02503nam2 22005173i 450 VAN0029041520250401110108.833N978366202796720250401d1993 |0itac50 baengDE|||| |||||i e bcrˆ1: ‰FundamentalsJean-Baptiste Hiriart-Hurruty, Claude LemarechalBerlinHeidelbe : Sger-Verlag, 3. -II, 4001VAN000241072001 Grundlehren der mathematischen WissenschaftenA series of comprehensive texts in mathematics210 Berlin [etc.]Springer305001VAN002904132001 Convex Analysis and Minimization AlgorithmsJean-Baptiste Hiriart-Urruty, Claude Lemarechal210 BerlinHeidelbergSpringer-Verlag1993215 2 volumiill.24 cm126B25Convexity of real functions of several variables, generalizations [MSC 2020]VANC022447MF49-XXCalculus of variations and optimal control; optimization [MSC 2020]VANC019757MF49J52Nonsmooth analysis [MSC 2020]VANC022711MF52A41Convex functions and convex programs in convex geometry [MSC 2020]VANC020312MF65K10Numerical optimization and variational techniques [MSC 2020]VANC020088MF90C25Convex programming [MSC 2020]VANC019709MFAlgorithmsKW:KConvex analysisKW:KMathematical programmingKW:KNonsmooth optimizationKW:KNumerical algorithmsKW:KOperations ResearchKW:KOptimizationKW:KBerlinVANL000066DEHeidelbergVANL000282Hiriart-UrrutyJean-BaptisteVANV030983352693LemarechalClaudeVANV044124352694Springer <editore>VANV108073650ITSOL20250530RICAhttps://doi.org/10.1007/978-3-662-02796-7E-book – Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o ShibbolethBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICAIT-CE0120VAN08NVAN00290415BIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA08DLOAD e-Book 11304 08eMF11304 20250528 Fundamentals1405372UNICAMPANIA03092nam 2200481z- 450 991105303730332120230911(CKB)5690000000228525(oapen)doab113956(EXLCZ)99569000000022852520230920c2023uuuu -u- -engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvances in Machine Learning and Mathematical Modeling for Optimization ProblemsMDPI - Multidisciplinary Digital Publishing Institute20231 online resource (280 p.)3-0365-7741-6 Machine learning and deep learning have made tremendous progress over the last decade and have become the de facto standard across a wide range of image, video, text, and sound processing domains, from object recognition to image generation. Recently, deep learning and deep reinforcement learning have begun to develop end-to-end training to solve more complex operation research and combinatorial optimization problems, such as covering problems, vehicle routing problems, traveling salesman problems, scheduling problems, and other complex problems requiring general simulations. These methods also sometimes include classic search and optimization algorithms for machine learning, such as Monte Carlo Tree Search in AlphaGO. The present reprint contains all of the articles accepted and published in the Special Issue of Mathematics entitled "Advances in Machine Learning and Mathematical Modeling for Optimization Problems". The articles presented in this Special Issue provide insights into related fields, including models, performance evaluation and improvements, and application developments. We hope that readers will benefit from the insights of these papers and contribute to these rapidly growing areas. We also hope that this Special Issue will shed light on major developments in the area of machine learning and mathematical modeling for optimization problems and that it will attract the attention of the scientific community to pursue further investigations, leading to the rapid implementation of these techniques.Mathematics & sciencebicsscResearch & information: generalbicsscartificial neural networks (ANNs)convex minimization problemsdecision theorydeep reinforcement learningend-to-end learningevolutionary computationfeature selectionmachine learningoptimization problemspickup and deliveryresource allocationstatistical learningtraveling salesman problemvehicle routing problemMathematics & scienceResearch & information: generalBOOK9911053037303321Advances in Machine Learning and Mathematical Modeling for Optimization Problems4525094UNINA