000			01140nam a22001937a 4500
008			220502b ||||| |||| 00| 0 eng d
020			_a9781886529281 (hb.)
082			_a519.6 _bBER
100			_aBertsekas, Dimitri P. _9809
245			_aConvex optimization algorithms
260			_aMassachusetts _bAthena Scientific _c2015
300			_axii, 564p.,
500			_ahttp://www.athenasc.com/convexalgorithms.html
520			_aThis book aims at an up-to-date and accessible development of algorithms for solving convex optimization problems. The book covers almost all the major classes of convex optimization algorithms. Principal among these are gradient, subgradient, polyhedral approximation, proximal, and interior point methods. Most of these methods rely on convexity (but not necessarily differentiability) in the cost and constraint functions, and are often connected in various ways to duality. The book contains numerous examples describing in detail applications to specially structured problems.
650			_aMathematical optimization _9688
650			_aConvex functions _91132
650			_aAlgorithms _91133
942			_cBK
999			_c7864 _d7864