Constraint satisfaction problems (CSPs) are mathematical questions defined as the set of objects whose state must satisfy a number of constraints or/ limitations. CSPs represent a entities in a problem as a homogeneous collection of finite constraints over variables, which is solved by constraint satisfaction methods. CSPs are the subject of research in both artificial intelligence and operations research, since the regularity in their formulation provides a common basis to analyze and solve problems of many seemingly unrelated families. CSPs often exhibit high complexity, requiring a combination of heuristics and combinatorial search methods to be solved in a reasonable time. Constraint programming (CP) is the field of research that specifically focuses on tackling these kinds of problems. Additionally, Boolean satisfiability problem (SAT), the satisfiability modulo theories (SMT), mixed integer programming (MIP) and answer set programming (ASP) are all fields of research focusing on the resolution of particular forms of the constraint satisfaction problem. Examples of problems that can be modeled as a constraint satisfaction problem include: * Type inference * Eight queens puzzle * Map coloring problem * Maximum cut problem * Sudoku, Crosswords, Futoshiki, Kakuro (Cross Sums), Numbrix, Hidato and many other logic puzzles These are often provided with tutorials of CP, ASP, Boolean SAT and SMT solvers. In the general case, constraint problems can be much harder, and may not be expressible in some of these simpler systems. "Real life" examples include automated planning, lexical disambiguation, musicology, product configuration and resource allocation. The existence of a solution to a CSP can be viewed as a decision problem. This can be decided by finding a solution, or failing to find a solution after exhaustive search (stochastic algorithms typically never reach an exhaustive conclusion, while directed searches often do, on sufficiently small problems). In some cases the CSP might be known to have solutions beforehand, through some other mathematical inference process. (Wikipedia).
Constraint-Satisfaction Problems in Python
Author David Kopec discusses Constraint-Satisfaction Problems in Python. To learn more, see David's book Classic Computer Science Problems in Python | http://mng.bz/opAp Use the discount code TWITKOPE40 for 40% off of any Manning title. A large number of problems which computational too
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Constraint Satisfaction Problems in Python
Author David Kopec discusses Constraint-Satisfaction Problems in Python. To learn more, see David's book Classic Computer Science Problems in Python | http://mng.bz/95B1 This video is also available on Manning's liveVideo platform: http://mng.bz/j2wP Use the discount code WATCHKOPEC40 f
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Constraint Satisfaction Problems and Probabilistic Combinatorics I - Fotios Illiopoulos
Computer Science/Discrete Mathematics Seminar II Topic: Constraint Satisfaction Problems and Probabilistic Combinatorics I Speaker: Fotios Illiopoulos Affiliation: Member, School of Mathematics Date: November 19, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
B06 Example problem with separable variables
Solving a differential equation by separating the variables.
From playlist Differential Equations
B04 Example problem with separable variables
Solving a differential equation by separating the variables.
From playlist Differential Equations
Calculus: We present a procedure for solving word problems on optimization using derivatives. Examples include the fence problem and the minimum distance from a point to a line problem.
From playlist Calculus Pt 1: Limits and Derivatives
B05 Example problem with separable variables
Solving a differential equation by separating the variables.
From playlist Differential Equations
B07 Example problem with separable variables
Solving a differential equation by separating the variables.
From playlist Differential Equations
Stochastic Local Search and the Lovasz Local Lemma - Fotios Iliopoulos
Short talks by postdoctoral members Topic: Stochastic Local Search and the Lovasz Local Lemma Speaker: Fotios Iliopoulos Affiliation: Member, School of Mathematics Date: September 25 For more video please visit http://video.ias.edu
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Calculus: Optimization Problems
In this video, I discuss optimization problems. I give an outline for how to approach these kinds of problems and worth through a couple of examples.
From playlist Calculus
Constraint Satisfaction Problems (CSPs) 1 - Overview | Stanford CS221: AI (Autumn 2021)
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-liang Assistant Professor
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021
Bartolomeo Stellato - Learning for Decision-Making Under Uncertainty - IPAM at UCLA
Recorded 01 March 2023. Bartolomeo Stellato of Princeton University, Operations Research and Financial Engineering, presents "Learning for Decision-Making Under Uncertainty" at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Abstract: We present two data-driven methods t
From playlist 2023 Artificial Intelligence and Discrete Optimization
Melanie Zeilinger: "Learning-based Model Predictive Control - Towards Safe Learning in Control"
Intersections between Control, Learning and Optimization 2020 "Learning-based Model Predictive Control - Towards Safe Learning in Control" Melanie Zeilinger - ETH Zurich & University of Freiburg Abstract: The question of safety when integrating learning techniques in control systems has
From playlist Intersections between Control, Learning and Optimization 2020
Optimization - Lecture 3 - CS50's Introduction to Artificial Intelligence with Python 2020
00:00:00 - Introduction 00:00:15 - Optimization 00:01:20 - Local Search 00:07:24 - Hill Climbing 00:29:43 - Simulated Annealing 00:40:43 - Linear Programming 00:51:03 - Constraint Satisfaction 00:59:17 - Node Consistency 01:03:03 - Arc Consistency 01:16:53 - Backtracking Search This cours
From playlist CS50's Introduction to Artificial Intelligence with Python 2020
Constraint Satisfaction Problems (CSPs) 2 - Definitions | Stanford CS221: AI (Autumn 2021)
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-liang Assistant Professor
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021
Alexandra Kolla - Quantum Approximate Optimization Algorithm (QAOA) and Local Max-Cut - IPAM at UCLA
Recorded 27 January 2022. Alexandra Kolla of the University of California, Santa Cruz, presents "Quantum Approximate Optimization Algorithm (QAOA) and Local Max-Cut" at IPAM's Quantum Numerical Linear Algebra Workshop. Abstract: We will discuss methods to determine how good of an approxima
From playlist Quantum Numerical Linear Algebra - Jan. 24 - 27, 2022
C49 Example problem solving a system of linear DEs Part 1
Solving an example problem of a system of linear differential equations, where one of the equations is not homogeneous. It's a long problem, so this is only part 1.
From playlist Differential Equations