Dynamic programming-The sum of the maximal contiguous subarray (Python implementation) & solving the maximum continuous product string (Python implementation)

def maxsum (self,array,n):    sum=array[0]    result=array[0] for    i in range (0,n):        if sum<0:            sum=a[ I]        else:            sum=sum+a[i]            start1=i        if Sum>result:            result=sum            end=i            start=start1    Print Result,start,end

The above is a dynamic programming idea: Suppose Sum[i] represents the largest continuous string ending with the element I, then sum[i]=max{sum[i-1]+a[i],a[i]}, judging the size of the element in parentheses, it becomes the judgment sum[i-1] is greater than 0

Maximum continuous product dynamic planning: Max represents the product of the largest continuous string ending in A, Min represents the smallest, then max=max{a[i],max[i-1]*a[i],min[i-1]*a[i]}min=min, because the maximum minimum may be just the symbol.

def maxmulti (self,array,n):    maxa=[]    mina=[]    maxa[0]=mina[0]=array[0]    value=maxa[0]    for I in range (0,n):        Maxa[i]=max (A[i],maxa[i-1]*a[i],mina[i-1]*a[i])        mina[i]=min (a[i],maxa[i-1]*a[i],mina[ I-1]*a[i])        Value=max (value,maxa[i])    print value

　　

Dynamic programming-The sum of the maximal contiguous subarray (Python implementation) & solving the maximum continuous product string (Python implementation)