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Illustration of Preallocating a Vector:
Illustration of calling the function:
>> myveccumsum([5 9 4])
ans =
5 14 18
At the first time in the loop, outvec will be [5]. Then in the second time, runsum will be 14 and outvec will store [5 14]. Lastly, runsum will be 18 and outvec will store [5 14 18].
Though this technique works, it is ineffective. The other method includes referring to each index in the output vector, and placing every partial sum into the next element in the output vector. Since each and every value of vec(i) is added to the running sum, this new sum is stored in the outvec(i).
Though initializing the output vector outvec to all zeros is not strictly essential, it greatly improves the efficiency of the function. Initializing this vector to all the zeros with similar size as the input argument preallocates that much memory for the outvec. Then, each element is changed in the loop to its right value. On the other hand, when building a vector up one element at a time, as in the earlier illustration, the MATLAB has to find a new memory each time the vector is extended.
The MATLAB has many other functions which work with vectors.
Finding products by for loop: an illustration, when 5 is passed to be the value of the input argument n, the function will compute and return 1 + 2 + 3 + 4 + 5, or 15: >> s
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Illustration of Gauss elimination: For illustration, for a 2 × 2 system, an augmented matrix be: Then, the EROs is applied to obtain the augmented matrix into an upper
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