LEADER 01742oam 2200457 a 450 001 9910704508703321 005 20130520102457.0 035 $a(CKB)5470000002442622 035 $a(OCoLC)173276605$z(OCoLC)173276540 035 $a(EXLCZ)995470000002442622 100 $a20070925d2006 ua 0 101 0 $aeng 135 $aurmn||||a|||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAquatox (release 2.2)$b[electronic resource] $emodeling environmental fate and ecological effects in aquatic ecosystems : technical documentation (addendum) 210 1$a[Washington, D.C.] :$cU.S. Environmental Protection Agency, Office of Water, Office of Science and Technology,$d[2006] 215 $a1 online resource (3, 18 pages) 300 $aTitle from title screen (viewed Sept. 25, 2007). 300 $a"October 2006." 300 $a"EPA-823-R-06-008." 517 $aAquatox 606 $aWater$xPollution$zUnited States$xAnalysis$vInteractive multimedia 606 $aAquatic ecology$zUnited States$xAnalysis$vInteractive multimedia 606 $aAquatic organisms$xEffect of water pollution on$zUnited States$vSoftware 606 $aWater$xPollution$zUnited States$xAnalysis$vSoftware 615 0$aWater$xPollution$xAnalysis 615 0$aAquatic ecology$xAnalysis 615 0$aAquatic organisms$xEffect of water pollution on 615 0$aWater$xPollution$xAnalysis 712 02$aUnited States.$bEnvironmental Protection Agency.$bOffice of Water. 801 0$bEJB 801 1$bEJB 801 2$bOCLCQ 801 2$bOCLCA 801 2$bGPO 906 $aBOOK 912 $a9910704508703321 996 $aAquatox release 2.2$93100048 997 $aUNINA LEADER 02169nam0 22004573i 450 001 VAN0249931 005 20230531122436.141 017 70$2N$a9783030603007 100 $a20220909d2020 |0itac50 ba 101 $aeng 102 $aCH 105 $a|||| ||||| 200 1 $aˆThe ‰Projected Subgradient Algorithm in Convex Optimization$fAlexander J. Zaslavski 210 $aCham$cSpringer$d2020 215 $avi, 146 p.$cill.$d24 cm 410 1$1001VAN0102834$12001 $aSpringerBriefs in optimization$1210 $aBerlin [etc.]$cSpringer 500 1$3VAN0249933$aˆThe ‰Projected Subgradient Algorithm in Convex Optimization$92909498 606 $a90C25$xConvex programming [MSC 2020]$3VANC019709$2MF 606 $a90C30$xNonlinear programming [MSC 2020]$3VANC023183$2MF 606 $a49M37$xNumerical methods based on nonlinear programming [MSC 2020]$3VANC023193$2MF 606 $a90C26$xNonconvex programming, global optimization [MSC 2020]$3VANC026769$2MF 606 $a65K05$xNumerical mathematical programming methods [MSC 2020]$3VANC028868$2MF 610 $aConvex Optimization$9KW:K 610 $aInfluence computational errors$9KW:K 610 $aNonsmooth convex optimization$9KW:K 610 $aOptimization problems bounded sets$9KW:K 610 $aProjected subgradient algorithm$9KW:K 610 $aQuasi-convex optimization$9KW:K 610 $aSubgradient algorithm$9KW:K 610 $aZero-sum games$9KW:K 620 $aCH$dCham$3VANL001889 700 1$aZaslavski$bAlexander J.$3VANV080282$0721713 712 $aSpringer $3VANV108073$4650 801 $aIT$bSOL$c20240614$gRICA 856 4 $uhttp://doi.org/10.1007/978-3-030-60300-7$zE-book ? Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$1IT-CE0120$2VAN08 912 $fN 912 $aVAN0249931 950 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$d08CONS e-book 4889 $e08eMF4889 20220909 996 $aProjected Subgradient Algorithm in Convex Optimization$92909498 997 $aUNICAMPANIA LEADER 04819nam 2200625 a 450 001 9911006781103321 005 20200520144314.0 010 $a1-282-25804-4 010 $a9786612258046 010 $a0-08-092125-6 035 $a(CKB)1000000000790331 035 $a(EBL)453121 035 $a(OCoLC)505428281 035 $a(SSID)ssj0000337102 035 $a(PQKBManifestationID)11251884 035 $a(PQKBTitleCode)TC0000337102 035 $a(PQKBWorkID)10287904 035 $a(PQKB)11616389 035 $a(MiAaPQ)EBC453121 035 $a(CaSebORM)9780080921259 035 $a(EXLCZ)991000000000790331 100 $a20090316d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEmbedded systems and software validation /$fAbhik Roychoudhury 210 $aAmsterdam ;$aBoston $cMorgan Kaufmann Publishers/Elsevier$dc2009 215 $a1 online resource (267 p.) 225 1 $aThe Morgan Kaufmann series in systems on silicon 300 $aDescription based upon print version of record. 311 $a0-12-374230-7 320 $aIncludes bibliographical references (p. 233-239) and index. 327 $aFront Cover; Embedded Systems and Software Validation; Copyright Page; Dedication Page; Table of Contents; Acknowledgments; Preface; Chapter 1. Introduction; Chapter 2. Model Validation; 2.1 Platform versus System Behavior; 2.2 Criteria for Design Model; 2.3 Informal Requirements: A Case Study; 2.3.1 The Requirements Document; 2.3.2 Simplification of the Informal Requirements; 2.4 Common Modeling Notations; 2.4.1 Finite-State Machines; 2.4.2 Communicating FSMs; 2.4.3 Message Sequence Chart-Based Models; 2.5 Remarks About Modeling Notations; 2.6 Model Simulations; 2.6.1 FSM Simulations 327 $a2.6.2 Simulating MSC-Based System Models2.7 Model-Based Testing; 2.8 Model Checking; 2.8.1 Property Specification; 2.8.2 Checking Procedure; 2.9 The SPIN Validation Tool; 2.10 The SMV Validation Tool; 2.11 Case Study: Air-Traffic Controller; 2.12 References; 2.13 Exercises; Chapter 3. Communication Validation; 3.1 Common Incompatibilities; 3.1.1 Sending/Receiving Signals in Different Order; 3.1.2 Handling a Different Signal Alphabet; 3.1.3 Mismatch in Data Format; 3.1.4 Mismatch in Data Rates; 3.2 Converter Synthesis; 3.2.1 Representing Native Protocols and Converters 327 $a3.2.2 Basic Ideas for Converter Synthesis3.2.3 Various Strategies for Protocol Conversion; 3.2.4 Avoiding No-Progress Cycles; 3.2.5 Speculative Transmission to Avoid Deadlocks; 3.3 Changing a Working Design; 3.4 References; 3.5 Exercises; Chapter 4. Performance Validation; 4.1 The Conventional Abstraction of Time; 4.2 Predicting Execution Time of a Program; 4.2.1 WCET Calculation; 4.2.2 Modeling of Microarchitecture; 4.3 Interference within a Processing Element; 4.3.1 Interrupts from Environment; 4.3.2 Contention and Preemption; 4.3.3 Sharing a Processor Cache 327 $a4.4 System-Level Communication Analysis4.5 Designing Systems with Predictable Timing; 4.5.1 Scratchpad Memories; 4.5.2 Time-Triggered Communication; 4.6 Emerging Applications; 4.7 References; 4.8 Exercises; Chapter 5. Functionality Validation; 5.1 Dynamic or Trace-Based Checking; 5.1.1 Dynamic Slicing; 5.1.2 Fault Localization; 5.1.3 Directed Testing Methods; 5.2 Formal Verification; 5.2.1 Predicate Abstraction; 5.2.2 Software Checking via Predicate Abstraction; 5.2.3 Combining Formal Verification with Testing; 5.3 References; 5.4 Exercises; Bibliography; Index 330 $a Modern embedded systems require high performance, low cost and low power consumption. Such systems typically consist of a heterogeneous collection of processors, specialized memory subsystems, and partially programmable or fixed-function components. This heterogeneity, coupled with issues such as hardware/software partitioning, mapping, scheduling, etc., leads to a large number of design possibilities, making performance debugging and validation of such systems a difficult problem. Embedded systems are used to control safety critical applications such as flight control, automotive el 410 0$aMorgan Kaufmann series in systems on silicon. 606 $aEmbedded computer systems$xDesign and construction 606 $aEmbedded computer systems$xTesting 606 $aComputer software$xTesting 615 0$aEmbedded computer systems$xDesign and construction. 615 0$aEmbedded computer systems$xTesting. 615 0$aComputer software$xTesting. 676 $a004.1 700 $aRoychoudhury$b Abhik$01823890 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911006781103321 996 $aEmbedded systems and software validation$94390832 997 $aUNINA