Convex & Large-Scale Optimization: Theorems, Proofs, and Python Implementations (Computational Mathematics Library)
Format:
Paperback
En stock
1.22 kg
Sí
Nuevo
Amazon
USA
- Master the mathematical foundations and algorithmic engines that power modern data science, machine learning and operations research. Convex & Large-Scale Optimization distills cutting-edge research into 24 tightly curated chapters, each pairing theorems with executable Python code you can run straight from the page. Why This Book?• Covers the full spectrum – from the geometry of convex sets to advanced interior-point, ADMM, and stochastic variance-reduced methods • High-value topics optimized for search demand: semidefinite programming, stochastic gradient descent, Nesterov acceleration, LASSO sparse regression, compressed sensing, distributed optimization over networks, randomized numerical linear algebra • Ready-to-use Jupyter snippets in every chapter • Dense graduate-level treatment: theorems, proofs and worked examples replace rote exercises • Bridges theory to practice for scalable problems in deep learning, robust finance, signal processing and controlInside you will find:Convex sets, separation theorems and Minkowski dualitySubdifferential calculus and Fenchel conjugacyKKT conditions, Lagrangian and Fenchel–Rockafellar dualityCanonical models: LP, QP, SOCP, SDP with Python CVX frameworksGradient, subgradient and accelerated first-order algorithmsProximal point, ADMM, Douglas–Rachford and coordinate descentStochastic gradient, SVRG, SAGA and online convex optimizationQuasi-Newton, conjugate gradient and large-scale Krylov methodsInterior-point techniques for semidefinite and conic problemsDistributed and federated optimization on large graphsSketching, preconditioning and high-dimensional conditioningConvex relaxations for nonconvex objectives with global guarantees
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