RESEARCH
Industry 4.0
NEW GENERATION
GENERAL BLACK-BOX OPTIMIZER
for
mathematical optimizations, self driving, robotics and other technologies
We have developed a novel non-parametric method for black box optimization where the target problem does not have to be differentiable and may be noisy, still our algorithm can almost always converge toward a global optimum without getting stuck at local optima.
We offer this technology to help generate high quality and high quantity training data within a simulated environment to achieve the best possible model capability.
Extremely robust.
What is the innovation?
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General black-box optimizer
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Non-parametric
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(Almost) only global optima
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More precision
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No restart trick
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No assumptions needed about problem
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Not required to be differentiable
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Solve noisy data as well
Exceptionally good fault tolerance
We've been stress testing our solution and comparing it against other well known methods with great success.
Extremely robust.
Here is an example of the complexity of functions it can process:
x30 - 68 - 47 * cos( x12 ) ^ x30 + 53 * 22 - x5 + 54 - 52 + x30 + 101 + x25 + 54 * x3 * sinh( x30 ) + 75 - x13 ^ 14 * tanh( x30 ) ^ x6 - 46 * x30 ^ 81 * 55 + 97 + x30 + 15 - 47 * x10 - x11 * x20 * x26 * x30 - sinh( x4 ) + x2 - 2 - sinh( x30 ) - 82 * tan( x15 ) * 21 + 5 * 75 * x23 - x30 * cos( x30 ) - 41 + 25 + 12 * 38 + sinh( x17 ) - 75 * 23 * x29 ^ x22 - tan( x7 ) ^ x19 - sinh( x30 ) * x8 * x30 + x18 - 3 - 18 * tan( x30 ) + 64 - 97 * x21 * x30 ^ 53 - sinh( x30 ) - 27 * 50 + 23 + 30 + 24 + 2 * 83 - 73 * 45 - 80 + 89 * x10 + 15 - x4 * tan( x28 ) * 10 + 55 * 10 * exp( x2 ) - 4 + x9 + cosh( x14 ) ^ x24 * 80 + x1 ^ sin( x27 ) ^ x16 * x4 + 49 + 26 = 100
( error 0, time 2.21s )
x1 = 0.43847643622855487
x2 = 0.46929961542364773
x3 = -0.22739104890647494
x4 = 0.5419443297863478
x5 = -0.5487106409433273
x6 = 0.6862159575182608
x7 = 0.3809093553736538
x8 = -0.2790904768113744
x9 = 0.7098757786923025
x10 = 0.07842643908661656
x11 = -0.0817442135773003
x12 = 0.442795812830219
x13 = 0.38427484689331487
x14 = -0.6565246308968888
x15 = -0.8437142735692804
x16 = 0.12579756898717992
x17 = 0.4923442585462639
x18 = 0.523572942374683
x19 = 0.21694979463611894
x20 = -0.09079774813410064
x21 = 0.519963214975034
x22 = 0.6087180159291697
x23 = 0.8651529146552689
x24 = 0.52404542812148
x25 = 0.6183467400711733
x26 = -0.44056998043975537
x27 = 0.17027625860936058
x28 = -0.13984902234792382
x29 = 0.16878526770059354
x30 = 0.1762607949000223
How did we get to this result?
Our technological research and development of inventory optimization needed help in general problem-solving. This is how this solution was created.
The development has taken 2 years.