Alternating Direction Multiplier Method for Matrix <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-5.7429pt" id="M1" height="18.0035pt" version="1.1" viewBox="-0.0657574 -12.2606 19.5928 18.0035" width="19.5928pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-109" d="M238 681C243 705 239 712 230 712C217 712 156 682 75 674L70 648H105C148 648 153 641 144 598L39 110C18 11 35 -12 55 -12C90 -12 166 36 221 103L205 125C174 93 130 65 118 65C112 65 108 68 114 96L238 681Z"/></g><g transform="matrix(.012,0,0,-0.012,4.187,4.134)"><path id="g50-51" d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z"/></g><g transform="matrix(.012,0,0,-0.012,10.037,4.134)"><path id="g50-45" d="M98 134C72 134 46 117 46 90C46 73 55 65 60 64C95 55 124 32 124 -4C124 -42 95 -68 44 -89L57 -123C122 -104 194 -60 194 22C194 94 136 134 98 134Z"/></g><g transform="matrix(.012,0,0,-0.012,12.883,4.134)"><path id="g50-50" d="M389 0V32C297 38 291 46 291 118V635C234 613 175 595 109 583V556L161 554C203 552 207 547 207 497V118C207 46 201 38 110 32V0H389Z"/></g></svg>-</span>Norm Optimization in Multitask Feature Learning Problems

<div>Convergence performance of ADMM-BB and IADM-MFL.</div>

Mathematical Problems in Engineering

fig1

Figure 1

Figure 1: Alternating Direction Multiplier Method for Matrix <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-5.7429pt" id="M1" height="18.0035pt" version="1.1" viewBox="-0.0657574 -12.2606 19.5928 18.0035" width="19.5928pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-109" d="M238 681C243 705 239 712 230 712C217 712 156 682 75 674L70 648H105C148 648 153 641 144 598L39 110C18 11 35 -12 55 -12C90 -12 166 36 221 103L205 125C174 93 130 65 118 65C112 65 108 68 114 96L238 681Z"/></g><g transform="matrix(.012,0,0,-0.012,4.187,4.134)"><path id="g50-51" d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z"/></g><g transform="matrix(.012,0,0,-0.012,10.037,4.134)"><path id="g50-45" d="M98 134C72 134 46 117 46 90C46 73 55 65 60 64C95 55 124 32 124 -4C124 -42 95 -68 44 -89L57 -123C122 -104 194 -60 194 22C194 94 136 134 98 134Z"/></g><g transform="matrix(.012,0,0,-0.012,12.883,4.134)"><path id="g50-50" d="M389 0V32C297 38 291 46 291 118V635C234 613 175 595 109 583V556L161 554C203 552 207 547 207 497V118C207 46 201 38 110 32V0H389Z"/></g></svg>-</span>Norm Optimization in Multitask Feature Learning Problems 

Mathematical Problems in Engineering

Alternating Direction Multiplier Method for Matrix -Norm Optimization in Multitask Feature Learning Problems

Figure 1