General Recurrent Neural Network for Solving Generalized Linear Matrix Equation

<div>Trajectories of state matrix <svg height="13.8595pt" id="M112" style="vertical-align:-2.2681pt" version="1.1" viewbox="-0.0498162 -11.5914 60.8719 13.8595" width="60.8719pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M748 650H522L515 622L546 617C580 611 587 604 565 575C518 513 469 451 419 393C376 474 349 534 330 580C318 609 325 612 361 618L383 622L392 650H151L144 622C214 616 224 612 257 543L360 327C270 218 187 124 159 95C106 40 92 34 26 28L17 0H252L259 28L236 31C189 37 188 47 209 78C249 136 308 210 377 294L478 79C494 44 487 37 449 32L418 28L409 0H673L680 28C596 34 591 39 554 116L436 361C526 469 574 521 604 553C659 612 669 614 739 622L748 650Z" id="g113-89"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="765" vert-adv-y="765" xpluscharwidth="9.945"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,9.945,0)"><path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 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135L414 144Z" id="g50-51"></path><glyph.data ascent="3443" descent="-2856" horiz-adv-x="487" vert-adv-y="487" xpluscharwidth="60.07668539"></glyph.data></g></svg> for solving GLME (<a href="https://static.hindawi.com/articles/complexity/volume-2017/9063762/figures/#EEq15">27</a>) with the multiple theoretical solutions <svg height="10.1524pt" id="M113" style="vertical-align:-0.04990005pt" version="1.1" viewbox="-0.0498162 -10.1025 16.161 10.1524" width="16.161pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M748 650H522L515 622L546 617C580 611 587 604 565 575C518 513 469 451 419 393C376 474 349 534 330 580C318 609 325 612 361 618L383 622L392 650H151L144 622C214 616 224 612 257 543L360 327C270 218 187 124 159 95C106 40 92 34 26 28L17 0H252L259 28L236 31C189 37 188 47 209 78C249 136 308 210 377 294L478 79C494 44 487 37 449 32L418 28L409 0H673L680 28C596 34 591 39 554 116L436 361C526 469 574 521 604 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different initial states <svg height="13.8595pt" id="M114" style="vertical-align:-2.2681pt" version="1.1" viewbox="-0.0498162 -11.5914 62.6857 13.8595" width="62.6857pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M748 650H522L515 622L546 617C580 611 587 604 565 575C518 513 469 451 419 393C376 474 349 534 330 580C318 609 325 612 361 618L383 622L392 650H151L144 622C214 616 224 612 257 543L360 327C270 218 187 124 159 95C106 40 92 34 26 28L17 0H252L259 28L236 31C189 37 188 47 209 78C249 136 308 210 377 294L478 79C494 44 487 37 449 32L418 28L409 0H673L680 28C596 34 591 39 554 116L436 361C526 469 574 521 604 553C659 612 669 614 739 622L748 650Z" id="g113-89"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="765" vert-adv-y="765" xpluscharwidth="9.945"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,9.945,0)"><path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 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Complexity

fig3

Figure 3

Figure 3: General Recurrent Neural Network for Solving Generalized Linear Matrix Equation 