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Optimization

Five ranked references. Rank 1 is the default spine, not a requirement to read all five.

1 | Convex Optimization

Stephen Boyd, Lieven Vandenberghe | Primary spine | Upper undergraduate / graduate

The canonical modern text for convexity, duality, KKT conditions, modeling, algorithms, and engineering applications.

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2 | Numerical Optimization

Jorge Nocedal, Stephen J. Wright | Algorithms / depth | Graduate

Core reference for line search, trust region, quasi-Newton, constrained methods, and large-scale numerical optimization.

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3 | Nonlinear Programming

Dimitri P. Bertsekas | Nonlinear depth | Graduate

Deep treatment of constrained nonlinear optimization, duality, and algorithmic convergence.

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4 | Introduction to Linear Optimization

Dimitris Bertsimas, John N. Tsitsiklis | Linear / combinatorial foundation | Upper undergraduate / graduate

Excellent foundations in LP, duality, simplex/interior methods, and discrete optimization thinking.

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5 | Algorithms for Optimization

Mykel J. Kochenderfer, Tim A. Wheeler | Open computational path | Undergraduate / graduate

Broad, implementation-oriented survey from gradient methods to stochastic and global optimization.

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  • Convex Optimization: Full legal web/PDF: open
  • Algorithms for Optimization: Full legal PDF / web: open