## 4 Method for Determining Optimal Weights
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of** & **Moduli** & **Modulo Size, Bit** & **Optimal Value \(\text{\emph{minmax}}(\text{C}(B_{i}))\)** & **Set of Optimal Weights** & **Absolute Error** \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{128} & & (3402751750609268616530418096319514004, & \multirow{4}{*}{0.02 \(\cdot\) 10\({}^{-26}\)} \\  & & & 30102063663319372474681011989979515, & \\  & & 24.351813 & 3365109509786191789383420612053757, & 0.022 \(\cdot\) 10\({}^{-26}\) \\  & & & 996129958441745868191040559160257160404) & \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{256} & & (1157908361929575616645177370505088125551 & \multirow{4}{*}{0.02 \(\cdot\) 10\({}^{-26}\)} \\  & & & 35547252453548758261246404652825624, & \\  & & & 113848504512510677418246407670152185760791528, & \\  & & 2885975185865074329930973307110037476 & & \\  & & 65237217685 & 3764187082146709658639372447493 & 0.08 \(\cdot\) 10\({}^{-26}\) \\  & & & 999230494414908962667852858140771335551 & \\  & & & 266367207268981904553733826510608006 & \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{512} & & (103703792445692602559587821963673673 & \multirow{4}{*}{0.03 \(\cdot\) 10\({}^{-26}\)} \\  & & & 6640362727809073133854883134149979018205 & \\  & & & 858416664687307363243243746391152622109 & \\  & & & 82873169243821876124328761264232 & \\  & & & 5534164404928607267620146562203258588284 & \\  & & & 557316440492860276157264104656203258588284 & \\  & & & 55731642934536924086019043213256511229 & \\  & & & 42658642136595843243246324657866066002498 & \\  & & & 2571591231421681187637607007300 & \\  & & & 1347607601367826819818866400 & 0.03 \(\cdot\) 10\({}^{-26}\) \\  & & & 79804530034905675496484988150072297516 & \\  & & & 244284637597144525139530985483832636267 & \\  & & & 2272794998293038208508552435557853910 & \\  & & & 6382541568718840326423438557685732766 & \\  & & & 237382971059684173317315215274670655142 & \\  & & & 941524695235808676308305285453555853910 & \\  & & & 93758809767174140797637892 & \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{512} & & (63671275205949397787289810879791647479330 & \multirow{4}{*}{26819959546342367635840} & \multirow{4}{*}{0.03 \(\cdot\) 10\({}^{-26}\)} \\  & & & 26819956453426736409885818246517912415527 & \\  & & & 88857518736195987082137643347345656840 & \\  & & & 708601207157177093118999764047921817028 & \\  & & & 84728998070836356396849344340212371 & \\  & & & 617553757421451642075764311117655526437519 & \\  & & & 45356366784387887666995037488118243731 & \\  & & & 6588351392265635 & \\  & & & 6427427528071303067846211711469302955 & \\  & & & 66192634087853584888888882546407653 & \\  & & & 6224213097416008313727821708712769658527 & \\  & & & 55523152805131700928448052384489278981429 & \\  & & & 552497249724072875249728773373283738730969273 & \\  & & & 437299097872564604182050537919764926454 & \\  & & & 2720759313157751765803303705841451503971420 & \\  & & & 82800912256343 & \\  & & & 92316069339164652272690470278333661630164087 & 0.1 \(\cdot\) 10\({}^{-26}\) \\  & & & 846507972202736846789327833166174082316014087 & 0.1 \(\cdot\) 10\({}^{-26}\) \\  & & & 84650679722027368467891741780291379326878 & \\  & & & 84650617795612027962113721896588469 & \\  & & & 469971573703130338365846 The performance studies were carried out in a similar way to the previous study. Research is conducted on the basis of programs written in Python, on equipment with the following characteristics:  
* CPU: frequency: 2.90 GHz, cores--6, process technology: 14 nm;
* GPU: video memory 6144 MB, memory frequency 14000 MHz, GPU frequency 1680 MHz, TDP 500 W;
* RAM: 16 GB, frequency 3200 MHz;
* OS: Windows 11. * The experiment is carried out in two stages:
* Stage A--performance study of 9 sets, 8 moduli, and dimensions from 8 to 1024 bits;
* Stage B--performance study of 20 sets, from 3 to 20 moduli, and a dimension of 32 bits. When conducting a two-stage simulation, the time characteristics of each method were obtained.