Linear Programming#
Sections#
Linear Programming (LP) is a type of mathematical optimization problem in which the constraints and optimization function are represented by linear functions.
Integer Programming (IP) is a type of mathematical optimization problem in which at least one variable is an integer.
Mixed-Integer Programming (MIP) involves problems in which not all variables are constrained to be integers (and so at least one variable is a non integer).
Integer Linear Programming (ILP) and Mixed-Integer Linear Programming (MILP) involve problems in which the non integer constraints (and optimization function) are linear.
Zero-One Linear (or Binary Integer) Programming involves problems in which the variables are restricted to be 0 or 1.
Quadratic Programming
Resources#
https://en.wikipedia.org/wiki/GNU_Linear_Programming_Kit
https://web.mit.edu/15.053/www/AppliedMathematicalProgramming.pdf
Keen, Ben Alex. Linear Programming with Python and PuLP.
https://optimization.cbe.cornell.edu/index.php?title=Simplex_algorithm
YouTube#
[ y ]
05-16-2019MIT OpenCourseWare. “24. Linear Programming and Two-Person Games”.[ y ]
03-04-2016MIT OpenCourseWare. “15. Linear Programming: LP, reductions, Simplex”.[ y ]
07-04-2023Tom S. “The Art of Linear Programming”.[ y ]
06-29-2020wenshenpsu. “Math484 Linear Programming Short Videos, summer 2020”.[ y ]
12-07-2022wenshenpsu. “Math484, Linear Programming, fall 2016”.
Texts#
2017Beck, Amir. First-Order Methods in Optimization. Society for Industrial and Applied Mathematics.2015[ P ][ Y ] Bubeck, Sébastien. Convex Optimization: Algorithms and Complexity.2001Chong, Edwin K. P. & Stanislaw H. Zak. An Introduction to Optimization, 2nd Ed. Wiley.1997Dantzig, George B. & Mukund N. Thapa. Linear Programming 1: Introduction. Springer Series in Operations Research and Financial Engineering.2003Dantzig, George B. & Mukund N. Thapa. Linear Programming 2: Theory and Extensions. Springer Series in Operations Research and Financial Engineering.2003Dechter, Rina. Constraint Processing. Morgan Kaufmann. [ home ].2003Morse, Philip M. & George E. Kimball. Methods of Operations Research. Dover Books on Computer Science.2018Nesterov, Yurii. Lectures on Convex Optimization. 2nd Ed. Springer: Springer Optimization and Its Applications.1999Nocedal, Jorge & Stephen Wright. Numerical Optimization. Series in Operations Research and Financial Engineering.2012Oki, Eiji. Linear Programming and Algorithms for Communication Networks: A Practical Guide to Network Design, Control, and Management. CRC Press.2021Pardalos, Panos M., Varvara Rasskazova, & Michael N. Vrahatis. Black Box Optimization, Machine Learning, and No-Free Lunch Theorem. Springer: Springer Optimization and Its Applications.2008Thie, Paul R. Gerard E. Keough. An Introduction to Linear Programming and Game Theory. 3rd Ed. Wiley.
Figures#
[ w ]
1920-1984Bellman, Richard[ w ]
---------Bland, Robert[ w ]
1914-2005Dantzig, George[ w ]
---------Dinitz, Yefim[ w ]
1934-----Edmonds, Jack[ w ]
1927-2017Ford Jr., L. R.[ w ]
1924-1976Fulkerson, D. R.[ w ]
1919-2005Harris, Ted[ w ]
---------Karmarkar, Narendra[ w ]
1935-----Karp, Richard[ w ]
1952-2005Khachiyan, Leonid[ w ]
1903-1987Kolmogorov, Andrey[ w ]
1941-2014Padberg, Manfred[ w ]
1903-1957Von Neumann, John[ w ]
1927-2016Wolfe, Philip
Terms#
[ w ] Approximation Algorithm
[ w ] Basic Feasible Solution
[ w ] Bauer’s Maximum Principle
[ w ] Bland’s Rule
[ w ] Block Matrix
[ w ] Boundedness
[ w ] Calculus of Variations
[ w ] Closedness
[ w ] Column Generation
[ w ] Combinatorial Optimization
[ w ] Compactness
[ w ] Constraint
[ w ] Constraint Satisfaction
[ w ] Constraint Satisfaction Problem (CSP)
[ w ] Convex Cone
[ w ] Convex Optimization
[ w ] Convex Polytope
[ w ] Convex Set
[ w ] Criss-Cross Algorithm
[ w ] Cunningham’s Rule
[ w ] Dantzig-Wolfe Decomposition
[ w ] Decomposition Method
[ w ] Devex Algorithm
[ w ] Dinitz’s Algorithm
[ w ] Directional Derivative
[ w ] Dual Linear Program
[ w ] Dual Problem
[ w ] Duality
[ w ] Duality Gap
[ w ] Edmonds-Karp Algorithm
[ w ] Ellipsoid Method
[ w ] Farkas’ Lemma
[ w ] Feasible Region/Set
[ w ] Flow Network
[ w ] Ford-Fulkerson Algorithm
[ w ] Fourier-Motzkin Elimination
[ w ] Game Theory
[ w ] Gradient
[ w ] Hyperplane
[ w ] Hypersurface
[ w ] Integer Programming
[ w ] Interior Point Methods
[ w ] Iterative Method
[ w ] Karmarkar’s Algorithm
[ w ] Lagrange Multiplier
[ w ] Line
[ w ] Linear Equality/Equation
[ w ] Linear Inequality
[ w ] Linear Function/Map/Transformation
[ w ] Linear Programming
[ w ] Mathematical Model
[ w ] Mathematical Optimization
[ w ] Max-Flow Min-Cut Theorem
[ w ] Max-Min Inequality
[ w ] Maximum Flow Problem
[ w ] Minimax Theorem
[ w ] Minimum-Cost Flow Problem
[ w ] Network Flow Problem
[ w ] Network Simplex Algorithm
[ w ] Newton’s Method
[ w ] Non Linear Programming
[ w ] Objective Function
[ w ] Operations Research
[ w ] Optimization Problem
[ w ] Plane
[ w ] Point
[ w ] Reduced Cost
[ w ] Relaxation
[ w ] Revised Simplex Method
[ w ] Simplex
[ w ] Simplex Algorithm
[ w ] Slack Variable
[ w ] Surface
[ w ] Unconstrainted Optimization
[ w ] Zadeh’s Rule
[ w ] Comparative Statics
[ w ] Envelope Theorem
[ w ] Gorman Polar Form
[ w ] Hicksian Demand Function
[ w ] Indirect Utility Function
[ w ] Shadow Price
Acknowledgments#
Byrne, Christopher. MATH 484 Linear Programs and Related Problems - Linear Programs I: A Clear, Concise, and Practical Introduction to Linear Programs and Duality Theory with Applications to Return On Investment (ROI). The Pennsylvania State University.