XXIV  104 
XXV  108 
XXVI  112 
XXVII  113 
XXVIII  127 
XXIX  136 
XXX  146 
XXXI  152 
XXXII  160 
XXXIII  167 
XXXIV  174 
XXXV  188 
XXXVI  189 
XXXVII  215 
XXXIX  223 
XL  232 
XLI  237 
XLII  241 
XLIII  249 
XLIV  253 
XLV  258 
XLVI  264 
XLVII  272 
XLVIII  273 
XLIX  289 
L  291 
LI  302 
LII  305 
LIII  318 
LIV  324 
LV  343 
LVI  344 
LVII  351 
LVIII  359 
LIX  364 
LX  374 
LXI  384 
LXII  392 
LXIII  393 
LXXIII  446 
LXXIV  447 
LXXV  455 
LXXVI  457 
LXXVII  463 
LXXVIII  466 
LXXIX  475 
LXXX  484 
LXXXI  496 
LXXXII  508 
LXXXIII  513 
LXXXIV  514 
LXXXV  521 
LXXXVI  525 
LXXXVII  531 
LXXXVIII  542 
LXXXIX  556 
XC  557 
XCI  561 
XCII  562 
XCIII  568 
XCIV  579 
XCV  585 
XCVI  596 
XCVII  609 
XCVIII  615 
XCIX  621 
C  623 
CI  631 
CII  633 
CIII  637 
CIV  639 
CV  640 
CVI  645 
CVII  652 
CVIII  653 
CIX  655 
CX  656 
CXI  657 
659  
CXIII  661 
CXIV  664 
CXV  668 
CXVI  672 
CXVII  681 
684  
685  
CXX  697 
701  
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Common terms and phrases
affine algorithm applied associated assume block bound called closed compute concave condition cone consider constraints convergence convex function convex optimization convex optimization problem cost defined definite denote derivative described differentiable direction distance distribution domain dual duality ellipsoid equality constraints equations equivalent error estimate Euclidean example exercise exists expressed factorization feasible fi(x Figure flops fo(x function geometric given gives gradient holds hyperplane inequality infeasible interpretation iterations leastsquares line search linear lower matrix maximize maximum means method Newton step Newton's method nonnegative norm objective obtain optimal value original parameter penalty positive primal probability problem minimize programming quadratic quasiconvex random residual result satisfies selfconcordant semidefinite programming separating Show shown simple solution solve standard strictly strong Suppose variables vector zero