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@@ -8,9 +8,10 @@ This is a to-do list for Decimo.
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-[ ] Implement different methods for adding decimo types with `Int` types so that an implicit conversion is not required.
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-[ ] Use debug mode to check for unnecessary zero words before all arithmetic operations. This will help ensure that there are no zero words, which can simplify the speed of checking for zero because we only need to check the first word.
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-[ ] Check the `floor_divide()` function of `BigUInt`. Currently, the speed of division between imilar-sized numbers are okay, but the speed of 2n-by-n, 4n-by-n, and 8n-by-n divisions decreases unproportionally. This is likely due to the segmentation of the dividend in the Burnikel-Ziegler algorithm.
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-[ ] Consider using `Decimal` as the struct name instead of `BigDecimal`, and use `comptime BigDecimal = Decimal` to create an alias for the `Decimal` struct. This just switches the alias and the struct name, but it may be more intuitive to use `Decimal` as the struct name since it is more consistent with Python's `decimal.Decimal`. Moreover, hovering over `Decimal` will show the docstring of the struct, which is more intuitive than hovering over `BigDecimal` to see the docstring of the struct.
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-[x] (PR #127, #128, #131) Make all default constructor "safe", which means that the words are checked and normalized to ensure that there are no zero words and that the number is in a valid state. This will help prevent bugs and ensure that all `BigUInt` instances are in a consistent state. Also allow users to create "unsafe" `BigUInt` instances if they want to, but there must be a key-word only argument, e.g., `raw_words`.
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-[x] (#31) The `exp()` function performs slower than Python's counterpart in specific cases. Detailed investigation reveals the bottleneck stems from multiplication operations between decimals with significant fractional components. These operations currently rely on UInt256 arithmetic, which introduces performance overhead. Optimization of the `multiply()` function is required to address these performance bottlenecks, particularly for high-precision decimal multiplication with many digits after the decimal point.
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-[x] (#31) The `exp()` function performs slower than Python's counterpart in specific cases. Detailed investigation reveals the bottleneck stems from multiplication operations between decimals with significant fractional components. These operations currently rely on UInt256 arithmetic, which introduces performance overhead. Optimization of the `multiply()` function is required to address these performance bottlenecks, particularly for high-precision decimal multiplication with many digits after the decimal point. Internally, also use `Decimal` instead of `BigDecimal` or `BDec` to be consistent.
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-[x] Implement different methods for augmented arithmetic assignments to improve memeory-efficiency and performance.
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-[x] Implement a method `remove_leading_zeros` for `BigUInt`, which removes the zero words from the most significant end of the number.
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-[x] Use debug mode to check for uninitialized `BigUInt` before all arithmetic operations. This will help ensure that there are no uninitialized `BigUInt`.
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