LEADER 00941aam a2200253 i 4500 001 991001267839707536 008 110615s2005 it 000 0 ita d 020 $a8874510918 035 $ab13987690-39ule_inst 040 $aDip.to Studi Storici$bita 082 04$a352.045211 100 1 $aLandoni, Enrico$d<1980- >$0475219 245 13$aIl Comune riformista :$bMilano, 1975-1985 /$cEnrico Landoni 246 14$aComune riformista : le giunte di sinistra al governo di Milano, 1975-1985 260 $aMilano :$bM&B,$c[2005] 300 $a423 p. ;$c21 cm. 440 0$aSaggi 651 4$aMilano$xAmministrazione$y1975-1985 907 $a.b13987690$b28-01-14$c15-06-11 912 $a991001267839707536 945 $aLE023 352.045 LAN 1 1 $g1$i2023000127940$lle023$o-$pE15.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i15286824$z22-06-11 996 $aComune riformista$9247667 997 $aUNISALENTO 998 $ale023$b05-07-11$cm$da $e-$fita$git $h3$i0 LEADER 04784nam 22005775 450 001 9910254296603321 005 20200704065810.0 010 $a3-319-58356-5 024 7 $a10.1007/978-3-319-58356-3 035 $a(CKB)4100000001381568 035 $a(DE-He213)978-3-319-58356-3 035 $a(MiAaPQ)EBC5182414 035 $a(PPN)222228466 035 $a(EXLCZ)994100000001381568 100 $a20171204d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aContinuous Nonlinear Optimization for Engineering Applications in GAMS Technology /$fby Neculai Andrei 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XXIV, 506 p. 68 illus., 66 illus. in color.) 225 1 $aSpringer Optimization and Its Applications,$x1931-6828 ;$v121 311 $a3-319-58355-7 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $a1. Introduction -- 2. Mathematical modeling using algebraically oriented languages for nonlinear optimization -- 3. Introduction to GAMS technology -- 4. Applications of continuous nonlinear optimization -- 5. Optimality conditions for continuous nonlinear optimization -- 6. Simple bound constraint optimization -- 7. Penalty and augmented Langrangian methods -- 8. Penalty-Barrier Algorithm -- 9. Linearly Constrained Augmented Lagrangian -- 10. Quadratic programming -- 11. Sequential quadratic programming -- 12. A SQP Method using only Equalit Constrained Sub-problem -- 12. A Sequential Quadratic Programming Algorithm with Successive Error Restoration -- 14. Active-set Sequential Linear-Quadratic Programming -- 15. A SQP algorithm for Large-Scale Constrained Optimization -- 16. Generalized Reduced Gradient with sequential linearization -- 17. Interior point methods -- 18. Filter methods -- 19. Interior Point Sequential Linear-Quadratic Programming -- 20. Interior Point Filer Line-Search IPOPT -- 21. Numerical studies. 330 $aThis book presents the theoretical details and computational performances of algorithms used for solving continuous nonlinear optimization applications imbedded in GAMS. Aimed toward scientists and graduate students who utilize optimization methods to model and solve problems in mathematical programming, operations research, business, engineering, and industry, this book enables readers with a background in nonlinear optimization and linear algebra to use GAMS technology to understand and utilize its important capabilities to optimize algorithms for modeling and solving complex, large-scale, continuous nonlinear optimization problems or applications. Beginning with an overview of constrained nonlinear optimization methods, this book moves on to illustrate key aspects of mathematical modeling through modeling technologies based on algebraically oriented modeling languages. Next, the main feature of GAMS, an algebraically oriented language that allows for high-level algebraic representation of mathematical optimization models, is introduced to model and solve continuous nonlinear optimization applications. More than 15 real nonlinear optimization applications in algebraic and GAMS representation are presented which are used to illustrate the performances of the algorithms described in this book. Theoretical and computational results, methods, and techniques effective for solving nonlinear optimization problems, are detailed through the algorithms MINOS, KNITRO, CONOPT, SNOPT and IPOPT which work in GAMS technology. 410 0$aSpringer Optimization and Its Applications,$x1931-6828 ;$v121 606 $aMathematical optimization 606 $aMathematical models 606 $aAlgorithms 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 606 $aMathematical Modeling and Industrial Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/M14068 606 $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018 615 0$aMathematical optimization. 615 0$aMathematical models. 615 0$aAlgorithms. 615 14$aOptimization. 615 24$aMathematical Modeling and Industrial Mathematics. 615 24$aAlgorithms. 676 $a519.3 700 $aAndrei$b Neculai$4aut$4http://id.loc.gov/vocabulary/relators/aut$0767620 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254296603321 996 $aContinuous Nonlinear Optimization for Engineering Applications in GAMS Technology$91563046 997 $aUNINA