1. Prepare a blank mind map paper. You can choose your favorite color or blank white paper.

2. Write "Gauss" in the center of the paper as the central theme.

3. Write relevant information about Gauss around the central theme, such as his life, achievements and interesting things. As a subtopic of mind mapping.

4. Continue to add detailed information under each sub-topic, such as Gauss's birthplace, educational background, main research fields, etc.

5. Use pens or marking tools with different colors to gradually add content according to the hierarchical structure, and use connecting lines to express the association between information.

content

Gauss, whose full name is Johann Carl Friedrich Gauss (German: Johann Carl Friedrich Gau), is a famous German mathematician, physicist, astronomer, geometer and geodesist.

Gauss ranks alongside Archimedes and Newton as the three greatest mathematicians in the world. He made great achievements in his life, with 1 10 achievements named after his name "Gauss", which is the highest among mathematicians. He made contributions to number theory, algebra, statistics, analysis, differential geometry, geodesy, geophysics, mechanics, electrostatics, astronomy, matrix theory and optics.

Extended data:

Gauss's achievements:

1, Gaussian algorithm

Multiply the first term and the last term by the number of terms and then divide by 2 to calculate the result of "1+2+3+4+5+(n-1)+n". Such an algorithm is called Gaussian algorithm.

Gauss was very naughty when he was a child. In a math class, the teacher listed a difficult formula for them to calm down and let them work out the number 1+2+3+4+5+6+ ... one hour+100.

Only Gauss in the class gave the answer in less than 20 minutes, because he thought of using (1+100)+(2+99)+(3+98) ...+(50+51) ... * * There are 50 65438. Later, people called this simple algorithm Gaussian algorithm.

2. Least square method

Least square method is a mathematical optimization technique. It seeks the best function matching of data by minimizing the sum of squares of errors. Unknown data can be easily obtained by least square method, and the sum of squares of errors between these obtained data and actual data is the smallest.

The least square method can also be used for curve fitting. Other optimization problems can also be expressed by least square method by minimizing energy or maximizing entropy.