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The knapsack problem is one of the most studied problems in combinatorial optimization, with many real-life applications. For this reason, many special cases and generalizations have been examined. [1] [2] Common to all versions are a set of n items, with each item having an associated profit p j and weight w j.
The problem for graphs is NP-complete if the edge lengths are assumed integers. The problem for points on the plane is NP-complete with the discretized Euclidean metric and rectilinear metric. The problem is known to be NP-hard with the (non-discretized) Euclidean metric. [3]: ND22, ND23
Sweller, et al. suggests a worked example early, and then a gradual introduction of problems to be solved. They propose other forms of learning early in the learning process (worked example, goal free problems, etc.); to later be replaced by completions problems, with the eventual goal of solving problems on their own. [42]
Problem solving in psychology refers to the process of finding solutions to problems encountered in life. [5] Solutions to these problems are usually situation- or context-specific. The process starts with problem finding and problem shaping, in which the problem is discovered and simplified. The next step is to generate possible solutions and ...
In 2003, he published a widely-read booklet titled The Cognitive Style of PowerPoint, revised in 2006. [19] Tufte found a number of problems with the "cognitive style" of PowerPoint, many of which he attributed to the standard default style templates: [19] PowerPoint's convenience for some presenters is costly to the content and the audience.
Different kinds of problem-solving (e.g., top-down, bottom-up, and opportunistic problem-solving) could be selectively mixed based on the current state of problem solving. Essentially, the problem-solver was being used both to solve a domain-level problem along with its own control problem, which could depend on the former.
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
Examples of divergent thinking include using brainstorming, free writing and creative thinking at the beginning of the problem solving process to generate possible solutions that can be evaluated later. [3] Once a sufficient number of ideas have been explored, convergent thinking can be used.