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Everything about Mathematical optimization
Study of mathematical algorithms for optimization problems
Mathematical optimization or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Mathematical optimizationA 5-minute orientation
- Skim a recent paper: MathOptInterface: A Data Structure for Mathematical Optimization ProblemsInfluential work since 2022
- Pick up a classic book: Graphs, algorithms, and optimizationThe most-reprinted book on the subject
- Read the landmark paper: Supervised classification and mathematical optimizationCited 130 times
- Go sideways: Nonlinear programmingA closely related topic
- Go sideways: Convex optimizationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Supervised classification and mathematical optimization
Toward Fuzzy Optimization without Mathematical Ambiguity
Mathematical optimization applications in metabolic networks
Mathematical optimization in intensity modulated radiation therapy
Mathematical optimization ideas for biodiversity conservation
Free material optimization via mathematical programming
MOPS — Mathematical optimization system
Codon optimization: a mathematical programing approach
🆕 Influential since 2022
MathOptInterface: A Data Structure for Mathematical Optimization Problems
Rigorous mathematical optimization of synthetic hepatic vascular trees
Adaptive Exploration Artificial Bee Colony for Mathematical Optimization
Mathematical optimization modelling for group counterfactual explanations
A Reinforced Whale Optimization Algorithm for Solving Mathematical Optimization Problems
📚 Books worth reading
Graphs, algorithms, and optimizationWilliam Kocay · 2004- 📕Introduction to OptimizationEdwin K. P. Chong, Stanislaw H. Zak · 2004
Modeling and Optimization: Theory and ApplicationsTamás Terlaky · 2012
Combinatorial OptimizationBernhard Korte, Jens Vygen · 2005
Principles of optimal designPanos Y. Papalambros, Douglas J. Wilde · 1988
Integer and combinatorial optimizationGeorge L. Nemhauser · 1988
Graphs, Networks and AlgorithmsDieter Jungnickel · 2006
Designing economic mechanismsLeonid Hurwicz · 2002
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Nonlinear programming In mathematics, nonlinear programming (NLP), also known as nonlinear optimization, is the process of solving an optimization problem where some of the…
- Convex optimization Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently…
- List of numerical analysis topics This is a list of numerical analysis topics. Validated numerics Iterative method Rate of convergence — the speed at which a convergent sequence approaches…
- Gradient descent Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate…
- Active-set method In mathematical optimization, the active-set method is an algorithm used to identify the active constraints in a set of inequality constraints. The active…
- Penalty method In mathematical optimization, penalty methods are a certain class of algorithms for solving constrained optimization problems. A penalty method replaces…
- MINOS (optimization software) MINOS is a Fortran software package for solving linear and nonlinear mathematical optimization problems. MINOS (Modular In-core Nonlinear Optimization…
- Limited-memory BFGS Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the collection of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–S…