- https://www.informs.org/
- 15.053 Optimization Methods in Business Analytics Instructor: James B. Orlin
- https://uwaterloo.ca/combinatorics-and-optimization/
- http://www.cityu.edu.hk/catalogue/pg/201920/course/MS8941.htm
- Strong Relaxations for Discrete Optimization Problems
- Deep Learning in Discrete Optimization, AMS 467/667, Spring 2019
- Northwestern University Open Text Book on Process Optimization
- https://ocw.mit.edu/courses/mathematics/18-433-combinatorial-optimization-fall-2003/lecture-notes/
- http://vanderbei.princeton.edu/307/lectures.html
- Bridging Continuous and Discrete Optimization
- https://www3.cs.stonybrook.edu/~algorith/implement/syslo/implement.shtml
- https://www-users.cs.umn.edu/~karypis/parbook/Lectures/AG/chap11_slides.pdf
- https://www.mpi-magdeburg.mpg.de/3562125/DOPT-Kaibel
- https://uwaterloo.ca/combinatorics-and-optimization/research-combinatorics-and-optimization/research-areas/
- http://www.math.uwaterloo.ca/~bico/
- http://www.math.uwaterloo.ca/~ltuncel/
- http://www.math.uwaterloo.ca/~cswamy/
- http://www.math.uwaterloo.ca/~lsanita/
- http://www.math.uwaterloo.ca/~jochen/
- http://www.math.uwaterloo.ca/~bguenin/
- https://uwaterloo.ca/scholar/vavasis
- https://math.ethz.ch/ifor/
- https://math.ethz.ch/ifor/people/robert-weismantel.html
- https://math.ethz.ch/ifor/people/rico-zenklusen.html
- https://math.ethz.ch/utils/search.Nzc2Mzc=.html?pagetype=people
- http://www.math.uni-magdeburg.de/~kaibel/
- http://www.mafy.lut.fi/study/DiscreteOpt/
- http://www.ekhalil.com/
- http://glaros.dtc.umn.edu/gkhome/index.php
- http://www.ams.jhu.edu/~wcook12/
- http://www.cs.cmu.edu/~pradeepr/
- http://www.cs.cmu.edu/~aarti/
- https://www.polyu.edu.hk/ama/events/conference/op20/en/
- https://theory.stanford.edu/~jvondrak/
- http://www.st.ewi.tudelft.nl/roos/
- https://iccopt2019.berlin/
- The 13th Workshop on Models and Algorithms for Planning and Scheduling Problems (MAPSP 2017)
- https://www.ise.ncsu.edu/fuzzy-neural/
- https://ifors.org/6th-international-conference-on-engineering-optimization-engopt-2018/
- https://sites.math.washington.edu/~rothvoss/lecturenotes.html
- https://sites.math.washington.edu/~rothvoss/409-spring-2017.html
- http://www.stern.nyu.edu/ioms/
- http://people.stern.nyu.edu/rcaldent/
- https://apps.dtic.mil/dtic/tr/fulltext/u2/a247397.pdf
- https://www.ise.ncsu.edu/people/fang/
- https://adler.ieor.berkeley.edu/
Integer-programming models arise in practically every area of application of mathematical programming.
- http://web.mit.edu/15.053/www/AMP-Chapter-09.pdf
- Duality and Discrete Optimization
- http://www.wsu.edu/~kbala/Math567.html
- http://home.ubalt.edu/ntsbarsh/Business-stat/opre/PartIII.htm
- The Branch-and-Cut Algorithm for Solving Mixed-Integer Optimization Problems
- Integer Optimziation and Lattices
- Integer Programming
- Branch and Bound Algorithms -Principles and Examples.
- https://web.stanford.edu/class/ee364b/lectures/bb_slides.pdf
- https://web.stanford.edu/class/cs369h/
- The Lasserre hierarchy in Approximation algorithms
- https://sites.math.washington.edu/~rothvoss/lecturenotes/LasserreSurveySlides.pdf
- https://ttic.uchicago.edu/~madhurt/Papers/sdpchapter.pdf
- http://www.samuelbhopkins.com/
- https://www.cs.princeton.edu/~kothari/
- https://profiles.stanford.edu/moses-charikar
Robert Weismantel, Head of the Mixed Integer Optimization team at ETHZ, said:
Mixed Integer Optimization deals with mathematical optimization problems with two types of variables: variables taking values in an integer domain, and variables taking values in a continuous domain. The fact that Mixed Integer Optimization problems naturally appear in many contexts has led to an increased interest in the design of strong algorithms for different variants of the problem.
- https://math.ethz.ch/ifor/research/mixed-integer-optimization.html
- https://arxiv.org/abs/1907.02206
- https://ryancorywright.github.io/pdf/UnifiedFrameworkforMIO.pdf
- https://www.sce.carleton.ca/faculty/chinneck/po/Chapter13.pdf
- https://mit-drl.github.io/goop/
- MINLP: Mixed-Integer Nonlinear Programming
- IMUS-MSRI2016
- Mirror-Descent Methods in Mixed-Integer Convex Optimization
- https://www.zib.de/groetschel/
- http://www.zib.de/groetschel/pubnew/blossom.pdf
Rico Zenklusen, Head of the Combinatorial Optimization team at ETHZ, said:
Technically speaking, Combinatorial Optimization is concerned with finding an optimal or close to optimal solution among a finite collection of possibilities. The finite set of possible solutions is typically described through mathematical structures, like graphs, matroids or independence systems. The focus in Combinatorial Optimization lies on efficient algorithms which, more formally, means algorithms with a running time bounded by a polynomial in the input size. Therefore, two of the arguably most prominent questions in Combinatorial Optimization are:
- How quickly can one find a single (or all) optimal solution(s) of a given problem?
- When dealing with a problem where, due to complexity-theoretic reasons, it is unlikely that an optimal solution can be found efficiently: What is the best solution quality that an efficient algorithm can guarantee?
- https://math.ethz.ch/ifor/research/combinatorial-optimization.html
- http://www.cs.cmu.edu/afs/cs.cmu.edu/project/learn-43/lib/photoz/.g/web/glossary/comb.html
- 18.438: Advanced Combinatorial Optimization, Fall 2009
- MATH 233B: Polyhedral techniques in combinatorial optimization, Stanford University, Winter 2017
- 514 - Networks and Combinatorial Optimization
- https://www.lix.polytechnique.fr/~liberti/teaching/xct/
- https://web.stanford.edu/class/ee364b/lectures/dikin_slides.pdf
- http://www.math.ust.hk/~mabfchen/Math4821B/