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A heuristic is a pragmatic method or rule of thumb for problem solving, often based on induction or analogy. Learn about different types of heuristics in mathematics, psychology, philosophy, and other fields, and how they can help or hinder decision making.
Problem solving is the process of achieving a goal by overcoming obstacles, ranging from simple personal tasks to complex issues in various fields. The web page explains the different types, methods, and approaches of problem solving in psychology, cognitive sciences, computer science, and logic.
Learn about heuristic techniques for problem solving in computer science, such as greedy algorithms, search algorithms, and antivirus software. Find out the definition, motivation, trade-off, examples, and pitfalls of heuristics.
Learn about multi-objective optimization or Pareto optimization, an area of multiple-criteria decision making that involves more than one objective function to be optimized simultaneously. Find examples, applications, methods and references for this field of mathematics and engineering.
Numerical analysis is the study of algorithms that use numerical approximation for solving problems of mathematical analysis. It has applications in engineering, science, finance, medicine and other fields, and uses direct and iterative methods to find approximate solutions.
Learn about the definition, properties, and algorithms of convex optimization problems, which are minimizing convex functions over convex sets. Find examples of special cases, such as linear, quadratic, and semidefinite programs.
The P versus NP problem is a major unsolved problem in theoretical computer science, asking whether every problem whose solution can be quickly verified can also be quickly solved. The web page explains the history, context, and implications of this problem, and gives examples of NP-complete and NP-hard problems.
Global optimization is a branch of mathematics that seeks to find the global minima or maxima of a function or a set of functions on a given set. It covers various methods, applications, and challenges of solving complex optimization problems.
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