A LinkedIn post today, by Oleksandr Kaleniuk (author of Manning's Geometry for Programmers book), mentioned a book ("Optimization Algorithms: AI techniques for design, planning, and control problems", 2024 - also see the companion GitHub repository for the book)
... that led me on a brief diversion, researching other books, articles, and journals on optimization algorithms for Operations Research
This blog post is a placeholder, as I continue to collect interesting citations.
References:
- https://en.wikipedia.org/wiki/Mathematical_optimization
- https://en.wikipedia.org/wiki/Operations_research
- https://en.wikipedia.org/wiki/Solver
- https://en.wikipedia.org/wiki/Maxima_and_minima
- https://en.wikipedia.org/wiki/Convex_function
- https://en.wikipedia.org/wiki/Convex_analysis
- https://en.wikipedia.org/wiki/Karush%E2%80%93Kuhn%E2%80%93Tucker_conditions
- https://en.wikipedia.org/wiki/Necessity_and_sufficiency
- https://en.wikipedia.org/wiki/Linear_programming
- https://en.wikipedia.org/wiki/Nonlinear_programming
- https://en.wikipedia.org/wiki/Quadratic_programming
- https://en.wikipedia.org/wiki/Critical_point_(mathematics)
- https://en.wikipedia.org/wiki/Function_of_a_real_variable
- https://en.wikipedia.org/wiki/Lipschitz_continuity
- https://en.wikipedia.org/wiki/Rademacher%27s_theorem
- https://en.wikipedia.org/wiki/Steinitz%27s_theorem
Journals:
Possibly Interesting Books:
(in a somewhat arbitrary suggested order of possible interest/value)
- Algorithms for Optimization (Mit Press, 2019)
- 4.7 stars, 119 reviews
- Numerical Optimization (Springer Series in Operations Research and Financial Engineering, 2nd Edition, 2006)
- 4.4 stars, 129 reviews
- High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 47) 1st Edition (2018)
- 4.7 stars, 74 reviews
- "High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the first to integrate theory, key tools, and modern applications of high-dimensional probability. Concentration inequalities form the core, and it covers both classical results such as Hoeffding's and Chernoff's inequalities and modern developments such as the matrix Bernstein's inequality. It then introduces the powerful methods based on stochastic processes, including such tools as Slepian's, Sudakov's, and Dudley's inequalities, as well as generic chaining and bounds based on VC dimension. A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, machine learning, compressed sensing, and sparse regression."
- High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 48, 2019)
- 4.8 stars, 65 reviews
Other Books:
(in a somewhat arbitrary suggested order of possible interest/value)
- Convex Optimization 1st Edition (2004)
- 4.6 stars, 198 reviews
- Convex Optimization Algorithms 1st Edition (2015)
- 4.9 stars, 16 reviews
- Convex Optimization Theory First Edition (2009)
- 4.6 stars, 22 reviews
- Convex Analysis and Optimization (2003)
- 4.8 stars, 9 reviews
- Introductory Lectures on Convex Optimization: A Basic Course (Applied Optimization, 87, 2003)
- 4.3 stars, 6 reviews
- Lectures on Convex Optimization (Springer Optimization and Its Applications, 137, 2018) Second Edition
- 4.7 stars, 21 reviews
- Optimization for Data Analysis (2022)
- 3.7 stars, 22 reviews
- Introduction to Linear Optimization (Athena Scientific Series in Optimization and Neural Computation, 6, 1997)
- 4.5 stars, 92 reviews
- Reinforcement Learning and Optimal Control First Edition (2019)
- 4.5 stars, 37 reviews
- Statistical Inference via Convex Optimization (Princeton Series in Applied Mathematics) (202)
- 4.6 stars, 3 reviews
Commercial Optmization Solutions:
Open Source Optimization Solutions:
- Google OR-Tools
- https://github.com/google/or-tools
- "OR-Tools is an open source software suite for optimization, tuned for tackling the world's toughest problems in vehicle routing, flows, integer and linear programming, and constraint programming."
- "After modeling your problem in the programming language of your choice, you can use any of a half dozen solvers to solve it: commercial solvers such as Gurobi or CPLEX, or open-source solvers such as SCIP, GLPK, or Google's GLOP and award-winning CP-SAT."
- License: Apache 2.0
- MiniZinc
- https://www.minizinc.org/
- https://github.com/MiniZinc
- " a high-level constraint modelling language that allows you to easily express and solve discrete optimisation problems."
- License: Mozilla Public License version 2.0
- pyomo
- https://www.pyomo.org/
- https://github.com/Pyomo/pyomo
- "Pyomo is a Python-based, open-source optimization modeling language with a diverse set of optimization capabilities."
- License: License.md
- R Project CRAN Task View: Optimization and Mathematical Programming
- https://cran.r-project.org/web/views/Optimization.html
- https://github.com/cran-task-views/Optimization/
- "This CRAN Task View contains a list of packages that offer facilities for solving optimization problems."
- Timefold Solver
- https://solver.timefold.ai/
- https://github.com/TimefoldAI/timefold-solver
- "The open source Solver AI for Java, Python and Kotlin to optimize scheduling and routing. Solve the vehicle routing problem, employee rostering, task assignment, maintenance scheduling and other planning problems."
- License: Apache 2.0
Optimization Competitions:
- https://www.minizinc.org/challenge/
- Noteworthy: See the different tools, by year, for past competitions, that won Gold, Silver, Bronze.
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