LEADER 02866oam 22006375 450 001 9910781055603321 005 20200520144314.0 010 $a1-282-35577-5 010 $a9786612355776 010 $a0-8213-7875-9 024 7 $a10.1596/978-0-8213-7874-8 035 $a(CKB)2550000000005686 035 $a(EBL)476234 035 $a(OCoLC)495092286 035 $a(SSID)ssj0000086182 035 $a(PQKBManifestationID)11998641 035 $a(PQKBTitleCode)TC0000086182 035 $a(PQKBWorkID)10025165 035 $a(PQKB)11360344 035 $a(MiAaPQ)EBC476234 035 $a(Au-PeEL)EBL476234 035 $a(CaPaEBR)ebr10354183 035 $a(CaONFJC)MIL235577 035 $a(The World Bank)ocn297146467 035 $a(US-djbf)15754320 035 $a(EXLCZ)992550000000005686 100 $a20090528d2009 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aDebt relief and beyond : $elessons learned and challenges ahead /$fedited by Carlos A. Primo Braga, Dorte Domeland 210 1$aWashington, D.C. :$cWorld Bank,$dc2009. 215 $axxii, 451 pages $cillustrations ;$d23 cm 300 $a"This book is the outcome of a conference titled 'Debt relief and beyond: a World Bank conference on debt and development,' held in October 2008 at the World Bank in Washington, D.C."--Preface. 311 $a0-8213-7874-0 320 $aIncludes bibliographical references and index. 327 $aContents; Preface; Acknowledgments; Contributors; Abbreviations; Introduction; Part I: Debt Relief; Boxes; Figures; Tables; Part II: Debt Sustainability; Part III: Odious Debt; Part IV: Debt Management; Index 330 $aThe history of debt relief goes back several decades. It reveals that a country's accumulation of unsustainable debt stems from such factors as deficiencies in macroeconomic management, adverse terms-of-trade shocks, and poor governance. Debt-relief initiatives have provided debt-burdened countries with the opportunity for a fresh start, but whether the benefits of debt relief can be preserved depends on transformations in a country's policies and institutions.In 1996, the Heavily Indebted Poor Countries (HIPC) Initiative was launched as the first comprehensive, multilateral, debt-relief frame 410 0$aWorld Bank e-Library. 606 $aDebt relief$zDeveloping countries 615 0$aDebt relief 676 $a336.3/435091724 701 $aBraga$b Carlos Alberto Primo$f1954-$011722 701 $aDo?meland$b Do?rte$f1971-$01465116 712 02$aWorld Bank. 801 0$bDLC 801 1$bDLC 801 2$bYDX 801 2$bCUV 801 2$bDLC 906 $aBOOK 912 $a9910781055603321 996 $aDebt relief and beyond$93674985 997 $aUNINA LEADER 03697nam 2200685z- 450 001 9910557107803321 005 20210501 035 $a(CKB)5400000000040969 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69429 035 $a(oapen)doab69429 035 $a(EXLCZ)995400000000040969 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (116 p.) 311 08$a3-03943-835-2 311 08$a3-03943-836-0 330 $aThe aim of this book was to collect the most recent methods developed for NSO and its practical applications. The book contains seven papers: The first is the foreword by the Guest Editors giving a brief review of NSO and its real-life applications and acknowledging the outstanding contributions of Professor Adil Bagirov to both the theoretical and practical aspects of NSO. The second paper introduces a new and very efficient algorithm for solving uncertain unit-commitment (UC) problems. The third paper proposes a new nonsmooth version of the generalized damped Gauss-Newton method for solving nonlinear complementarity problems. In the fourth paper, the abs-linear representation of piecewise linear functions is extended to yield simultaneously their DC decomposition as well as the pair of generalized gradients. The fifth paper presents the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and nonsmooth optimization problems in many practical applications. In the sixth paper, a problem concerning the scheduling of nuclear waste disposal is modeled as a nonsmooth multiobjective mixed-integer nonlinear optimization problem, and a novel method using the two-slope parameterized achievement scalarizing functions is introduced. Finally, the last paper considers binary classification of a multiple instance learning problem and formulates the learning problem as a nonconvex nonsmooth unconstrained optimization problem with a DC objective function. 606 $aInformation technology industries$2bicssc 610 $aabs-linearization 610 $aachievement scalarizing functions 610 $aasynchronous computing 610 $aB-differential 610 $abiased-randomized algorithms 610 $aDC function 610 $aDC optimization 610 $aDCA 610 $aGauss-Newton method 610 $aglobal convergence 610 $aheuristics 610 $ainteractive method 610 $alevel decomposition 610 $amultiobjective optimization 610 $amultiple instance learning 610 $an/a 610 $anon-smooth optimization 610 $anonlinear complementarity problem 610 $anonsmooth equations 610 $anonsmooth optimization 610 $aparallel computing 610 $asoft constraints 610 $aspent nuclear fuel disposal 610 $astochastic hydrothermal UC problem 610 $astochastic programming 610 $asuperlinear convergence 610 $asupport vector machine 615 7$aInformation technology industries 700 $aKarmitsa$b Napsu$4edt$01296135 702 $aTaheri$b Sona$4edt 702 $aKarmitsa$b Napsu$4oth 702 $aTaheri$b Sona$4oth 906 $aBOOK 912 $a9910557107803321 996 $aNonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov$93023795 997 $aUNINA