Description of fast matrix multiplication algorithm: ⟨5×10×11:392⟩

Algorithm type

16X4Y6Z5+24X2Y6Z5+2X4Y4Z4+2X3Y4Z3+2X3Y3Z2+2X2Y4Z2+16X2Y3Z3+2X2Y2Z4+10X2Y3Z2+6X2Y2Z3+2XY5Z+24XY3Z3+2X3Y2Z+68X2Y2Z2+16XY4Z+4XY2Z3+10X3YZ+10X2Y2Z+22XY3Z+4XY2Z2+10XYZ3+2X2YZ+52XY2Z+16XYZ2+68XYZ16X4Y6Z524X2Y6Z52X4Y4Z42X3Y4Z32X3Y3Z22X2Y4Z216X2Y3Z32X2Y2Z410X2Y3Z26X2Y2Z32XY5Z24XY3Z32X3Y2Z68X2Y2Z216XY4Z4XY2Z310X3YZ10X2Y2Z22XY3Z4XY2Z210XYZ32X2YZ52XY2Z16XYZ268XYZ16*X^4*Y^6*Z^5+24*X^2*Y^6*Z^5+2*X^4*Y^4*Z^4+2*X^3*Y^4*Z^3+2*X^3*Y^3*Z^2+2*X^2*Y^4*Z^2+16*X^2*Y^3*Z^3+2*X^2*Y^2*Z^4+10*X^2*Y^3*Z^2+6*X^2*Y^2*Z^3+2*X*Y^5*Z+24*X*Y^3*Z^3+2*X^3*Y^2*Z+68*X^2*Y^2*Z^2+16*X*Y^4*Z+4*X*Y^2*Z^3+10*X^3*Y*Z+10*X^2*Y^2*Z+22*X*Y^3*Z+4*X*Y^2*Z^2+10*X*Y*Z^3+2*X^2*Y*Z+52*X*Y^2*Z+16*X*Y*Z^2+68*X*Y*Z

Algorithm definition

The algorithm ⟨5×10×11:392⟩ could be constructed using the following decomposition:

⟨5×10×11:392⟩ = ⟨3×5×6:70⟩ + ⟨2×5×5:40⟩ + ⟨3×5×6:70⟩ + ⟨2×5×6:48⟩ + ⟨2×5×6:48⟩ + ⟨3×5×5:58⟩ + ⟨3×5×5:58⟩.

This decomposition is defined by the following equality:

TraceMulA_1_1A_1_2A_1_3A_1_4A_1_5A_1_6A_1_7A_1_8A_1_9A_1_10A_2_1A_2_2A_2_3A_2_4A_2_5A_2_6A_2_7A_2_8A_2_9A_2_10A_3_1A_3_2A_3_3A_3_4A_3_5A_3_6A_3_7A_3_8A_3_9A_3_10A_4_1A_4_2A_4_3A_4_4A_4_5A_4_6A_4_7A_4_8A_4_9A_4_10A_5_1A_5_2A_5_3A_5_4A_5_5A_5_6A_5_7A_5_8A_5_9A_5_10B_1_1B_1_2B_1_3B_1_4B_1_5B_1_6B_1_7B_1_8B_1_9B_1_10B_1_11B_2_1B_2_2B_2_3B_2_4B_2_5B_2_6B_2_7B_2_8B_2_9B_2_10B_2_11B_3_1B_3_2B_3_3B_3_4B_3_5B_3_6B_3_7B_3_8B_3_9B_3_10B_3_11B_4_1B_4_2B_4_3B_4_4B_4_5B_4_6B_4_7B_4_8B_4_9B_4_10B_4_11B_5_1B_5_2B_5_3B_5_4B_5_5B_5_6B_5_7B_5_8B_5_9B_5_10B_5_11B_6_1B_6_2B_6_3B_6_4B_6_5B_6_6B_6_7B_6_8B_6_9B_6_10B_6_11B_7_1B_7_2B_7_3B_7_4B_7_5B_7_6B_7_7B_7_8B_7_9B_7_10B_7_11B_8_1B_8_2B_8_3B_8_4B_8_5B_8_6B_8_7B_8_8B_8_9B_8_10B_8_11B_9_1B_9_2B_9_3B_9_4B_9_5B_9_6B_9_7B_9_8B_9_9B_9_10B_9_11B_10_1B_10_2B_10_3B_10_4B_10_5B_10_6B_10_7B_10_8B_10_9B_10_10B_10_11C_1_1C_1_2C_1_3C_1_4C_1_5C_2_1C_2_2C_2_3C_2_4C_2_5C_3_1C_3_2C_3_3C_3_4C_3_5C_4_1C_4_2C_4_3C_4_4C_4_5C_5_1C_5_2C_5_3C_5_4C_5_5C_6_1C_6_2C_6_3C_6_4C_6_5C_7_1C_7_2C_7_3C_7_4C_7_5C_8_1C_8_2C_8_3C_8_4C_8_5C_9_1C_9_2C_9_3C_9_4C_9_5C_10_1C_10_2C_10_3C_10_4C_10_5C_11_1C_11_2C_11_3C_11_4C_11_5=TraceMulA_3_6A_3_7A_3_8A_3_9A_3_10A_1_5+A_4_6A_1_1+A_4_7A_1_2+A_4_8A_1_3+A_4_9A_1_4+A_4_10A_2_5+A_5_6A_2_1+A_5_7A_2_2+A_5_8A_2_3+A_5_9A_2_4+A_5_10B_6_6B_5_1+B_6_7B_5_2+B_6_8B_5_3+B_6_9B_5_4+B_6_10B_5_5+B_6_11B_7_6B_1_1+B_7_7B_1_2+B_7_8B_1_3+B_7_9B_1_4+B_7_10B_1_5+B_7_11B_8_6B_2_1+B_8_7B_2_2+B_8_8B_2_3+B_8_9B_2_4+B_8_10B_2_5+B_8_11B_9_6B_3_1+B_9_7B_3_2+B_9_8B_3_3+B_9_9B_3_4+B_9_10B_3_5+B_9_11B_10_6B_4_1+B_10_7B_4_2+B_10_8B_4_3+B_10_9B_4_4+B_10_10B_4_5+B_10_11C_6_3C_6_4C_6_5C_7_3C_1_1+C_7_4C_1_2+C_7_5C_8_3C_2_1+C_8_4C_2_2+C_8_5C_9_3C_3_1+C_9_4C_3_2+C_9_5C_10_3C_4_1+C_10_4C_4_2+C_10_5C_11_3C_5_1+C_11_4C_5_2+C_11_5+TraceMulA_1_6-A_4_6A_1_7-A_4_7A_1_8-A_4_8A_1_9-A_4_9A_1_10-A_4_10A_2_6-A_5_6A_2_7-A_5_7A_2_8-A_5_8A_2_9-A_5_9A_2_10-A_5_10B_6_1+B_6_7B_6_2+B_6_8B_6_3+B_6_9B_6_4+B_6_10B_6_5+B_6_11B_7_1+B_7_7B_7_2+B_7_8B_7_3+B_7_9B_7_4+B_7_10B_7_5+B_7_11B_8_1+B_8_7B_8_2+B_8_8B_8_3+B_8_9B_8_4+B_8_10B_8_5+B_8_11B_9_1+B_9_7B_9_2+B_9_8B_9_3+B_9_9B_9_4+B_9_10B_9_5+B_9_11B_10_1+B_10_7B_10_2+B_10_8B_10_3+B_10_9B_10_4+B_10_10B_10_5+B_10_11C_1_1C_1_2C_2_1C_2_2C_3_1C_3_2C_4_1C_4_2C_5_1C_5_2+TraceMulA_3_5A_3_1A_3_2A_3_3A_3_4-A_1_5+A_4_5-A_1_1+A_4_1-A_1_2+A_4_2-A_1_3+A_4_3A_4_4-A_1_4-A_2_5+A_5_5-A_2_1+A_5_1-A_2_2+A_5_2-A_2_3+A_5_3-A_2_4+A_5_4B_5_6B_5_1+B_5_7B_5_2+B_5_8B_5_3+B_5_9B_5_4+B_5_10B_5_5+B_5_11B_1_6B_1_7+B_1_1B_1_8+B_1_2B_1_3+B_1_9B_1_4+B_1_10B_1_5+B_1_11B_2_6B_2_7+B_2_1B_2_8+B_2_2B_2_3+B_2_9B_2_4+B_2_10B_2_5+B_2_11B_3_6B_3_1+B_3_7B_3_2+B_3_8B_3_3+B_3_9B_3_4+B_3_10B_3_5+B_3_11B_4_6B_4_1+B_4_7B_4_2+B_4_8B_4_3+B_4_9B_4_4+B_4_10B_4_5+B_4_11C_6_3C_6_4C_6_5C_7_3C_7_4C_7_5C_8_3C_8_4C_8_5C_9_3C_9_4C_9_5C_10_3C_10_4C_10_5C_11_3C_11_4C_11_5+TraceMulA_1_5+A_1_6A_1_1+A_1_7A_1_2+A_1_8A_1_3+A_1_9A_1_4+A_1_10A_2_5+A_2_6A_2_1+A_2_7A_2_2+A_2_8A_2_3+A_2_9A_2_4+A_2_10B_6_6B_6_7B_6_8B_6_9B_6_10B_6_11B_7_6B_7_7B_7_8B_7_9B_7_10B_7_11B_8_6B_8_7B_8_8B_8_9B_8_10B_8_11B_9_6B_9_7B_9_8B_9_9B_9_10B_9_11B_10_6B_10_7B_10_8B_10_9B_10_10B_10_11C_6_1C_6_2C_7_1-C_1_1-C_1_2+C_7_2-C_2_1+C_8_1-C_2_2+C_8_2-C_3_1+C_9_1-C_3_2+C_9_2-C_4_1+C_10_1-C_4_2+C_10_2-C_5_1+C_11_1-C_5_2+C_11_2+TraceMulA_1_5A_1_1A_1_2A_1_3A_1_4A_2_5A_2_1A_2_2A_2_3A_2_4B_5_6-B_6_6B_5_7-B_6_7B_5_8-B_6_8B_5_9-B_6_9B_5_10-B_6_10B_5_11-B_6_11B_1_6-B_7_6B_1_7-B_7_7B_1_8-B_7_8B_1_9-B_7_9B_1_10-B_7_10B_1_11-B_7_11B_2_6-B_8_6B_2_7-B_8_7B_2_8-B_8_8B_2_9-B_8_9B_2_10-B_8_10B_2_11-B_8_11B_3_6-B_9_6B_3_7-B_9_7B_3_8-B_9_8B_3_9-B_9_9B_3_10-B_9_10B_3_11-B_9_11B_4_6-B_10_6B_4_7-B_10_7B_4_8-B_10_8B_4_9-B_10_9B_4_10-B_10_10B_4_11-B_10_11C_6_1+C_6_4C_6_2+C_6_5C_7_1+C_7_4C_7_2+C_7_5C_8_1+C_8_4C_8_2+C_8_5C_9_1+C_9_4C_9_2+C_9_5C_10_1+C_10_4C_10_2+C_10_5C_11_1+C_11_4C_11_2+C_11_5+TraceMulA_3_6A_3_7A_3_8A_3_9A_3_10A_4_6A_4_7A_4_8A_4_9A_4_10A_5_6A_5_7A_5_8A_5_9A_5_10-B_5_1+B_6_1-B_5_2+B_6_2-B_5_3+B_6_3-B_5_4+B_6_4-B_5_5+B_6_5-B_1_1+B_7_1-B_1_2+B_7_2-B_1_3+B_7_3-B_1_4+B_7_4-B_1_5+B_7_5-B_2_1+B_8_1-B_2_2+B_8_2-B_2_3+B_8_3-B_2_4+B_8_4-B_2_5+B_8_5-B_3_1+B_9_1-B_3_2+B_9_2-B_3_3+B_9_3-B_3_4+B_9_4-B_3_5+B_9_5-B_4_1+B_10_1-B_4_2+B_10_2-B_4_3+B_10_3-B_4_4+B_10_4-B_4_5+B_10_5C_1_3C_1_1+C_1_4C_1_2+C_1_5C_2_3C_2_1+C_2_4C_2_2+C_2_5C_3_3C_3_1+C_3_4C_3_2+C_3_5C_4_3C_4_1+C_4_4C_4_2+C_4_5C_5_3C_5_1+C_5_4C_5_2+C_5_5+TraceMulA_3_5+A_3_6A_3_1+A_3_7A_3_2+A_3_8A_3_3+A_3_9A_3_4+A_3_10A_4_6+A_4_5A_4_1+A_4_7A_4_2+A_4_8A_4_3+A_4_9A_4_4+A_4_10A_5_5+A_5_6A_5_1+A_5_7A_5_2+A_5_8A_5_3+A_5_9A_5_4+A_5_10B_5_1B_5_2B_5_3B_5_4B_5_5B_1_1B_1_2B_1_3B_1_4B_1_5B_2_1B_2_2B_2_3B_2_4B_2_5B_3_1B_3_2B_3_3B_3_4B_3_5B_4_1B_4_2B_4_3B_4_4B_4_5C_1_3-C_7_3C_1_4-C_7_4C_1_5-C_7_5-C_8_3+C_2_3C_2_4-C_8_4C_2_5-C_8_5C_3_3-C_9_3C_3_4-C_9_4C_3_5-C_9_5C_4_3-C_10_3C_4_4-C_10_4C_4_5-C_10_5C_5_3-C_11_3C_5_4-C_11_4C_5_5-C_11_5TraceMulA_1_1A_1_2A_1_3A_1_4A_1_5A_1_6A_1_7A_1_8A_1_9A_1_10A_2_1A_2_2A_2_3A_2_4A_2_5A_2_6A_2_7A_2_8A_2_9A_2_10A_3_1A_3_2A_3_3A_3_4A_3_5A_3_6A_3_7A_3_8A_3_9A_3_10A_4_1A_4_2A_4_3A_4_4A_4_5A_4_6A_4_7A_4_8A_4_9A_4_10A_5_1A_5_2A_5_3A_5_4A_5_5A_5_6A_5_7A_5_8A_5_9A_5_10B_1_1B_1_2B_1_3B_1_4B_1_5B_1_6B_1_7B_1_8B_1_9B_1_10B_1_11B_2_1B_2_2B_2_3B_2_4B_2_5B_2_6B_2_7B_2_8B_2_9B_2_10B_2_11B_3_1B_3_2B_3_3B_3_4B_3_5B_3_6B_3_7B_3_8B_3_9B_3_10B_3_11B_4_1B_4_2B_4_3B_4_4B_4_5B_4_6B_4_7B_4_8B_4_9B_4_10B_4_11B_5_1B_5_2B_5_3B_5_4B_5_5B_5_6B_5_7B_5_8B_5_9B_5_10B_5_11B_6_1B_6_2B_6_3B_6_4B_6_5B_6_6B_6_7B_6_8B_6_9B_6_10B_6_11B_7_1B_7_2B_7_3B_7_4B_7_5B_7_6B_7_7B_7_8B_7_9B_7_10B_7_11B_8_1B_8_2B_8_3B_8_4B_8_5B_8_6B_8_7B_8_8B_8_9B_8_10B_8_11B_9_1B_9_2B_9_3B_9_4B_9_5B_9_6B_9_7B_9_8B_9_9B_9_10B_9_11B_10_1B_10_2B_10_3B_10_4B_10_5B_10_6B_10_7B_10_8B_10_9B_10_10B_10_11C_1_1C_1_2C_1_3C_1_4C_1_5C_2_1C_2_2C_2_3C_2_4C_2_5C_3_1C_3_2C_3_3C_3_4C_3_5C_4_1C_4_2C_4_3C_4_4C_4_5C_5_1C_5_2C_5_3C_5_4C_5_5C_6_1C_6_2C_6_3C_6_4C_6_5C_7_1C_7_2C_7_3C_7_4C_7_5C_8_1C_8_2C_8_3C_8_4C_8_5C_9_1C_9_2C_9_3C_9_4C_9_5C_10_1C_10_2C_10_3C_10_4C_10_5C_11_1C_11_2C_11_3C_11_4C_11_5TraceMulA_3_6A_3_7A_3_8A_3_9A_3_10A_1_5A_4_6A_1_1A_4_7A_1_2A_4_8A_1_3A_4_9A_1_4A_4_10A_2_5A_5_6A_2_1A_5_7A_2_2A_5_8A_2_3A_5_9A_2_4A_5_10B_6_6B_5_1B_6_7B_5_2B_6_8B_5_3B_6_9B_5_4B_6_10B_5_5B_6_11B_7_6B_1_1B_7_7B_1_2B_7_8B_1_3B_7_9B_1_4B_7_10B_1_5B_7_11B_8_6B_2_1B_8_7B_2_2B_8_8B_2_3B_8_9B_2_4B_8_10B_2_5B_8_11B_9_6B_3_1B_9_7B_3_2B_9_8B_3_3B_9_9B_3_4B_9_10B_3_5B_9_11B_10_6B_4_1B_10_7B_4_2B_10_8B_4_3B_10_9B_4_4B_10_10B_4_5B_10_11C_6_3C_6_4C_6_5C_7_3C_1_1C_7_4C_1_2C_7_5C_8_3C_2_1C_8_4C_2_2C_8_5C_9_3C_3_1C_9_4C_3_2C_9_5C_10_3C_4_1C_10_4C_4_2C_10_5C_11_3C_5_1C_11_4C_5_2C_11_5TraceMulA_1_6A_4_6A_1_7A_4_7A_1_8A_4_8A_1_9A_4_9A_1_10A_4_10A_2_6A_5_6A_2_7A_5_7A_2_8A_5_8A_2_9A_5_9A_2_10A_5_10B_6_1B_6_7B_6_2B_6_8B_6_3B_6_9B_6_4B_6_10B_6_5B_6_11B_7_1B_7_7B_7_2B_7_8B_7_3B_7_9B_7_4B_7_10B_7_5B_7_11B_8_1B_8_7B_8_2B_8_8B_8_3B_8_9B_8_4B_8_10B_8_5B_8_11B_9_1B_9_7B_9_2B_9_8B_9_3B_9_9B_9_4B_9_10B_9_5B_9_11B_10_1B_10_7B_10_2B_10_8B_10_3B_10_9B_10_4B_10_10B_10_5B_10_11C_1_1C_1_2C_2_1C_2_2C_3_1C_3_2C_4_1C_4_2C_5_1C_5_2TraceMulA_3_5A_3_1A_3_2A_3_3A_3_4A_1_5A_4_5A_1_1A_4_1A_1_2A_4_2A_1_3A_4_3A_4_4A_1_4A_2_5A_5_5A_2_1A_5_1A_2_2A_5_2A_2_3A_5_3A_2_4A_5_4B_5_6B_5_1B_5_7B_5_2B_5_8B_5_3B_5_9B_5_4B_5_10B_5_5B_5_11B_1_6B_1_7B_1_1B_1_8B_1_2B_1_3B_1_9B_1_4B_1_10B_1_5B_1_11B_2_6B_2_7B_2_1B_2_8B_2_2B_2_3B_2_9B_2_4B_2_10B_2_5B_2_11B_3_6B_3_1B_3_7B_3_2B_3_8B_3_3B_3_9B_3_4B_3_10B_3_5B_3_11B_4_6B_4_1B_4_7B_4_2B_4_8B_4_3B_4_9B_4_4B_4_10B_4_5B_4_11C_6_3C_6_4C_6_5C_7_3C_7_4C_7_5C_8_3C_8_4C_8_5C_9_3C_9_4C_9_5C_10_3C_10_4C_10_5C_11_3C_11_4C_11_5TraceMulA_1_5A_1_6A_1_1A_1_7A_1_2A_1_8A_1_3A_1_9A_1_4A_1_10A_2_5A_2_6A_2_1A_2_7A_2_2A_2_8A_2_3A_2_9A_2_4A_2_10B_6_6B_6_7B_6_8B_6_9B_6_10B_6_11B_7_6B_7_7B_7_8B_7_9B_7_10B_7_11B_8_6B_8_7B_8_8B_8_9B_8_10B_8_11B_9_6B_9_7B_9_8B_9_9B_9_10B_9_11B_10_6B_10_7B_10_8B_10_9B_10_10B_10_11C_6_1C_6_2C_7_1C_1_1C_1_2C_7_2C_2_1C_8_1C_2_2C_8_2C_3_1C_9_1C_3_2C_9_2C_4_1C_10_1C_4_2C_10_2C_5_1C_11_1C_5_2C_11_2TraceMulA_1_5A_1_1A_1_2A_1_3A_1_4A_2_5A_2_1A_2_2A_2_3A_2_4B_5_6B_6_6B_5_7B_6_7B_5_8B_6_8B_5_9B_6_9B_5_10B_6_10B_5_11B_6_11B_1_6B_7_6B_1_7B_7_7B_1_8B_7_8B_1_9B_7_9B_1_10B_7_10B_1_11B_7_11B_2_6B_8_6B_2_7B_8_7B_2_8B_8_8B_2_9B_8_9B_2_10B_8_10B_2_11B_8_11B_3_6B_9_6B_3_7B_9_7B_3_8B_9_8B_3_9B_9_9B_3_10B_9_10B_3_11B_9_11B_4_6B_10_6B_4_7B_10_7B_4_8B_10_8B_4_9B_10_9B_4_10B_10_10B_4_11B_10_11C_6_1C_6_4C_6_2C_6_5C_7_1C_7_4C_7_2C_7_5C_8_1C_8_4C_8_2C_8_5C_9_1C_9_4C_9_2C_9_5C_10_1C_10_4C_10_2C_10_5C_11_1C_11_4C_11_2C_11_5TraceMulA_3_6A_3_7A_3_8A_3_9A_3_10A_4_6A_4_7A_4_8A_4_9A_4_10A_5_6A_5_7A_5_8A_5_9A_5_10B_5_1B_6_1B_5_2B_6_2B_5_3B_6_3B_5_4B_6_4B_5_5B_6_5B_1_1B_7_1B_1_2B_7_2B_1_3B_7_3B_1_4B_7_4B_1_5B_7_5B_2_1B_8_1B_2_2B_8_2B_2_3B_8_3B_2_4B_8_4B_2_5B_8_5B_3_1B_9_1B_3_2B_9_2B_3_3B_9_3B_3_4B_9_4B_3_5B_9_5B_4_1B_10_1B_4_2B_10_2B_4_3B_10_3B_4_4B_10_4B_4_5B_10_5C_1_3C_1_1C_1_4C_1_2C_1_5C_2_3C_2_1C_2_4C_2_2C_2_5C_3_3C_3_1C_3_4C_3_2C_3_5C_4_3C_4_1C_4_4C_4_2C_4_5C_5_3C_5_1C_5_4C_5_2C_5_5TraceMulA_3_5A_3_6A_3_1A_3_7A_3_2A_3_8A_3_3A_3_9A_3_4A_3_10A_4_6A_4_5A_4_1A_4_7A_4_2A_4_8A_4_3A_4_9A_4_4A_4_10A_5_5A_5_6A_5_1A_5_7A_5_2A_5_8A_5_3A_5_9A_5_4A_5_10B_5_1B_5_2B_5_3B_5_4B_5_5B_1_1B_1_2B_1_3B_1_4B_1_5B_2_1B_2_2B_2_3B_2_4B_2_5B_3_1B_3_2B_3_3B_3_4B_3_5B_4_1B_4_2B_4_3B_4_4B_4_5C_1_3C_7_3C_1_4C_7_4C_1_5C_7_5C_8_3C_2_3C_2_4C_8_4C_2_5C_8_5C_3_3C_9_3C_3_4C_9_4C_3_5C_9_5C_4_3C_10_3C_4_4C_10_4C_4_5C_10_5C_5_3C_11_3C_5_4C_11_4C_5_5C_11_5Trace(Mul(Matrix(5, 10, [[A_1_1,A_1_2,A_1_3,A_1_4,A_1_5,A_1_6,A_1_7,A_1_8,A_1_9,A_1_10],[A_2_1,A_2_2,A_2_3,A_2_4,A_2_5,A_2_6,A_2_7,A_2_8,A_2_9,A_2_10],[A_3_1,A_3_2,A_3_3,A_3_4,A_3_5,A_3_6,A_3_7,A_3_8,A_3_9,A_3_10],[A_4_1,A_4_2,A_4_3,A_4_4,A_4_5,A_4_6,A_4_7,A_4_8,A_4_9,A_4_10],[A_5_1,A_5_2,A_5_3,A_5_4,A_5_5,A_5_6,A_5_7,A_5_8,A_5_9,A_5_10]]),Matrix(10, 11, [[B_1_1,B_1_2,B_1_3,B_1_4,B_1_5,B_1_6,B_1_7,B_1_8,B_1_9,B_1_10,B_1_11],[B_2_1,B_2_2,B_2_3,B_2_4,B_2_5,B_2_6,B_2_7,B_2_8,B_2_9,B_2_10,B_2_11],[B_3_1,B_3_2,B_3_3,B_3_4,B_3_5,B_3_6,B_3_7,B_3_8,B_3_9,B_3_10,B_3_11],[B_4_1,B_4_2,B_4_3,B_4_4,B_4_5,B_4_6,B_4_7,B_4_8,B_4_9,B_4_10,B_4_11],[B_5_1,B_5_2,B_5_3,B_5_4,B_5_5,B_5_6,B_5_7,B_5_8,B_5_9,B_5_10,B_5_11],[B_6_1,B_6_2,B_6_3,B_6_4,B_6_5,B_6_6,B_6_7,B_6_8,B_6_9,B_6_10,B_6_11],[B_7_1,B_7_2,B_7_3,B_7_4,B_7_5,B_7_6,B_7_7,B_7_8,B_7_9,B_7_10,B_7_11],[B_8_1,B_8_2,B_8_3,B_8_4,B_8_5,B_8_6,B_8_7,B_8_8,B_8_9,B_8_10,B_8_11],[B_9_1,B_9_2,B_9_3,B_9_4,B_9_5,B_9_6,B_9_7,B_9_8,B_9_9,B_9_10,B_9_11],[B_10_1,B_10_2,B_10_3,B_10_4,B_10_5,B_10_6,B_10_7,B_10_8,B_10_9,B_10_10,B_10_11]]),Matrix(11, 5, [[C_1_1,C_1_2,C_1_3,C_1_4,C_1_5],[C_2_1,C_2_2,C_2_3,C_2_4,C_2_5],[C_3_1,C_3_2,C_3_3,C_3_4,C_3_5],[C_4_1,C_4_2,C_4_3,C_4_4,C_4_5],[C_5_1,C_5_2,C_5_3,C_5_4,C_5_5],[C_6_1,C_6_2,C_6_3,C_6_4,C_6_5],[C_7_1,C_7_2,C_7_3,C_7_4,C_7_5],[C_8_1,C_8_2,C_8_3,C_8_4,C_8_5],[C_9_1,C_9_2,C_9_3,C_9_4,C_9_5],[C_10_1,C_10_2,C_10_3,C_10_4,C_10_5],[C_11_1,C_11_2,C_11_3,C_11_4,C_11_5]]))) = Trace(Mul(Matrix(3, 5, [[A_3_6,A_3_7,A_3_8,A_3_9,A_3_10],[A_1_5+A_4_6,A_1_1+A_4_7,A_1_2+A_4_8,A_1_3+A_4_9,A_1_4+A_4_10],[A_2_5+A_5_6,A_2_1+A_5_7,A_2_2+A_5_8,A_2_3+A_5_9,A_2_4+A_5_10]]),Matrix(5, 6, [[B_6_6,B_5_1+B_6_7,B_5_2+B_6_8,B_5_3+B_6_9,B_5_4+B_6_10,B_5_5+B_6_11],[B_7_6,B_1_1+B_7_7,B_1_2+B_7_8,B_1_3+B_7_9,B_1_4+B_7_10,B_1_5+B_7_11],[B_8_6,B_2_1+B_8_7,B_2_2+B_8_8,B_2_3+B_8_9,B_2_4+B_8_10,B_2_5+B_8_11],[B_9_6,B_3_1+B_9_7,B_3_2+B_9_8,B_3_3+B_9_9,B_3_4+B_9_10,B_3_5+B_9_11],[B_10_6,B_4_1+B_10_7,B_4_2+B_10_8,B_4_3+B_10_9,B_4_4+B_10_10,B_4_5+B_10_11]]),Matrix(6, 3, [[C_6_3,C_6_4,C_6_5],[C_7_3,C_1_1+C_7_4,C_1_2+C_7_5],[C_8_3,C_2_1+C_8_4,C_2_2+C_8_5],[C_9_3,C_3_1+C_9_4,C_3_2+C_9_5],[C_10_3,C_4_1+C_10_4,C_4_2+C_10_5],[C_11_3,C_5_1+C_11_4,C_5_2+C_11_5]])))+Trace(Mul(Matrix(2, 5, [[A_1_6-A_4_6,A_1_7-A_4_7,A_1_8-A_4_8,A_1_9-A_4_9,A_1_10-A_4_10],[A_2_6-A_5_6,A_2_7-A_5_7,A_2_8-A_5_8,A_2_9-A_5_9,A_2_10-A_5_10]]),Matrix(5, 5, [[B_6_1+B_6_7,B_6_2+B_6_8,B_6_3+B_6_9,B_6_4+B_6_10,B_6_5+B_6_11],[B_7_1+B_7_7,B_7_2+B_7_8,B_7_3+B_7_9,B_7_4+B_7_10,B_7_5+B_7_11],[B_8_1+B_8_7,B_8_2+B_8_8,B_8_3+B_8_9,B_8_4+B_8_10,B_8_5+B_8_11],[B_9_1+B_9_7,B_9_2+B_9_8,B_9_3+B_9_9,B_9_4+B_9_10,B_9_5+B_9_11],[B_10_1+B_10_7,B_10_2+B_10_8,B_10_3+B_10_9,B_10_4+B_10_10,B_10_5+B_10_11]]),Matrix(5, 2, [[C_1_1,C_1_2],[C_2_1,C_2_2],[C_3_1,C_3_2],[C_4_1,C_4_2],[C_5_1,C_5_2]])))+Trace(Mul(Matrix(3, 5, [[A_3_5,A_3_1,A_3_2,A_3_3,A_3_4],[-A_1_5+A_4_5,-A_1_1+A_4_1,-A_1_2+A_4_2,-A_1_3+A_4_3,A_4_4-A_1_4],[-A_2_5+A_5_5,-A_2_1+A_5_1,-A_2_2+A_5_2,-A_2_3+A_5_3,-A_2_4+A_5_4]]),Matrix(5, 6, [[B_5_6,B_5_1+B_5_7,B_5_2+B_5_8,B_5_3+B_5_9,B_5_4+B_5_10,B_5_5+B_5_11],[B_1_6,B_1_7+B_1_1,B_1_8+B_1_2,B_1_3+B_1_9,B_1_4+B_1_10,B_1_5+B_1_11],[B_2_6,B_2_7+B_2_1,B_2_8+B_2_2,B_2_3+B_2_9,B_2_4+B_2_10,B_2_5+B_2_11],[B_3_6,B_3_1+B_3_7,B_3_2+B_3_8,B_3_3+B_3_9,B_3_4+B_3_10,B_3_5+B_3_11],[B_4_6,B_4_1+B_4_7,B_4_2+B_4_8,B_4_3+B_4_9,B_4_4+B_4_10,B_4_5+B_4_11]]),Matrix(6, 3, [[C_6_3,C_6_4,C_6_5],[C_7_3,C_7_4,C_7_5],[C_8_3,C_8_4,C_8_5],[C_9_3,C_9_4,C_9_5],[C_10_3,C_10_4,C_10_5],[C_11_3,C_11_4,C_11_5]])))+Trace(Mul(Matrix(2, 5, [[A_1_5+A_1_6,A_1_1+A_1_7,A_1_2+A_1_8,A_1_3+A_1_9,A_1_4+A_1_10],[A_2_5+A_2_6,A_2_1+A_2_7,A_2_2+A_2_8,A_2_3+A_2_9,A_2_4+A_2_10]]),Matrix(5, 6, [[B_6_6,B_6_7,B_6_8,B_6_9,B_6_10,B_6_11],[B_7_6,B_7_7,B_7_8,B_7_9,B_7_10,B_7_11],[B_8_6,B_8_7,B_8_8,B_8_9,B_8_10,B_8_11],[B_9_6,B_9_7,B_9_8,B_9_9,B_9_10,B_9_11],[B_10_6,B_10_7,B_10_8,B_10_9,B_10_10,B_10_11]]),Matrix(6, 2, [[C_6_1,C_6_2],[C_7_1-C_1_1,-C_1_2+C_7_2],[-C_2_1+C_8_1,-C_2_2+C_8_2],[-C_3_1+C_9_1,-C_3_2+C_9_2],[-C_4_1+C_10_1,-C_4_2+C_10_2],[-C_5_1+C_11_1,-C_5_2+C_11_2]])))+Trace(Mul(Matrix(2, 5, [[A_1_5,A_1_1,A_1_2,A_1_3,A_1_4],[A_2_5,A_2_1,A_2_2,A_2_3,A_2_4]]),Matrix(5, 6, [[B_5_6-B_6_6,B_5_7-B_6_7,B_5_8-B_6_8,B_5_9-B_6_9,B_5_10-B_6_10,B_5_11-B_6_11],[B_1_6-B_7_6,B_1_7-B_7_7,B_1_8-B_7_8,B_1_9-B_7_9,B_1_10-B_7_10,B_1_11-B_7_11],[B_2_6-B_8_6,B_2_7-B_8_7,B_2_8-B_8_8,B_2_9-B_8_9,B_2_10-B_8_10,B_2_11-B_8_11],[B_3_6-B_9_6,B_3_7-B_9_7,B_3_8-B_9_8,B_3_9-B_9_9,B_3_10-B_9_10,B_3_11-B_9_11],[B_4_6-B_10_6,B_4_7-B_10_7,B_4_8-B_10_8,B_4_9-B_10_9,B_4_10-B_10_10,B_4_11-B_10_11]]),Matrix(6, 2, [[C_6_1+C_6_4,C_6_2+C_6_5],[C_7_1+C_7_4,C_7_2+C_7_5],[C_8_1+C_8_4,C_8_2+C_8_5],[C_9_1+C_9_4,C_9_2+C_9_5],[C_10_1+C_10_4,C_10_2+C_10_5],[C_11_1+C_11_4,C_11_2+C_11_5]])))+Trace(Mul(Matrix(3, 5, [[A_3_6,A_3_7,A_3_8,A_3_9,A_3_10],[A_4_6,A_4_7,A_4_8,A_4_9,A_4_10],[A_5_6,A_5_7,A_5_8,A_5_9,A_5_10]]),Matrix(5, 5, [[-B_5_1+B_6_1,-B_5_2+B_6_2,-B_5_3+B_6_3,-B_5_4+B_6_4,-B_5_5+B_6_5],[-B_1_1+B_7_1,-B_1_2+B_7_2,-B_1_3+B_7_3,-B_1_4+B_7_4,-B_1_5+B_7_5],[-B_2_1+B_8_1,-B_2_2+B_8_2,-B_2_3+B_8_3,-B_2_4+B_8_4,-B_2_5+B_8_5],[-B_3_1+B_9_1,-B_3_2+B_9_2,-B_3_3+B_9_3,-B_3_4+B_9_4,-B_3_5+B_9_5],[-B_4_1+B_10_1,-B_4_2+B_10_2,-B_4_3+B_10_3,-B_4_4+B_10_4,-B_4_5+B_10_5]]),Matrix(5, 3, [[C_1_3,C_1_1+C_1_4,C_1_2+C_1_5],[C_2_3,C_2_1+C_2_4,C_2_2+C_2_5],[C_3_3,C_3_1+C_3_4,C_3_2+C_3_5],[C_4_3,C_4_1+C_4_4,C_4_2+C_4_5],[C_5_3,C_5_1+C_5_4,C_5_2+C_5_5]])))+Trace(Mul(Matrix(3, 5, [[A_3_5+A_3_6,A_3_1+A_3_7,A_3_2+A_3_8,A_3_3+A_3_9,A_3_4+A_3_10],[A_4_6+A_4_5,A_4_1+A_4_7,A_4_2+A_4_8,A_4_3+A_4_9,A_4_4+A_4_10],[A_5_5+A_5_6,A_5_1+A_5_7,A_5_2+A_5_8,A_5_3+A_5_9,A_5_4+A_5_10]]),Matrix(5, 5, [[B_5_1,B_5_2,B_5_3,B_5_4,B_5_5],[B_1_1,B_1_2,B_1_3,B_1_4,B_1_5],[B_2_1,B_2_2,B_2_3,B_2_4,B_2_5],[B_3_1,B_3_2,B_3_3,B_3_4,B_3_5],[B_4_1,B_4_2,B_4_3,B_4_4,B_4_5]]),Matrix(5, 3, [[C_1_3-C_7_3,C_1_4-C_7_4,C_1_5-C_7_5],[-C_8_3+C_2_3,C_2_4-C_8_4,C_2_5-C_8_5],[C_3_3-C_9_3,C_3_4-C_9_4,C_3_5-C_9_5],[C_4_3-C_10_3,C_4_4-C_10_4,C_4_5-C_10_5],[C_5_3-C_11_3,C_5_4-C_11_4,C_5_5-C_11_5]])))

N.B.: for any matrices A, B and C such that the expression Tr(Mul(A,B,C)) is defined, one can construct several trilinear homogeneous polynomials P(A,B,C) such that P(A,B,C)=Tr(Mul(A,B,C)) (P(A,B,C) variables are A,B and C's coefficients). Each trilinear P expression encodes a matrix multiplication algorithm: the coefficient in C_i_j of P(A,B,C) is the (i,j)-th entry of the matrix product Mul(A,B)=Transpose(C).

Algorithm description

These encodings are given in compressed text format using the maple computer algebra system. In each cases, the last line could be understood as a description of the encoding with respect to classical matrix multiplication algorithm. As these outputs are structured, one can construct easily a parser to its favorite format using the maple documentation without this software.


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