ieee-754

How to refine the result of a floating point division result?

﹥>﹥吖頭↗ 提交于 2021-01-02 04:00:43
问题 I have an an algorithm for calculating the floating point square root divide using the newton-raphson algorith. My results are not fully accurate and sometimes off by 1 ulp. I was wondering if there is a refinement algorithm for floating point division to get the final bits of accuracy. I use the tuckerman test for square root, but is there a similar algorithm for division? Or can the tuckerman test be adapted for division? I tried using this algorithm too but didn't get full accuracy results

How to refine the result of a floating point division result?

自作多情 提交于 2021-01-02 03:59:36
问题 I have an an algorithm for calculating the floating point square root divide using the newton-raphson algorith. My results are not fully accurate and sometimes off by 1 ulp. I was wondering if there is a refinement algorithm for floating point division to get the final bits of accuracy. I use the tuckerman test for square root, but is there a similar algorithm for division? Or can the tuckerman test be adapted for division? I tried using this algorithm too but didn't get full accuracy results

Converting hex string representation to float in python

半城伤御伤魂 提交于 2020-07-18 09:35:19
问题 I have data in IEEE 745 hexadecimal format: 0x1.5c28f5c28f5c3p-1 How would I convert this to a float in python? is this a standard module? 回答1: >>> float.fromhex('0x1.5c28f5c28f5c3p-1') 0.68 It's in the standard library, float.fromhex. 回答2: Ah ha: It's in the standard library, "float.fromhex", https://docs.python.org/2/library/stdtypes.html#float.fromhex 来源: https://stackoverflow.com/questions/34002545/converting-hex-string-representation-to-float-in-python

How to see the size of the incoming floating point number?

本秂侑毒 提交于 2020-06-01 07:36:28
问题 The user writes a number to the input, it is stored in a string. How can I check if this number is included in size in the float type or does it need a double ? 回答1: Unless your floating point numbers are huge or extremely small, i.e. out of the range spanning -3.4E38 to 3.4E38, a float 32 will store anything you throw at it in terms of size but not accuracy. As such, the real issue is how many significant digits you need in order to minimize rounding errors. I recommend you to read https:/