ÁLGEBRA LINEAR II
Ao Verificar se B = {(2, 2), (-1, - 2)} gera o espaço V.
Fazendo: 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Obtemos:
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Verificando se A = {(1, 0, 2), (-1, 1, 0), (1, 1, 1)} é uma base de R3 .
Podemos afirmar que A é base de R3 porquê
A é Linearmente Independente e A gera o espaço V.
A é Linearmente Dependente e A gera o espaço V.
A é Linearmente Independente e A não gera o espaço V.
A é Linearmente Dependente.
A é Linearmente Dependente e A não gera o espaço V.
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)
Os vetores v1, v2 e v3 são LI porque o determinante é igual a -2, ou seja, diferente de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual a -1, ou seja, diferente de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual a 1, ou seja, diferente de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual a 2, ou seja, diferente de zero
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)
I, II e III
II e III apenas
I apenas
I e II apenas
I e III apenas
Seja T : IR2
IR 2, um operador linear dado por T (x, y) = ( 2y, x) .
O polinômio minimal é dado por:
p(x)= ( - x +
) . ( - x +
)= p (λ)
p(x)= (x -
) . (x -
)= p (λ)
p(x)= (x -
) . (x +
)= p (λ)
p(x)= (x2 -
) . (x2 -
)= p (λ)
p(x)= (x2 +
) . (x2 +
)= p (λ)
Dada a matriz A de uma transformação linear T: V
V .Determinando o autovetor de A =
, ao utilizar o autovalor λ 1= 12, teremos como solução:
V = (5/2, 1)
V = ( - 5/2, 1)
V = (- 5/2, - 1)
V = (2/5, 1)
V = (3/2, - 1)
![](data:image/png;base64,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)
6 e -3
2 e -9
-2 e -9
-2 e 9
2 e 9
Seja o operador linear de IR2 definido por T(x, y) = (3x - 2y, 5x + 6y), indique a alternativa que determina as coordenadas do vetor
IR2 tal que T (
) = (- 12, 8).
X= - 2 e y = - 3.
X= -3 e y = 2.
X= 3 e y = - 2.
X= 3 e y = 4.
X= - 2 e y = 3.
Existem funções que relacionam espaços vetoriais ou subespaços vetoriais a outros espaços vetoriais chamadas de transformações lineares.
Assim sabendo que T: IR2
IR3 associa vetores pertencentes a IR2 do tipo (x, y) = (- 4, 1), definido pela imagem f(x, y) = (2x, x - y, - y); tem-se uma transformação linear, se:
f(- 4, 1) = (- 8, 5, - 1)
f(- 4, 1) = (8, 5, - 1)
f(- 4, 1) = (8, 5, 1)
f(- 4, 1) = (- 8, - 5, 1)
f(- 4, 1) = (- 8, - 5, - 1)
Verifique se o vetor dado v = (1, 2, 4) pertence ou não ao subespaço vetorial com v1= (1, 2, 1) ; v2 = (1, 0, 2) e v3 = (1, 1, 0) assinale a alternativa que indica os valores reais de a, b e c; na combinação linear definida por
![](data:image/png;base64,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)
Com os valores reais em a = 2; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 2; b = 2 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = 1 e c = 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 1; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = -1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Verificando se A = {(1, 0, 2), (-1, 1, 0), (1, 1, 1)} é uma base de R3 .
Podemos afirmar que A é base de R3 porquê
A é Linearmente Independente e A gera o espaço V.
A é Linearmente Dependente e A gera o espaço V.
A é Linearmente Independente e A não gera o espaço V.
A é Linearmente Dependente.
A é Linearmente Dependente e A não gera o espaço V.
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)
Os vetores v1, v2 e v3 são LI porque o determinante é igual a -2, ou seja, diferente de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual a -1, ou seja, diferente de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual a 1, ou seja, diferente de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual a 2, ou seja, diferente de zero
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)
I, II e III
II e III apenas
I apenas
I e II apenas
I e III apenas
Seja T : IR2
IR 2, um operador linear dado por T (x, y) = ( 2y, x) .
O polinômio minimal é dado por:
p(x)= ( - x +
) . ( - x +
)= p (λ)
p(x)= (x -
) . (x -
)= p (λ)
p(x)= (x -
) . (x +
)= p (λ)
p(x)= (x2 -
) . (x2 -
)= p (λ)
p(x)= (x2 +
) . (x2 +
)= p (λ)
Dada a matriz A de uma transformação linear T: V
V .Determinando o autovetor de A =
, ao utilizar o autovalor λ 1= 12, teremos como solução:
V = (5/2, 1)
V = ( - 5/2, 1)
V = (- 5/2, - 1)
V = (2/5, 1)
V = (3/2, - 1)
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlcAAABBCAYAAAD8K4ErAAAciElEQVR4Ae2di5HcNrNGlYJjcArOwSE4BqfgDJyBMlAEisAJOAFn4Bz019lb5+pTGyDBGc7OYxtVNEignx8ajR7OWPvpW7dGoBFoBBqBRqARaAQagdMQ+HSapBbUCDQCd0Pg33///fbTTz/dTX8rbgQagUagEfiOQBdX37HouxdH4K+//vr2+++/f/v555+/ffr06a0Y4ZnC5Jnar7/++mY/PuTFeLdGoBFoBBqB+yPQxdX916AteAcEKKwoRLKY+vz589vYsxUl2PtsNr/DEreKRqARaAQeBoEurh5mKdqQVQTybQ33f/755y6rxVV9S+VbrF0BD0SwUlyBScXpgVxoUxqBRqAReGkEurh66eV9TecsGs7w7pdffnm63yqtFFeJzZl4pdy+bwQagUagERgj0MXVGJcefWAEzioWfJvF14PP1Lq4eqbValsbgUbgIyLQxdVHXPUn9/na4uqff/55+yqRrwSfrbBi6SiusJ0eLPi/BPO3ZHV5r8WryuvnRqARaAQagW0EurjaxqdnHxCBa4qF/C0SXwk+Y3GFD1lM7f0w/xq8HnD526RGoBFoBB4egS6uHn6J2sCKwBnFAm+vKFCQRf/eLYs8/dnq9+zTl7///vs/pMr9z0QPNAKNQCPQCNwEgacorjgc+AqkWyMAAmcWC7y9Qh7F1jM3izV+R1bbWXj5GzV0dWsEHgGBVzsb/Kr/EbBtG65D4HBxZRI3YdPzmw+C4suXL9dZM+F+tQ00cfOUYTfn6JC9RgFr65qP3o5cI/sor3Yc5RvRG89n4zXSdcuxLT/OwusVi6tb7ZdbrnXL/o7Aq50NxuN3D1/3Tl/Pzr2+xb/3X6y4uLjCARI612+//fZWYBHovAk4+/B9tQ20ul0uCb4//vjjrdA9ew1YYw9pdNyzaccRG4hTfgRem37Vf/+q0j3K88wPE8roDdwleI38vbS4ko94frR2q/3yaH6eZQ/xRzzRP0LDlkeMq0uxMedfyn8vPu0+Uijdau9RVJnzvn79ei9Ivl1cXFUQOZzceBxiZx5Wr7aBVlf7koBdlX2EjrVkDShEWNtRkXJE3rW0bpwjcoxNihBj0x+C37tYvLUfl+A1sski6ejBKt8rHYIjfD7CmPvoaAzcCptXOxvM+bfC61ZytbvWBbfSN5NLMUVMkNPpyff3aqcVVzrg5jvzwHq1DSRWe/2jBKxfCdIbtGe/GdvDIueJB64jzX9+wd9Ywc/9rb7KPmLbEVr8YA2O+HEJXiObLJKOHqzydXE1QvW5xszvR2PgVl4S268UV+b8W+F1K7nafe/iKt/gkyPv+dXgsRPq27f/fzs1A9G3HOkUbzzyMGBDMFYPaHjZtLwZgYbezZwbaFXeLJCobpGXrw/VNePJcQ8LbOPth/bioz4xp3x0OY6cFf0GqwejPbrFhPvUj2z5tFe+UT9bQ3ntwRtfWB/8QNaZxbN6Vnt9WaX/6HSX4EURR6IyholtC2viLxvPub+JQWOLXv3ZQ2NzPziPLOLapgz0cK8u7t0LyMA+7SVmiVcuEy7yuWfMVvdLyuPevU2Pjtqg0R7kp++VdvSMD9iq77M8lLaoZ+WDgdiKC7wjHRUHbRWPxFpb7aGhgWu1E9/MfcxjB/pHjXHmacjRJvWAc10D5qDLtoJp+lVzKLKw2byHDmwjvjJ2pNE+aIiv0VfzaZ/3rJ+xg9/w+izNKg7S1x4ssA856Sd6XBfmjA+wdBxZK/FT10k8Mma4T/3Ilk+b5Rv18K80/DAe0IesGjMrcs6gOb24wihBc5FwmEBlEblMgoxnIBpY8EtHwAKQgCF/Vd4MIGSjCzu0SZsZ22sGLHZgG7K03Q2mzdpPb1vRz8aTl02nneDFvXrVg36afqQueenxD56KvfS1J5lAjw027Ep/HJ/14oWcvWsmI8eVkWN9P0fgKF7EmLFt7NE7RhzZLAzc38SXccu6IwtebGDcWLQwMAESv865l9Rj/DhOT5wzDg2ytY1x9WMTtMwxLk3G8mi/VHnqZTzz1Z7vYjTrwUBd5iJ1pY3ih634C62+zGQ7Dj0ylc+zPjNmc8xne/EFay7ptIV5xskT2u4cdmMnl2eBvsCTjXmw0CZkKAcdXK5rHpbwQGdbxVS/0mbuadiGzegTN9daXdirb9Iwhz3I3mvwQJt7QlsYtyGTSwxmOEhfe3xBHrbSo0M96HY90CG+9Db0Qa+PPOsnY7RHOauIC3w0t7BXecbHe7Tvq7ioHXAxuG6OZBf8LRqBQB7NTeFzykMfMrdalbdFO5sz6Gbzjhuw0Js0mDNQGc8kLB5Jq6zsq375Ko6uARs+9SBLnpSb99qYCSrn673rkvQeiHv+KAsbsXnlkmerJx64uq0hcBQvY6TGnXHvHjU2TGZa4xsK96x8Pku3lfwyjuVnf1Sbci/kWwUPCnxxXLs4aGyphzHlMZ57y5g/6rt6aq8t2Kl90niQu+dYP3zPBg++XdpmuabKEw9x91kcpHccnLKRI9J+n6vtFhtbOQWfkZVxlM9HMNXeUQ5lTcCnros8YOG9a6TPzFUMnLM37tMP52o8Op79CIecz3vsEf/E1j2Onxnn6k/alOf9LH6ME+nEaYSzuqStvTZWjCudz9LnuqE397u079EfPqEEq4KYxgpa0pCAcZ45q2gWHXk0eRIYZeYGcmxPnnSznoBCN3oNFPRw7TUDVtulNwHXzTXCbEW/mCSO6BrJ0wZ5fM7ew9BPHDk3ux8Fp8nhiJyZ/EvGV9fpEtmvyHMUL+jZE7XVuCfW2MuM18s9hQz5oM9mHBOXld9E6Tg21f2GLGVAl424haceEnV/1OeZPOQgTx/oV3xPm/KeA2Pmk7rAgAYdB34egilr7/5IrqmyKh4+02fL9c5x7l0L7ceXeuDxXGOONSXHgDU84JBrgOx8PoKpftS44RmZ6OU+L/M7vPJXHKrvo2d5R0VDjUf4sWEPh5EeefGn2qkvr3RWEUPEWjbPvBHWSXeL+/1Komg1MFjwWTNASBIUS248nGeOpGHydNEJgLq5lJ8baFWevKNewNWJTdjhBh7x5Bi+jwJ2Nl4xW9UvjhXrKi9tkyfHuGctRgms0uUzWOPn7AKvezTtuYfuZ9R5BC9jeFQ4O5d7VtmzHrzkIzazGaszXsbhlV+9KWO2F2bj6lRGfZ7xQY89+rBls3PqGPXqmSX91MVaKJM9xwFCDhl9EK26juaayq+drAHNZ/ps2DfL3/Iow4Md22gWRT4z5vkgDvisHNcAOue5d34FU2m1CX6a4+I96qGhUCSfOo9NrBN5dq8Zc6P1c04ZqzhIX/vZ3pmN67+4HI0f+bSjynOcvvrq3CVnlTHketQeHN+7nV5ceSATeDTBxbkMprq4gDHbnMyxEEfkvREP/oMN2MZVA2G22FVMtd352bgYMH9Ev/ZUO1Oeuu3l8ZkenWCLz356zPnZvRuLtUNnXn4aXUkm4lIDfvQ8syXH5cuxvp8jcAQv12q1uJrt2bRGme5h54zVzAvOZS8/8VfbbC/MxtWpnPo844MeHPWB+xXf1VN79awUAvCCAWuiverfwu6SXDOzE/007abPpj055r08ysAu6H3LQH4hN+mL6w2+NV/B5xogP5/Vs4KptNpUbZ3JkI4eeykU8cMP5tiTRWLSe+8a6q/j9M5xfwSHlJH3yqjrNRtPXLBv9azU7hmedbz6qs3ovOSsIobAHvvrpTx1vFd/enHl4tDTBL0GUl1cAACcSoeM3ECr8mYAqnd0eCh7xuu4MvRxb1xM4JN3Rb8FDDzZUl6Ocz/ywcBbSRgpT/055j1FFesy8kMaexIkNq9c8mz16OXqtobAEbzYf9CzH2szdllH2taeTV5jhdjMRuygay8uq96UMdsLs/G6P+rzjA+d2KoPq76nrXmPz8gb7R/xYt+OGmvk3qy5IenFbaSj+l2flVPx8K0T49m28NDWLJTMSSNf1TmKi1wD9OfzEUzVUfHbkpH+ju7F2xgZ0TC2Ffe5Dtq4gsNMlzbV9ZqNq5N5aVbixzWGJ1vKy3Hu01fnjIuRz9KMeorAGe7G7FGZIz1Hxg6fUFtgOcdGs0gSrAq64/DQDLi6kMoUOPn25M1AYIPnhpSOTc4CMbfXDDptl342rg/MH9GffOqgn40zVwPWwKq4przRvYcsm2bW+LR2j68GWaOVdZrZ/dHGj+LlQZnJKA904944ZE/WRpxnzGGDe1ha90vmC+fQp1zp1CsNvTZAk202XvdHfZ7xITt9kE4bU3f1Pee8xz/fCnCfzYMK/PELHCuN+qvfKedIrlFnyuPeWHCcHhzQn0176rjFE3Ky4RtylA+dzTfmVZbjGUe5JquYokd79UvdKSNtcp58Cg/rXnmhSXvkqb2+40euK+N5BunvCg5Vh8+z9ZqNJy5H4if51E0/G2eu7r1LzyrxhH/U9CP3KnuKtcqxEe81Y/uVRJEuWBjFPRcb06Bgs+CMzUVkHoeg50CWnmcaPI4xD/A8O+aGWpWn/lHvotKjH18AWl0jnhzTBm13bjYOHfKZp63qd3OBh3Yio8pTf8rm3qBCNzz1ynVKGdyrexaw0IjbKAlVeWc+4w9XtzUEKl7GTyb2lGSygo/9bLy6P+C3eTgSo+5vD2r4bMwjz7xBTzPJITvn1AXNbF8xpy/urTehG+P6Il19nsmDHvvTp1Xf1VV79xi+ip0yxUffKz6uzWwN1aV/9PiGXHgTX2hzzaHVDunE15yizdhNjsAOeVIXdFyjHGFM0GdDlnqNCWQ6xr2trskKpvBurXNiQSxDi5/aCxbY4Bowz+U8Nuw1scInZMmrj/AfwWGmz/jBvmyzcejwy/XWT3rmZvEj7vghHTKqvLRB2YwZV+iGp15bZ5U2jWJMfdgFtjbx1k/Hz+wPn1CCBQheBsgsqAhWnRF8Axh5NsAxMSMb0ASdhbCtypO+9gStC4IedAJyLnblyWdo4UvbmZ+Ni5kLeUQ/vG44sAOjKi9tSx+0x3WqvfYkv/euw1ZQu6FIPO/Z9ONWOsGF+AB3+ns34oW1zz1wxKaKl/GzlYzYY3kAwDPas9jB4SoturAV3DK+0GVsQpMxg1zjjTlw59l8YhxjQ236krqgmY1rg3Lq84wPemyra7Diu7pGPXan7+bHpAWHpDEuiYu9diTX4Av6XUP0jvDI/IstrhO6oFcGcrB7FmfKRm9tGS/6OzoLRmuygqm6a9xoB/rNAehQD7biJ7ZkwcU8ewBsVprrgm/KxpYaj6s4zHQiE/n4m202XnHRTmRwsZ4jO5ENr/4QA9he5aUN6av2qKf2zM8aOtG31VhLZLI+xhHrdct2uLi6pTEtuxFYQcCNt0J7lIaEyWblwNgqLI/KvYYem/C5HuyrMiteJsFV/qZrBBqBRuBVECC3kxP9UHArv7q4uhWyLfdmCNRi4SxFfLrhExCf1h6l8YkNmyisziqu+MRGgdWtEWgEGoGPhoBvJG/tdxdXt0a45Z+OwC2KKz/NbL1+Pt2RHYEUeRRW2HRWcYUskku3RqARaAQ+IgJ8M/EeHy67uPqI0fXkPt+iuPLt0CNBw9eBFkJnFVeP5F/b0gg0Ao3AqyLQxdWrruwL+3V2ccUPL5HJpxkuCi2e6W/9vfxsmXjDxCcsv6Lkq7yzvhac6ezxRqARaAQagXMQ6OLqHBxbyjsicHZxRUFlMeX/7UNR4/+hdY+vCimmsrDDvi6u3jHIWlUj0Ag0Alcg0MXVFeA1630QmBVXFknO7/VaL18tovxfdimy3rNhTy2kLK4o+o7+L8Ti8J4+tK5GoBFoBD4yAl1cfeTVf1Lfzy4WZsUV8FjUvCdU+rfVH7FHOUd4mrYRaAQagUbgcgS6uLocu+a8EwJnFwuPVlyNYMXn+jZrRDcaOxuvkY4eawQagUagEfiOQBdX37HouydBwGLBnuLomuYP2vm/87LNxpPmve6PFlcWjGJE360RaAQagUbgfRDojPs+OLeWB0eA31Xxf+f5uyt+b8Vvm/L/2LuXC/zOiuLo6G+t7mVv620EGoFG4KMj0MXVR4+A9v8NAQoY//QNhQxFFf/G1CP8CRz/aQjseu8f13d4NAKNQCPQCBxHoIur45g1RyPQCDQCjUAj0Ag0AlMEuriaQtMTjUAj0Ag0Ao1AI9AIHEegi6vjmDVHI9AINAKNQCPQCDQCUwS6uJpC0xONQCPQCDQCjUAj0AgcR6CLq+OYNUcj0Ag0Ao1AI9AINAJTBLq4mkLTE41AI9AINAKNQCPQCBxHoIur45hdzcH/Un/pv7Z9tfIWcAoC/HtYrOO1/4DpKca0kEagEWgEGoGHQqCLq5OWw38Re+Ww7eLqJNDvKOZViyuKfuLTf0z1jhC36kagEWgEnhaBLq5OWrovX768vY2i32tdXO0h9Pjzo+KKdeV6hjazlX9IlQKLP/3T7UcEjnyA+pGznxqBRuCjIfAcJ8GLrUoXV8+/oK9aXD3/ytzOgy6ubodtS24EXg2BLq7usKJdXN0B9JNVdnF1MqBPIK6LqydYpDaxEXgQBC4qrvjKwD90S6HA3z7j6wT+PpvNAoJDiD84K51fm339+vWNz3GeVxoJLv/WGl9hKFN+ZPnbEeSj//Pnz06/9diaspBp8oTXph8+2yvfZ3nx17aqAyzFCH1cjK18NZOHPD6KDfLkxzb+Vp6+OI6dzOmLuuGt6yFN/g2+xIk1SB/4u3zaIh7K8Nl+hl3KwH4wWflbf6s+qb/2K+uRuMMvdrVX9pE9g4/YgCx8SV2JM5gwn23F92qjz8jJNUIWc3V/QccYc8Qc9sLnejOObaxf5oS0s95DN1rv3E/pOzqOxik82FnxxZaZfuNNLJCRF+Nn+F/x6OdGoBF4bgQOF1ckOxIniZSCiuTiQUDispGAoKOH1qTNs4mK5JgHsolMGbUn+cKPLGTkH9qVlmQPDXKh4VIH9zbtSVkeDozZ1Oezvfw+IxvaPAzUu6cDnMBQe/WL8T1MPHjFGp3qxZ/ETP/obdimfeqXLgss/UU2unhGNg178R0+ZUjPuM0xn+0rdhx0yEIPOpin55lr78BGD5e20I98Un/t0bG3HuKObBo9vnKlXuaO7Bn9pscH4lld2IV81kAsec7iZ8X3LVuVi93EHvIZq809z1pgH/a6VsjnXlsrb31Gj76Ju/zq9hk9yCfmuB/5r15kwg8vjXF4GKdnDnxX4g0foUcGvWvM+LX+Vzz6uRFoBJ4fge8n36IvJCWSez3gTNgkGhpJiCSWB7SfdhmXDlp5SXRbzaSZNNhh8vQw8DnpTIyMaceIzuQpb312POUxpg/6pQ7Ga5vJTDpwg27En3Tog441yTdS+OZ4FmjanbQpj3swrTbKhz259mLOfG3yOF6fHa/YYTsxUm3U1z1MlJv9yKec37uv6zGyBcy4aju6Z7JYQpa6kMO9DXzqOjmX/cj3ma11jSyiMoaUx9xWs+hOm0f0xmr1G53I0P+qDzvABF+4p2l/jVPmoCOuqp7VeDNOV+Nv1f8RJj3WCDQCz43Af0+CDX9MciY8nr18Y2TiGSV9D4OaJJUr78wEZJJMM9EnrcmP5Kld9iZwnk3AJuSUUe2uz9Iqw2d1I5/m/IoO6LEZG+HjAEAv1x4m6BvRuR61YK12ohsZrCm6Payq3/rz5lz8R3lZRDtdeeqzdMoQO3XzXC/n5J31Kz7NeBnfWw/kV9x55som3aV7BlnKGMWCsVJ17q3nyFZk1DUCB2gzjoytXHPinHH2tjLUgf1bDTo+HMwavkAzkqMt2ElT90gWMpivzXHk18s5eIzT0Tqs+I9s5GlrtaOfG4FG4HUQ+PEk2PHL5EKCmF0mnkxKKXY0btKRN+nz3iSLDIoAEjmJygLGxDqzjXF1wT9q1b76LI+6fBYb5NPgmx0YKRPboWOMgxK5FFkWg3uY6E+lm41XO9WjTWAqTR5E1V/9dtw1cJzeOcfqs+Pqw2btxp7ZhZytturTSMbqemhn4q69KVffnBv1ymBu5NtIlzoqpqu+a4dy7Ks8xonL3C/Eaz7zockij3FkWNyhB/tnTd+we9a0aRRj8ouhtCNZI3zlF49Rj0yaa6kudaz6r64urkSu+0bgdRG4qLjKT6wzaEaJDNrRuEmnJq2RbGgzcSOPZE/iNbGOknDKkifHvK/21Wfp1OWziRf7aKs65ONwSbtXMZnRzcbVx7w04FffBla/q7/67XjlZ965Suuz/cgm1viSdsSnkXxt2VsP9UBvAzOubMq7Zs+MdKkjMZZuZT1HtiIz5anDDzUUEawzvOm3PPl2C159x65Z0+Z7F1cr8aY/6Tt+XeP/DJcebwQagedG4MeTYMcXDggS60oigo6kU9to3ARbk1blrc8UI7xpQSYyPAT2DjIOH3hGrdpXn+Uxofps4vUgUUcWTNKmTOVUulVMZnSz8bTT+xFeaSN2a6c+2G9hXnnqszK0A5vBAd3gd0lT1opPI/nauLceI3x9e5Nyz9gzI13q0F6ej/g+shUZKU8dFlQUQK53FtOz9dIe7J+1lfVW52hNr/1acEW/tqsLv7Jd43/K6ftGoBF4HQTGFcbEPxIRSZmLT7G1kXxMpPVwlnY0vnV4yAcNSbYeepnAlcPBXOl49tMxPXbU1/Pyc8DY9DflwVcPp7QDXg8E+mzSqUNb0J3N8ZrIk4Z7ba50s3H1M48f4FB5HddG9IwOXcYtHirmyK8YZSGsH9BZiHKfuur6MAdNfUOiLHptX/Ep+bwXd22p48plvmI3woi4MYYu3TMjXdqVOo/4nnzKop+Ns0b4wdd+rGM2xpjLPcJ9XdfkyXt11vWmgGP/6D902dCBbtZB3cpKOu+hqzKYk6fqZw7dxpt2GAPKvdZ/5XTfCDQCr4PAoeIKtz1MSVT+PocEaJIjAdFmiWw0PktaCbM0JHEOQBKcB2Ee7BY1lY5ndNNI2j4ry4O/2pfySML6Kb82Yg+8+p864IEXHvlM8ukXupBjskZeTeTqs5e/0s3G004OJO0RB+2sODDO2Kh5iCILOjGCPnkydqBLPuhG2EGDzVzSV1/TpiM+JZ/34oYvW+shXdpiPOIb4/S09PuSPTPSpb3oEOMjvs9sTXnqoLdwQ1ctQvCVcdade3ADP2PLdU15eZ+/WRIf7DOeoNXe1GGcpT0z+5GBja5J6s+9uhVv0CHD2MBPCq9V/13HtDft6PtGoBF4HQTGp+WOfyRDkx/JxqRFovETpGNV1GjcpEOS2mokpSyCSHLYoU55OcwqHc+Z1PDBRIxNzDNW7UO2h4Vz2CuvOk2weZAgL+3AVhN0Jnns9aDw8PBA3sNkht1svNqZOIjnyMbqr37Tg1HGAwcUWI94iJH0FbpqkzLBXVqwRy5j2LfVVn2ayVhZjxG+4oCt2qsObEqMjKWVPTPSpdyK8arvM1urPPVAT3xwjRpryJx+z9Z1xMsYa5r4IIu9g+82ZFpgq4e1yjazHxoxT3rv8W8l3jI2sBGbaCv+u47yqLv7RqAReD0ELiquXg+G//OIBEsCJsl3ux6BrYPueuktoRFoBBqBRqAReEwEuriKdeEtAsVVf7IMUK647eLqCvCatRFoBBqBRuBpEfiwxRVfN/G1A6/zufz6jq8dup2DQBdX5+DYUhqBRqARaASeC4EPW1xx8OfvebinyOKrwW7nINDF1Tk4tpRGoBFoBBqB50LgwxZXz7VMbW0j0Ag0Ao1AI9AIPAsCXVw9y0q1nY1AI9AINAKNQCPwFAh0cfUUy9RGNgKNQCPQCDQCjcCzIPA/AYBZ6+1YdaAAAAAASUVORK5CYII=)
6 e -3
2 e -9
-2 e -9
-2 e 9
2 e 9
Seja o operador linear de IR2 definido por T(x, y) = (3x - 2y, 5x + 6y), indique a alternativa que determina as coordenadas do vetor
IR2 tal que T (
) = (- 12, 8).
X= - 2 e y = - 3.
X= -3 e y = 2.
X= 3 e y = - 2.
X= 3 e y = 4.
X= - 2 e y = 3.
Existem funções que relacionam espaços vetoriais ou subespaços vetoriais a outros espaços vetoriais chamadas de transformações lineares.
Assim sabendo que T: IR2
IR3 associa vetores pertencentes a IR2 do tipo (x, y) = (- 4, 1), definido pela imagem f(x, y) = (2x, x - y, - y); tem-se uma transformação linear, se:
f(- 4, 1) = (- 8, 5, - 1)
f(- 4, 1) = (8, 5, - 1)
f(- 4, 1) = (8, 5, 1)
f(- 4, 1) = (- 8, - 5, 1)
f(- 4, 1) = (- 8, - 5, - 1)
Verifique se o vetor dado v = (1, 2, 4) pertence ou não ao subespaço vetorial com v1= (1, 2, 1) ; v2 = (1, 0, 2) e v3 = (1, 1, 0) assinale a alternativa que indica os valores reais de a, b e c; na combinação linear definida por
![](data:image/png;base64,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)
Com os valores reais em a = 2; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 2; b = 2 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = 1 e c = 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 1; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = -1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
A é Linearmente Independente e A gera o espaço V.
A é Linearmente Dependente e A gera o espaço V.
A é Linearmente Independente e A não gera o espaço V.
A é Linearmente Dependente.
A é Linearmente Dependente e A não gera o espaço V.
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)
Os vetores v1, v2 e v3 são LI porque o determinante é igual a -2, ou seja, diferente de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual a -1, ou seja, diferente de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual a 1, ou seja, diferente de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual a 2, ou seja, diferente de zero
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6VhUwvi7ycF0M2lP1rWZI9Ja/bnPPC88q20rPWQXFBXo9X23m5MCbPqXoSd/s5yFB9h+oHxD2t5wWO1fQedzjn9WXfy3aqbYhLLsf9nCCN21D6Ajb0sjjnca8XaWU7ttoHl1mptnGdq12IKy3baoexctmveVwm9nFdXDdkuWyn1ceIe7/sQ+PBy0UntVVtN6VX/3b6dOS419vTVXmk034dS0PUMpDvedl3e7gOHHe9qW+ffhx3e3BO5da04PbDxpwXxF1nl9Vijh2rnsRJD2xJ632LuGRVu8+RVevIeckV1MHlsd9Xb855ftK5fC8biPf1e9cbWroJ1UN6yr4qq+qs9LKZp5U9JavaV/H9ZrTElmJDRiNty/gYyg0nQ4AMC3PLc7mtdJzzRlJZMjyQh3Stc56/ygLiqm8tf0xeheOch7GykOt2druN2bDaH1T23LyqI+ym3LoP7Ls9ke11HpPn1OOttoF6fI5da/u3aOkMLdlTdQTqRh0B+dp30A0dHeS4TYXr47KdPv36ZKKX7ON5x+xay59SbkuH2gcct81YO7ocymjZpkVLbm037xt99QRkVZtBraPLGJIHLZu1GCoDqpwhe3r9XU6VCX1phdvP21O4XlXHylQ7+r5wPWvdKZP0Dmk5PlcWePopZbXKEBx3m3h+10PU/urU9Ojl9nTGZFe9gPTkG8vr+0J595MdPTpkiXId1KXdVnl1+XLZYVZndscbb7yx2lsslg2xJZ/HFmO8+eabq702c+UN4WXddNNNq702KpMw1mZ1yXVOXlDbUdchnnrqqW2yx9rv9ddfX+21mSMP2ytdn5633HJLt3311Ve7LcvnNa2XN8U2vhyPvn3UcqTLW2+91W0rff2Kdynvu+++rXNDeJm+dM8jWqe1JC+kp0Bma0ycOHGie3wAZ8+eXSwn3215VTZhil2nltuHlye9psB7JrxvAjw2nPPeSdX5yJEjO5rDeNdseWHd0t/nzin9HPyRC2EO6L1T/DHQ2HxY7TUH+tBO6wdT7QicV9qxOrXwfjtXVt/8AFVv2bMvz9A1hX4qvQhDcxnvK5KePqbHhnPeYTx69Ohq7wpVL+YjXbOH5iaYe81ZB5MdraVTti288MILqzProa88Os7SG906PjYAdgIN4WWPXezH2Gt5YmiAgZdJGGqzqtOcvM5YJ17eXVwlezcvEc+R5/1GoW8i1yTLZP3iiy+ujl6hyhiyDe8e4PQoLfr2UW2ni2ff5DfUr3C2VJ6/r1FRmbzHQF0la3kH2R0XQ33WL/KAzNaFmJewGa+UxcuyDz300OrMFVS2wlifm1puCyZnHBWVhdM3FX8HhUm89d5JH1VnLrCaw+rFZQycLXSnHvQxMaWfowfONO2sNHMYcmiH5mScecaTykTXIaq9xBQHzNtXYc4/AsyZL4bG4lz2WpYje/bNKWNOm+tFGPoFAuYebkS4qRqa91pcunRptXeFqhc2ufnmm7f2h5hzjVgXo46WJse+F9/2minlaTLFW64daTcwiJhwn3jiidWRedDw3ui7lTcEch9//PFV7MpL4mKKDblA6KVALnzYkc63m/bWJKa8yPeVCVY0KLdv8hwDvXyCnyOPVYdTp06tYv2gM+X4oNTkulPb+AUUfVtItr8ESr+pqz4wp1/VizftIXtRD8pUu7Ev/IXvO++8s+sf9BMgP86b9HCdq8yK2sHvcMfsWld9dlJuC03U1GfOipbKx7lptQ8OumzlyEHzc9hCK2Lo4/3D6zeE6gFT+7nQBbfqiz37Vgqot5fhTh6QT/0H2/ocAC5XK4MVbEp7qo8jp67ucJ6LODBu3XY48bXcOUy1o/rCXszxu5VVrz9z5hTgeN81hbHqc8AUND8/88wz3f4QyNb4p63Jpzw4Sn5Thg66XtW5ia33g91ec/aM5YVkEiT1wPPgFjUdAZYNvvVcdGm4bc9Nl4bq0rEVVYbKQ4aOIXPZObbkkoa4wznSiVZZ5PH6kF5lEKRrlQVeL6h5oE9exWXNKYvgz9OFnyeojrK/61WfgXs+Qs0rqj2lB0H6epsQ13kF5a31qzZAh5pnSF4FvT2dy3ZqOoJTz8k2bGv/k30Uqv0qnrbKqnj7ESS3Hhfq537ObcV5HZcNhLcrwfuLl9dnUyF7tGwgGQqyK6gMt8lQuRyrfZo06l+1Pugz1I4V9Tvvr6D61bJFLdfrCN4GKqPVnz1dTaO2U5BtpJvSel9AHumkNzKGbOC2r3qqHD/vsjwv5fTp15KjtOC25Dj18X7l9VMQ7Pe1kZhqR/A6EaRH7UvS2SGv96OpsoDzXo+aB3SMMNSm4GmrrrU9CK53C+oiu/WhdnV7U1cHvb1ch/w6TrraD/y8grfdfnADf5YFh+sI3eEMLfter3BHxJ2s3xleK/ZiFerYsWO7+i0pvePQuiMOV1aCeCw69VH7fsJd/fKCNqn9aOflhXbSCuEcubuhNTbD4ed6aNdZP1gawrUOL1jWRyZcSOa+P3Otsrxj3PpHgXA1PK7aVIdc77mMOUM4iydPnpz1GDaE0E8crRAMrfZwR6/AhWk3q0DXArwDgy14x4j3IkIbHhBs4mofq5m8c3P69OnVkX7o61ntDmHvyKPDEEIIIYQ1kRWtEEIIIYQ1EUcrhBBCCGFNxNEKIYQQQlgTcbRCCCGEENZEHK2w8fBzC/zHG/CbK/oF4RBCCGHTiaMVBsGx6fsUx37AzwpQ/pkzZ7ofDvWPf89BP0+w6fBv+PvlSB502x401B0bhJ1BX9VPoIQQ+rlmHS1dWNkCFy8mhrC3YNN125XfJeK7VvxwKD8IOecjvn3QN7Iytpnsp7O5E9zBUJjKTurGDcamOcTMq/ymGr8ORKjz7bqpdmRuoPzr1XG+3uu/6Vyzjha/aswEkF83Xi98amSdnxuh/fTjiZRDm+7FD0Ii53r/EdKwc/jhTzkZ/Ir6Om826P+b9nkSPu7OZ3fEQc+3zAmUrw+VX29c7/XfdCY5Wtw56M6NIHQXo+DetJblax6nysUrF37X6JOY59FdnnvzSq9jbLkjZCWEOzCdF16O0vdBeUrnd1O1Hm4H5LsdCIBOivtdoPRWcF0rNa3LcV0JQnlcJ33LT/HW3bPXUemBfcUpn7x9acH1que8LWoZOl51q7b1dhkCOWon5JPPdat35l7G1Dohk7jq5f1CuP6tth6qXx1/VWfhdq32c2p/8rJaNiI9xxT3+s2VJd05xjhlvNZ8Skuo9XBZtX2E69pKwzGdJ0zl3nvv3RNHyOvgfQG9PV77BLbuw9O5LafKaNkVOQ8++GD3bUMdV3tLDvp6GRyvxwjgdvc+3Ne/+/qI0lBG7R9uwz654LpUGWIovzOUzo/LriA7epAebKmbqPUkrcpAJnXmvI5LH9cj7BNLL3gQvnJNMraOjuvL4TUdX+MeEq8vagt9wbvuOzUPX+nmmMr2L5NXfUhbv1xO3I+Rpk9n0vkXwaVfzaMvnssu5HM9FCcfsJUs6UydBOe8XMfTkld1qXnYr2UoreIE6Uha6SebK151RLbKUt0VV1q3Re0vipNHOgH7pKFclQ0t3aR31c2RbgI5XgfXBfmyF3iZ4HmH6kSePn2gpld9ptbP9eij2pW42oe8Xs+qq+tWbaS4yyIu5siq9kZftzeQ3o+RRvXiuNexBWUhQ7Ykj+tYdRiSWfUjLjuMUfOKKsPTuS7UQ/tAHo871Ef1rfV3+/eBXKVR31O89h2dl3zyehzqMcW9npJJmtoeHiev8gnpJ128fuRF5yG5pCffEGN6OSqzwnHX3dP5OY65PlUe+xwTXuc6xkD182NhfxjuVUvUaWuHoTPQ2R3v/LVTVIYGijoYZTutPCAdPX09VvVt5QHp4PSlhVY9Xc+qc58eUM9BHUyi77gGk+P6t+pS64AemvRb5XBO532/VTb1qfYRXi75pkwAXl61Lfh5p+rmZdc83iatOrXaSbjcoXTQOu91GqsfZbXq6lS7en28bX1feFm+D630yMV2c2W5vaHWuyXP60Faz9+ilgnIRHbdh9Y4EejHOQ9u4yFabdoqy+tMevK1aNmmD9eT/arHGG6jWm6tQ6ue9Vitl2S08PaGlnyvH+fV3jWv4+e0LxlTGJKNfWqfa6V3O3j51aZuf6ht4Hlb/T0cHKOPDnn2u2zobqmYZUdfftajOAXic9DSr4LgOfOyE3XvAHDclzpvvvnm1d7eUN/3oUzeP2jR927QkSNHVntXIN2lS5dWsWloyb2WgWzs36JvaZs6OJKpr/dPQfq0OHr06GpvGvpPwbosXut10003rfa2o+VvwlNPPbU6eoXaH9BtSPc5uBzXm34rxuo0Rl+fEkP143HViy++2JXrj5YqfExY+rHfB7orHaHaegrqYzuRNdRuta+rr5CHd+2WF6utsvrkjPVbzXGEOoYqlLecP7uwvLh1dt3tIxnNdwR06cMfbw2l06MiBQe9Nf/6oytnt317p6j9uNao7KF+2+Khhx7qxgY899xz3Xt0ok8u75epLTnnj+mcqXoNjU/lJ/h8Qh945ZVXuv1XX321247NEWHzmfSOFg3NwGSg0Sk0oSy98K3JRmHOC8Y+WSnoZUqcLeI4XN6Zd/rv/X3USZk6VsdJ9E3g1TEj3VxnRNQy6kunTt97IXVClMw+R2Yuc51IQT1oT7V1rVfLEWSCuu+++7by+IQJtT+g2zomJpWvoH8AGKvTGH19SozVjz5AuWz9Jsjh4iH9FFqge02nf0SYy17KgtrX1VdkC+YdymBOob+0GOu33o4KU/oScxZzoS6QO4Vx62W3xjdtzMVbadC5Bf2KedPbvqLjOMEtp2K3fXs3oA/XGpVNPebA9QN7cq3CXidOnOiOj8nVS/0cx4mt43OuXn3jU/kV/B+KkI8DRvkuv+/GOmw+s/7r0Ccd/sWeFayd3sVxx0GHGsOdnmPHjm3LQ8ftu+uosDLgExd1YXL0uznkMZnU/5whLcefeOKJ1ZF3Ov3DDz+87a4Se2CXnfwEgWzqdTp16lRXRuXOO+/sJhKlZULAKUF3dPV6oTd1nXLRaEE5miQoh4lZE9dc1J7YyR1C9Hv88cdXsSsOliY5d1p9ZUR9SOl2q1sL2dMnyUpfnca46667to0hyvBV4Tn165uEcUzdrn3owjR1PA2xW1n0U3eKJM/bgDpVpxv6Vrx5YR3byZaMD28rxhhjbScgk3ajPYGL5FB/qWgu8vllCG/rZ599drXXRjdXQ20x5EDttG/vBa4Xq1JO7SMt6B+MIfA5fUiuGLopnZK/ojYbmk/oR9jYHTDXmzp7ew+tZragDembmm/C/jHqaDFAtcRJ4I6RxqfR8ba1zKowtRGZPH25XwF8aVx3ZcBdK4NH51566aVOzhQ0WZNPTgh3EThfkvfMM8807yKB40zUSquLV9UJfSkH+8xFNmUASR4XgNYqYU3L4NWKQdWVAbybn2DgIsCkpnLUB+bC3bH6CxMgckVtC+pHoE66wyP4xVV9CJ04xxab7ES3IdDLdSBoohyq0xjoif7Kj42n1o82lS4EaPUT9QlP2/eYkX7rfY+wU2dpN7JwJNV/lQd53gbqG0B9dJwy9cjIwZbYVrbEefe2wnashEkOYWgFwXVBJv1gar+rfYk5k/6vx0wKfrMk0LOOkxYc977DBZp90AVXgXq35tHd9O3dgj7YXzpWWn2kQhr6jd+oDsllTOu42rTad0wvMTQ+++YTtZkfJyAL6O849DqOfuFwcMPSa24/RwjhAODiwkWw5TSEEMK1Cg4wjq1fknHAWFDYzY1yOHhmPToMYd3wiIeJJYQQrida7+Oyyt23ahkOD3G0wsbAkjyPfngXL4QQrid4LFkfX/PYUI/Iw+Eljw5DCCGEENZEVrRCCCGEENZEHK0QQgghhDURRyuEEEIIYU3E0QohhBBCWBNxtEIIIYQQ1kQcrRBCCCGENRFHK4QQQghhTcTRCiGEEEJYE3G0QgghhBDWRBytEEIIIYQ1EUcrhBBCCGFNxNEKIYQQQlgTcbRCCCGEENZEHK0QQgghhDURRyvsC7/4xS8Wn/vc5xY33HDD4sYbb1w8//zzqzMhhHC4+fa3v714//vf381vH/3oR1dHQ7hCHK2wL5w9e3bxwQ9+cHH58uXFyZMnF9/85jdXZ0II1zvciLmTwk3ZD3/4w27/MHDq1KnFj3/848XFixcXr7766qHSPayfOFqHHN1JHQRMikyOHlit+uQnP9mFn/3sZ6uUi8Ujjzyy+MxnPtPtf+ADH1gcO3as25+KJmLqe61C3agjda18/etf35M75YPsL+Fgof/QjzYRbsROnz69uOWWW7o+2hoDO0Hzxlx5OEqa0zxgw5YdX3/99cX73ve+Tv9bb711cdttt63OTAN5yA/XJhvhaG3yBDCFPv25oF3LjsE///M/d5MKq1THjx9fXLhwYfGTn/ykO/eFL3xh8fGPf3zx3//9311cEP+Xf/mXzvGaw3333dfJx4HbBGrbamLWnSz9gX4xFS4EDz74YGdLJutNpF5w9pq5NtsvrsVxvIkX9ptvvnnx5JNPduMAx+Xuu+9endl/KPvpp5/unD/mHeY3xubtt9/erV5dunSpOed/61vfWnzlK1/pnK6pMGc888wznfw+6H8+/q7l68q1SFa0wo5hMqmrIzgJ//u//9vtP/DAA4uf//zn3T7gZH3pS19a/MM//MPqyHQOeuIdA92YKHeqI3YbmmgPGh7lnDlzptORcP78+eaFJhwOuNHZpP6GPrqJQi/G+0HzW7/1W6u9d/jYxz62+Pu///vFV7/61cVLL720OnoFnJ93v/vdi9///d9fHZkGc8ZYfXUTRmAcEt+rVb+wfuJo7RNclLhYcceuuxLuZPxOpV64dLx1rsJ5paUcp09OXR7XaoyW2xXGym7x61//upt0fuM3fqOLy8ligsJB++IXv9gdH6PqOHQnp5ftCb4yUmW4fahbbRcvw+1KALa8i8Fkp/T+iAJ5jz76aOeMcEy6sC8bA7J1TjqK2gYV7zeEoUnX073xxhuro1eYal9WGnw1kffsuKuveJ0E9pDN+8rrsxngzHseIRvJFspTbef9d47dpkCZffKok58jqL5Vx2qzIbzMoXxe15qOc36TRJpWere9U8eF15u4y6o3YzpO8LbxstRfRLWlj6OK24dHkhU/T5jbB7iRZA57z3veszpyxZ7AGOEffYb0c2q9+nCnWOPwrbfe6rbhELBswAPn+PHjl0+fPr2KHT769L/11lsvL+8+un3OY+4aJy9cuHChiy8v4F2cvNqv5xzkcU7lk8bLGZLj+jmeH4iTt4X0Z0ua1157rZMLjz32WLeF5YW5k6OgfEOoLipbcenvUH+VC5RHPtXZ9Scue9V2kT1hqLxqu5oWubWOnHc9PI30FMiXjpLtaV02de2zJ8c5L5AjO0mudKp1GAK50s9RPVyGyhgrr2UzdHX92a/61zwc6+u/vj+V2taCcr1s9Ec+eD+CVnu6zFrPPkhT69bSrdpa+kgH4rIjcK7GPT/1lH6SJcZksa9ykdGqJ/Jrv1Cc9C5P5avfOJTjaZHraYfarEL5nGerPOjy9NNPXz537txV+nrQuSGqrsRdtz5UXqv+YTNp97B9hs5FJzus9OnPIGJSgDqIGIgMFof0fQO0b/DWSQ76JjNwOeSr6ebKU53YIpu8TESE3dKaeKbY2mnp7nJbZWgS04TWklvLq5Nfn1xvQ0/j/cH3RUueaLUZVJ3A07ZkEuf4EMio+jnIl4w55dXzLTt4ncbqJ7wPkH6sfhXkIddplQ0cQ+9aF9ehZYeW3lNw+zmtMjxtLU96iyqX9JLXKtPzV1mkVd3ZTqmn27zKg1abQD3u7TTWZhWOoTtb0hDQn7mNG8rdMqTrENhf9gyHgzw63DDefPPNblsfsczh6NGjq71hObwX8OKLL3bH/dHCcqBvy/PUU0+tzgyzvMvrXlr/x3/8x8VnP/vZreX03aBHSQrE+zhy5MhqbztuD+Cl27F3IliW570pbKFHhP6YY90sJ+HVXht/5IB+Qwy9XD/HvsBjFspbXnxWR67m4Ycf3np/5dlnn+3iYm551Q6qy9Bjk6H+u5zzukeUHK+Pp3ZCtS36MobpY143xpn3w5qPvoveU/C6DdlvqN13go8Z2VBhDD2e4xE0c0TNVx+lVlvcdNNNq70r8JixPgYXffOA6GuzIZbOTfduFDC3ffrTn77qH312guYWwtiYB+Yg2gE7hsNDHK09gIHbel+FyWJs0Ldg0rnnnnu6ixkXBsIcpMsUOQxajrOVI8GAV3qFKQP7Xe961+IP//APu/cXHnvsse5Cpgl2pzDBVV36/mOxb7KsbcMEXd8b6YO2pUzakovL1HcvWkyZSMXQRZd24sIte+gC0MdQG8yxL3KoA+UNvfR///33dw4A6dkSF3PKg2oH1aVeeJ2x/qtjOGC7vRmotq1jXhdR+pvXs+aj707pH8g5ffr0Vh2wZx+1jL3EdVCY+o8gtAXpT548uXWDp34lWdUW1bFmvsKZbTHmNI21WR+8G/Xv//7v3X8j/vKXv1x84xvfWJ3ZOV5nhT4HmXHPHOQObzgcbJyjxSBgYtqL1ZD94t577+0mbb8I42QwWez0v9BAF5MxWzBRyEnCfuhy4sSJLg5T5MjxYDJB3hT7c0fnd3X/93//t/UfNx/5yEe6CZX/PNzpnZ8u2FOcG+6S+bdrgf3Jhx1q2zBZPfTQQ6vYNOrkN3RHDa1VM/I899xz3T7thB4t1GfUpuhe06q9gFWjFuhMH3ziiSe6OGX66tcc+5IGWTjt/lJ8C8rFAaBN2Mp2Y+VVm2EHyvSVJ+riMitz+q9fzJlzZO8pqI6uG/k15l9++eVtzsgLL7ywSvWOHVxH+q5W/jQHDtkJSIecFnfdddc2W6NbX9q5MHb6+u4c6kqznB10pg0FDpmPV+zG+VY/rPMAcTHWZhV32DSH/d3f/V23Ys+Pk3KzM6Wf9UF7u65DoHPfT0BQB/pL2GCWDXfgLDv/1jP/5QDqnlMvPf0uflhAX/RWWA7e1ZkrUD/qKfTc3yGP6k16ySIf58hTIT3neWav9LIl9MmRnf2cqOcIrfYgT00HqpuCy55LlUVo2QFcH3+HobaN16W2C5CGMmo+t6ufc3uyFTovXapdkaGyVU/h9Va/cD05pvPIJ96ilolcT+vleJqK29aD19eRfdg6Y+XpmLefp3UbtGwOtc4E9Khl1zK8fR23tYLK9HO1DTw9wcurunjZOteybasOfXpz3NN5Wuzh+pLG2wE7u9ya3mUriCqLtGq3akuh/kIgDem979T+N4SX0bKln/c6OdXOBNmj6uJ1nQtt4rJa+rT6M0E2VVuEzeUG/iwbKYQQwh7BIzG+fuCPC1l1WF6UR1e5teKS93BCuDbIO1ohhLDH1HeIeMwHQ++WAel41B0nK4Rrh6xohRDCHoPDdKu9AwZTVrNCCNcecbRCCCGEENZEHh2GEEIIIayJOFohhBBCCGsijlYIIYQQwpqIoxVCCCGEsCbiaIUQQjj08Cvt/gv//vWEEA6SOFqHHCaXTZpQhj4dMgT5aqBefGaDH3DUJzDY8mOQN9544+JnP/tZl4Z9/U7REMi51idf6uefGHGw6U7aprJXcsLhY5Pbnk9R8RMafKqG+eCifcZnN+x0jtWncWpgTmMOe/7551cpr5TBPPatb31r8cUvfrFLx3YM6kna3XwKKKyfjXC06HS6CwE6zmGayKv+gsGZATCN1157rfsOmX5thO03v/nNxYc+9KHuW5Kf+tSnuuM///nPux9z5KOu3/3ud7tvjtXfK2pBOzApbcoHWVuTtztJmkCnOJCCvHw/7Xr+scu+sXiQ7KQtDwPYmrBJ8IOwOFzMCSdPnlwdPRj4KgDfesX5O336dBfefvvt7hxj9Mtf/vK269xf/uVfLv72b/928bGPfWxx7ty5bo4bg285In/s+6PhYMmKVtgIPvjBD6723oEPVP/N3/xNN4loRYsffPz1r3/dTaRf+9rXumNMSH0fGRbI8A/7biI4gbtxksjrn3wJYZ0wnjZpTKEL8wBzBDdqm3DDwY2i8773va879v3vf3/xla98ZfHKK690x5mffvrTny4ef/zxTv9/+7d/624wx2DOyI/gbj5xtPYJ7rJZceAOkLtbrdqxsqF4vRPX8da5CueVtj466pND+X5Od1e6A1cYKpuyhtL5ecLclUrd1fmq1Y9+9KOtr/KzosU+dpQzVnGbDz0CqPV2XV0GQasTyuPtWO/ydZyAjQgPPvhg92iDY9KJfJyjXNWXLWkoh3Mt2dJT+cVY21Cuztc+44zJcdsM2Zfzbqe+tLUeQFryAuckg6C2IM358+cXjz76aHdcMmq7ug2RSVx1VB7Xk+B9we0xVN8pDOkGyPfzBNW36thqmz6m5htqe84RoNqRwDHsprjSCtLrnNuRPMQpr5XXZRLUNtWWlO9UWw7h6d54443V0SuMtdkUfvWrXy3e9a53Ld797nevjiwWL7744uLOO+/c2r/tttuuqoOoNuhLhw2xndu6L21YI/wy/EHDV8j1ZXRArQu7+CL6flP1F8uL5NYX6PWF9RrXF9ipL3F9ZZ682q/nHH31XuWTxssZkuP6OZ4fiLfaQ2ULfYleaYlThlD6Vj1AtpBM0j/wwAOX33777W325di5c+e6fbakb9UD0EFygTihhctBR+Vj6zLUdiB7+3mX01ce5902gIzajm4rznk5QBrZ2/MjXzoCOtS02ldZO21j16mvvkA+rzP73q6CY56OsqRD1afa0W0AqhvpBOmlo+R5HpUn23v5vj+VVlvCmG5uW6WV3aW3ZLZk9UEZylfr6qgMgT6uA/E+Oyqutqn6e93AZUknxWteZLbqSRpRdXe7AvvSrYJenhY5tR5efpXtYA/0ZktgLrvjjjsuv/baa1txIM5xwT5B551qD8XZVijD9a12CfvDRli8To7eiQ4DVX/hEwLnfWLRZOKQvq/efTZBfp0wGPR9A9/ltCaIOfJa9Xb5LZ3dJhXZh3wE4k8//fSWU7UTqg7sc6zSqjf0TWKS2zqPrWQX9ltyW+W5PVtyax8C6QGe3/eFp630tcuYnCqTfY61qGmR2+pXqrvSev8b06eeZ59jjtu+1Q5enpB9VD+vxxittoQx3WpdvI1admjpPYW++rTK6GuXasdWnZEn/WuZsmvdF56Xcsbq6eW35LX0g9Zxr9tYm1VIT/lskfve97738mOPPdbNay0nagotHVptBa20rXqH9ZJHhxvGm2++2W3r0vAcjh49utoblsPzfZaoOe7L38tBuC3PU089tTpzNTfffPNqrw0vpzos39el+Ba8HH/77bcvPvvZzy7+5E/+pPsPw51yzz33bNWF/T788UWlvgO2nFi32qrFpUuXui3vifBoU+XvJ0NtUx9/0OZ9jLXxVPu20GMwB1svLw5b76/Q/06cONHtgx4NKoxR2+7IkSOj/5FGmV6G0vM+zPLCuVXn3T6GGdKNcy+99FK3j504znlR24Vx37JnZc7cMtb2c/GxP7ffKG/fvEVbSB7j06lx2f2tt97qtpXaLs5O+hMsnZ7uBX3SMq996UtfWp2ZD4/IVVcC8Tn01TushzhaewADTxdWp06MU2GyZOJhQl86w12Yg3SZIodJi+Ns9Q4Gk5LSK/S9WDrmNNUBTTlTJm+9HL+8++veW/j0pz/dxXeC11+hBbr1US9gc9oW21Emk+xO3ucQcy96Q21DGy/vxLfsUS9EzlgbT7XvHB566KHu3/RxCtDNX/jlglXLG3ohuLYdDvJQfYG2qmXoP7soizj15l27Kc5NH2O66YLKMert9aztwrgfchBgypzgjLX9bthNv6nzFv1E7z1yvDo+NS671xtBMdSmY202xJ/+6Z92utCWOIb+Ew9z4EbE7UbIP8JsLhvvaOmFyE2G/w7hDpjBLngBsV4g5qJJYOyumYErJ4lJoK4ATJGj1RwuJsibcqd+7NixbnVBVCeCixUXTIFMZOuC5dTJixfbP//5zy++853vdI7Khz/84atepp0COvCfPGPgzHm90Yf6cOFiUvOysfVO2tZXGsfugnXBdEeVPFx4Zashe4y1DchRpN/26TKljafYdy7qh/QffrJCEHd9KvWm5/777+9s5v351KlT22RWGDuModonK36R1iqRzwFjjOnGqo07I34hlR2kI1sf9/TRoRXaKXPClD60U/aq39Q6atycPXu22wLjlPHq4+WJJ57oxnV1TImTlvOAXXHgxNz+5H2RF+CR/4lPfKLb/8EPfrD48z//89mr9dJhTl/rA/vtZF4NM1kO4AOHZ8g8SxaotZxgun2Ob4iag/CcHj0VloN1deYK1IN6CupX60UevYegehPIxznZxCE955cT11Z6t2WfnOVFbOu4zol6jiC9KuTzNFVPP0/ow9MQpI/rT/C6TaXq4HV11CYK2EFQLx1nX8hWnpa2IIDnIzg6J33Yev28TUVtZ7d3ze/1rm1DXOc4Ttq9amPiLTjn+dC1Ly2orm5bIJ+XRxDehqqPHyO4jVSfittHAT1q2bUMr59QH/GgMod0q+cIKg+qLl42tuuzrecjTW1Px9tWtlJayiBAtWNrXCDL6+eyCdJX9XaUt9pSecDHRqvv6BzB81VqGejjdZN+Cl4np7YPgby8m8W7Wn58LlUHAscq6FDr6mmpl9owrI8b+LM0fAghhA2CFbLlBXFr5ZQVDB77TZmya94QwsGRd7RCCGHD0CNBfzw59V0gHhuePHkyTlYIG0JWtEIIYQPhPSB/PwgyXYdw+IijFUIIIYSwJvLoMIQQQghhTcTRCiGEEEJYE3G0QgghhBDWRBytEEIIIYQ1EUcrhHDNwhcG+LmDL37xi6sj/fBffvxKNnlCCIcDfZVBv3DPFwzGvuqw38TRuoagg+lTPCFsKvTRKZ9z0QQ69CmZIXCYKIdvRH7ta19bHe2Hz/585jOfWfzO7/zOxk3Um8zcTw+FwwdjcOhzTQfJc8891323lTFLPxz6Zu1BsRGOVnUQfODSuDudaPeLPgdnkztnCGKvxhjjdi8dfX5wk4+K33fffasj8/jGN77RfVeu9W3NPviYOd/gO2zff5NTOubwKJ3Ctfqdu/2+bhyG69ReQZ/ZtLry3Va+icuXE9CtfsPyoMmKVghhT+An+fzDx3vBxYsXu4+2z4XVrL/6q7/qPsA7Fxwz7orHnJZNAqcU+4/9Gjwfo8ampOUTPfVj+CGMgUOzSatG6EO/x7miX7/wwgurM5tDHK19gjt97gRY/dLdJBMcd0KK19UAHW+dGwKPXvl8Eq13s34O3XTc71Zcv5qnD/LXsvxRDHXxc4I0xFWmHi9VHaY+1vE8U+/AhurbshHn2fd80lt4m9dzLtPPVfuRbgild9DJ692SR1vw6+NcfDnu6V1vgtuduOqsPLKDGKr3VF588cXFbbfd1u1L3vPPP9/Fh/j+97+/uOOOO666s5XOemeL+I033rhNb2AV7Xvf+94q1g86kZe6q67eZ0DHCW5f8HMa47XtJc/3gfSyq8bO2NjgAimbcHHikz582mcM2a3q1MLb3euLDM55n+eY17f28zmyZL+d9mmXVdtpN1TbSU9Qu3mYqu9UKM9lTMHz1DbhHHqB9Pc6Kr3i1ZZuZ4JoyVI5UG1FOuHH3b5Qyxvqu2tj6QEeOPoyu0AtfV28fhV+E6n6C/TW1/b1Jfca15fVqS9xvhwP5NV+PdcHsjydygCOuR05p7jkV2q5fekqyPV8fB1eZVF//1o8OitOevLJJkCZHietx/tAjrcJecbyDdW3r+467rK9bPRVmwPnvA94m5AWeZLJVrjMPlw2oJPyUI7nJ660bF0PqPYiL/IF+zWPyxyr91hbwGuvvXb5ve99b7f/9ttvd3lc5hCPPfbY5QceeGAVuwIyKPvcuXOdXOSTrsVUHUlD3dRWLTtxTJBecrERoeJ2dLwccB01dtR3p0AZrmsf6o+tcQGuF/Vxu3kdVZ7sobj6keqwE1lVR87P6dO1TnNplQfSU3qpjhwH8lR7iCF9p4JM2QyQ5/EW1ZYqVzoTl16qT417fq8jW7fTkCzwcjkuOQ7yVFbVnbp6ebKxzu8XO+9Ze0g1IIbQYMMwbqhNZKgDtDontAY26VXvitukj5Yeffm8fO3XdD6xCa9THzWNBlCrc7tdhtKJKf2hlcbr28dQffts1JJb29rxNuqzZUuPIZmCPC1bjulY7dXXDl5/3xd99QGv95S6wNNPP905SzhE2k7Fy6vgcKE/adhvMVXHVjmyzVg/pL3qeeBYbX+oNncd+9qsonQKY+mh1R+9rV0v3wev75R+Rn1acmFIFhBX+illgcqQbC9vDi19oNU/3J5epus4pu9OmdKvx9p7rN95WiC95NVznr8li3yyH2WO6Q5uo5a9qg77QR4dbhhaxvfldMJOWXaq1d725WBeGhQ8Qlh2xu4Y53xJlnc4lIewHASrM9Opj298OfzRRx9dHe3Hl37rR3b7qMvVN910U7cdW3rvq++QjSr8l5vj8s6fP786egVe4mxx9OjR1d4VkDn2XsSJEye25J89e3axnJS22d71mGL32m70pSmPmYSXV+s9hR/84AeL73znO4vf/u3f7uK8qO7QBrQz8v0Rwxjve9/7useKf/zHf9zt7zU+5ob6Ie+W8IhSNhK0M49M59ZrCrTpct7fCuhaHw21mDMPaIwQfJ6ZyhtvvLHamy9rrH/29ek543sudT5gbGseovxXXnml23/11Ve7reu42zEIc+dbqPPPbvF5lzlc+vhY6ePSpUvdVu9ekc/H1di1UmNOkNf72H4QR2sPYDCoMzhMRn0X0iHolAx4Br4mxJ2iCZGJg0Emech2mGh0nIGggbG8o9jKozDnv7jAB5kmdcla3q108T5wDrnoKP3yTmR1ZpjqlLz11lvdtk5claH69tmo4oOYQU0dJQvnx+mbNGt/Qma9aFfQj4mLtn7ppZe6F58d6aAw9tJord+c/jxW7ynQ7ufOnevaHIercurUqcWPf/zjTi8uUv7uxe2337746U9/uoptRxfRn/3sZ912r3EnZKwf4mxhH/qdO1Xk4zhbvXMy5aI0F9qorx87c+YBn7cUdspeyoKhPj11fM+lXtQZ2z4PMS/jIFAmZTu7GYMwd74VrevZXsF4lj4KY/OyYM4iPfMLdcM+Y9dKjTnBmKrO77o5dI4Wk9Fe3+XtFv4rqv73Dp2AiZHBu1Pkic+5u2LQanAyQbsOPlHz2yMt3PtnhYR67WTCYdIQTzzxxLbVFR9UzzzzzGqvH3cwnn322dVeP1wAmJD8pUj+ZZ+LxRBT61vvkMBXBWgDd3I0qJHrKzusZuAsCGTQh6SH96cqs4+HH364k0k57iDS9m4Ph4nbnQPah/byOtW+NIW+ek8BJ+iXv/xl95tWv/d7v9cd4yV4dNIPijJhsiKFvuiml+bhAx/4wLY6CWz68ssvL77yla904wrdWnbhQoOzNoW+MTenH/atIHjfZ1/jlvIodw7kcXnA+Bu7yM2ZB6gbddwLdiNrN33axzf9BSfIx+IcGLPeP9hiS2zKPjrKQSBIlyn6ch308314+06Zb3U9k86U0RpLO0Hz026pfbbvWkkf8nmT89Sl7yZhbSwb98BZdqhtz7FRa+mhdvs8S112rm4fSEvYNNATvRVcZ6B+rjf1q+YnD3KA9JJFPs7JJn2Qruqx7FSrs1fO6/iyA3Zb8LII0gGqPILLbIGuVabjx9FDdkEux6p85Hn6atsWkuX5ptBX3z4bqR39vJel836OtMLbRHaAqoe3yRCqd6u+Lo/gesjGroPbvdqcY7U/kqbaRcHrzdbLacH7WXfccccqduXldl5gb9mBtLzg7ug9LO9L6Ce7cJ44ofY34PjYeAPq4W1fy1R7KHi7UIafg5re7VTPYQud1zkvuwXlu4xWP2lR+yNBZbHvtkInTycdkUGdRUtn2VNMlQXEvX/IvsoDbnPPX9tQctSPx/pCyz6ybZXtsuo5gtujT18g7rbqw2Wjk9ujD+8nlEEe2URxaLUhenk7eHqofVD1askirezYygNuQ8rh3FB/PAhu4M+y8BD2DO6auRPd97uGA4A7XZaudzOMuFNlBYXHSGEeuoNt9TX9hMOUX4V3kMnK6ZTf42FV4dixY3v++2Hh2qc1dzAX8Nh/rO/txbwT9o+8oxXCAXPXXXd17yOFebiTxWPF+njn85//fGfX+jhhCB5Z8mgjTm9YN7yfeeut29+7q+9v9cGN7JmJ76uGgyeOVggHCCsi3Jnu9DMz1yu8P8J7gATeofmDP/iD1Zl34P0tVgZ4GXnqR6W/9a1vdS/YT7nYhbAbuEHw/zol0K+nOPn06+vhicG1Qh4dhhBCCCGsiaxohRBCCCGsiThaIYQQQghrIo5WCCGEEMKaiKMVQgghhLAm4miFEEIIIayJOFobAp8V4cfqpv4bun+KZAr82/puPiURQgghhPnE0doj/HdQ5oLDxO8p8W24Kb9ize+nfOYzn+m+AzelPOR/4Qtf6L771PetOuTgvN14441dPT784Q/HKQshhBB2SRytPeLcuXPdr/zu5IcOv/GNbyw+8YlPzPoBug9+8IPdrwNP+ajo2bNnux/BG/ohPOQg83/+538Wb7/9dvdjj1M+YhxCCCGEfvKDpXsEj+b4fMfcT3ew2vSbv/mb3RfFd+Kk8V1BvsvWt1K1U3iE+R//8R+TvvcWQgghhDZZ0dojfvCDH3QrQsC7Vjx+m/K+1fe///3FHXfccZWThdPGYzwe4SmOU1VXsPiEw/e+971V7Gp4n4t86MPjSeQMwSNEnEYcv3zvLYQQQtgdcbT2CD5e+7u/+7vdPh8Gfeyxxya9b/Wf//mfV31YFL773e8u/uu//mvxk5/8pHtXSt9gq87P0aNHu5WnFuTjW3CseLFw+Z73vGfx6KOPrs5eDQ4iuvA+F07eu9/97tWZEEIIIeyEOFp7gFaJjhw50q043XvvvZOcLMBJ+tCHPrSKvQP5eU+K1S7eleLld+JzeO655xYPPPDA1mPFP/uzP1ucP3++22/xyCOPdA7Zv/7rv3aO46c+9anVmRBCCCHshDhae8CPfvSjxS9/+cvuXSse1dWX2uvjuznwaBBnS48l58BjwO985ztduYR77rmnOz7234Q4ZrxoP+SUhRBCCGGcOFp7AO9n8aiQ95pwuKojc+rUqe6xH+dfffXVbedvv/32xU9/+tNVbDuslLHixePDncCjQn7SgVUqD1NenH/Xu9612gshhBDCTomjtQfwmO0jH/lI90I7q0+8nM4q1vPPP9+df/3117vHfpznHajbbrutOw4f+MAHOgeswmrUX//1X3fvZHEe56z1cj3vg+GstfijP/qjTg85dsjs+wkJVrxIC6T78pe/vDh+/HgXJz/nqwMZQgghhBEuh13x2muv8fMYl99+++0ufu7cuS7+2GOPdXHn6aef7s475CP90plaHbl8+eTJk5eXDtuWzAceeODye9/73qvywtJxu3zhwoVV7GpOnz7d5aWMpePU6dvizJkzXZmkIz06qHzkc3yonBBCCCFcTX5Ha5/QalFrRUkrVVNfoBfI5D8K81tXIYQQwmYSR2sfcCeLx4m8/+TvSekTPH/xF38x+dfheX/r4x//ePfYcic/dBpCCCGE9RNHa83wvlP9nawLFy5c9UI6ztY//dM/LX71q1+NrmzhuL388suLr371q7N/8iGEEEII+0ccrRBCCCGENZH/OgwhhBBCWBNxtEIIIYQQ1kQcrRBCCCGENRFHK4QQQghhTcTRCiGEEEJYE3G0QgghhBDWRBytEEIIIYQ1EUcrhBBCCGFNxNEKIYQQQlgTcbRCCCGEENZEHK0QQgghhDURRyuEEEIIYU3E0QohhBBCWBMb52h9+9vfXtxwww2LX/ziF6sjIYQQQgiHkxsuL1ntHzhf//rXF88888zi9ddfXx0JIYQQQji8bJSjFa49Pve5zy2eeuqpbv/ixYuLW265pdsPIYQQrgc24tHhRz/60e5xoQdWtzYNnAZCmAaPf3GycLDkz9O2PB4OIYQQrgc25h2t06dPdxdjhUceeWR1JhxW3nrrrW6rVSy2tO0nP/nJLh5CCCFc6xy6/zpkpUurXu9///tXRxeLH/7wh1vHCb7yRB7iWjljRYVAnOMc0wqaXsZXQC6QltUZAsddvqd3nVpMTctqkKeVvsB+zUuc40L1UujD7enpqj1ddsVXJF3He+65p9v34+zLpuhI+eiu4/UYAVu4nq5Ly0599NmEcinPy2DfZUt/kG1UjxBCCKGXyxvA8ePHL58+fXoV6+fMmTM8f1rFLnd5OHbhwoXuOFtBXDLZEietkCwvV3IuXry4LS5OnjzZBafKoC6EFnPS3nrrrVtp0Ye8SovunHeIq37k8/PEW+VIruordFz27EsH2MPtSjrFq/3A5ZLX41CPKS67q92E79dzzpBNpKfKULyWU+vleocQQggt2lelfYYLni5sCi1IJ+fD4QKpi6TwC2nL0eCiWZ2Vlhx3YOr5lgxdhCtz0raOex1aslxP34cxh8rTQstefbaveLpWPYjLQWnZe4qNW3WBvnrCkE3G9AR0mlL/EEIIwdnYd7T6uPnmm1d72zl69Ohq7wqk28nPROjRoMLyQrw606Y+wrvpppu6LY+dKnPSLh2D1d7OePDBB7fq0CeLd6aon9LyuEycP39+Kz+BeB9T0+0lev+LR4Uqe8xmU2wyxKVLl1Z7IYQQwjQO3Ttab7zxxmpvO/UiSLrq2Ezh5MmT2xw+wtDL29WZqy+AO3PSDjl4R44cWe31c+bMmavq0SpHL6hT3qOPPrr13tHx48evyt/6BwVs7E4y+fYLdMV5QnfKHnOKp9okhBBC2CsOlaN17NixzhkQrMCwonHixIluJcpfTibdQw89tIpNQ3JaK0zAqpmfwwHj4u4rQY8//njnrFXmpL377ru7rdJSL683K2HIUn1J507Gww8/vDh16tQqNg13OO6///5uZcrtOYRWGbHNfq1oOdL97Nmz3bbFTmzSB3ZhVWyqfUIIIVy/bIyjhSOhxzoE/68+wYoKjonSvPTSS50Dg2PCagX/5aZzxOf+jIDk8FhJcghyruSAcExOEA6O685F/8knn+zOVeakvXDhwlZaHEZWjQT5iKu+rOb5ShJ2uu+++7bKIbRW9/yxGwGZ2AD5lO/2JLQci2effXbrkRx2azmO6wJdvT8MPdqbapMQQghhL8kvwx8ScOxwLF944YXVkRBCCCFsOofuHa0QQgghhMNCHK0QQgghhDWRR4chhBBCCGsiK1ohhBBCCGsijlYIIYQQwpqIoxVCCCGEsCbiaIUQQgghrIk4WiGEEEIIayKOVgghhBDCmoijFUIIIYSwJuJohRBCCCGsiThaIYQQQghrIo5WCCGEEMKaiKMVQgghhLAm4miFEEIIIayJOFohhBBCCGsijlYIIYQQwpqIoxVCCCGEsCbiaIUQQgghrIk4WiGEEEIIayKOVgghhBDCmoijFUIIIYSwFhaL/w/K43byHcTALAAAAABJRU5ErkJggg==)
I, II e III
II e III apenas
I apenas
I e II apenas
I e III apenas
Seja T : IR2
IR 2, um operador linear dado por T (x, y) = ( 2y, x) .
O polinômio minimal é dado por:
p(x)= ( - x +
) . ( - x +
)= p (λ)
p(x)= (x -
) . (x -
)= p (λ)
p(x)= (x -
) . (x +
)= p (λ)
p(x)= (x2 -
) . (x2 -
)= p (λ)
p(x)= (x2 +
) . (x2 +
)= p (λ)
Dada a matriz A de uma transformação linear T: V
V .Determinando o autovetor de A =
, ao utilizar o autovalor λ 1= 12, teremos como solução:
V = (5/2, 1)
V = ( - 5/2, 1)
V = (- 5/2, - 1)
V = (2/5, 1)
V = (3/2, - 1)
![](data:image/png;base64,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)
6 e -3
2 e -9
-2 e -9
-2 e 9
2 e 9
Seja o operador linear de IR2 definido por T(x, y) = (3x - 2y, 5x + 6y), indique a alternativa que determina as coordenadas do vetor
IR2 tal que T (
) = (- 12, 8).
X= - 2 e y = - 3.
X= -3 e y = 2.
X= 3 e y = - 2.
X= 3 e y = 4.
X= - 2 e y = 3.
Existem funções que relacionam espaços vetoriais ou subespaços vetoriais a outros espaços vetoriais chamadas de transformações lineares.
Assim sabendo que T: IR2
IR3 associa vetores pertencentes a IR2 do tipo (x, y) = (- 4, 1), definido pela imagem f(x, y) = (2x, x - y, - y); tem-se uma transformação linear, se:
f(- 4, 1) = (- 8, 5, - 1)
f(- 4, 1) = (8, 5, - 1)
f(- 4, 1) = (8, 5, 1)
f(- 4, 1) = (- 8, - 5, 1)
f(- 4, 1) = (- 8, - 5, - 1)
Verifique se o vetor dado v = (1, 2, 4) pertence ou não ao subespaço vetorial com v1= (1, 2, 1) ; v2 = (1, 0, 2) e v3 = (1, 1, 0) assinale a alternativa que indica os valores reais de a, b e c; na combinação linear definida por
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAALIAAAAfCAYAAABDCJdYAAAIbUlEQVR4Ae1aDVATZxrmlE2CtJZOx9oDz461RVraA+/ggmeqTS8WxBOKnnPSnjJn1bH+pAM9xp+j8isFD1FHOLTltAVPrace0TPF8FM6vePqoPEsRqjIgRkQyUTTATRoknluNjGEJbv5gSSwc8vMzn7fm919n+f5nv3+Fj9wf5wCLFXAdFsJRfW36NADfizlwMHmFKAowBmZIgdXYasCLhk5KysLkZGR2L9/P27cuMEqrmzGziQ0mzl5C7tLRh4YGEB5eTni4uKQk5ODtrY2Jo0nXJzN2JnEZDMnb2G3M/Ly5csRGhpqd8yZMwfh4eEICwtDcnIyk8bjGmczdibh2MzJl9jtjHz9+nU0NTXZHQUFBRCJRFi/fj0uXLjApPu4xtmMnUk4NnPyJXY7I9MJWlFRgYSEBGRkZKChoQHk8MCWv/HGbmg+jqw/HkB9r8ljko03p7EQ8RZ2l4xMvlm1tbXmuXF/f/9YePj83vHGrj/1Dp7mRSFXZfQY9/HmNBYi3sJuZ2SNRoO8vDysWbPGfCgUCgruy5cvo6ioiBKbKBWfYTd2Q6lQQHnbeS87ViP7hpMR3UoFFMrbcM7I9db2DXYLHjsjZ2ZmmufB5O6EUCjE3r17KchPnz4NiURCiXm6YtK1ou7oAeRnZiAjazc+OfsdtC50aD7Dfr8CiQIB3q6875T6kJGv3sK/ju5BduYulJ5WotcFPuTDfcPpPioSBRC8XQnnjB5TNvTiStUhFGbvRHbREdS22Y/UvsFuwWNn5OjoaFRWVprnwWSvPDojG9GSLwTh5wc/uoNgHmr1V4oRG8zDlJAILJBIsCAiGAGTCMx85xi6nHQXnsHu1JuAu0b2D8FPI55D0Iy5+GVMGKbxCUx7Yxf+bd/2dsl9w8k9I5u6ZZD+LAiTA2dg7kIxYl4Mgn/gy1h7qovSo/sGuwMjk/NhnU6HlStXUoz84MED836yKz1yn0qBE8eO4RjdcUIBVZ9dmwGmXhxJDAIRJkW9zvp7H75OfQWEIBYH7zh2MimcJ7BbM1POA+1orJZDLpdDLtuG+TweRNtllrq8Go3t9Atgc488aTKCl5ai2XyJCXdqUjF3SgCEu5opKegq3uM0gPbG6sf4Zdg2nweeaDtkJD+5HNWN7aBn1Id/vDcDxLPxKLn2uP823MShX08DMVuKhkEbC+9ht+Wwlux65NzcXJATctKAq1evRlVVlfVakPPjzZs3m3cvhoIeLRhwr+MqLn1/1/ZmG/vQWvQmBMR87L7peDz2JnZjSz6EBMMI40cgKlcFOnRmIxOvYkeTwaaUqRflSwJBzCuwxRhKXuNkbEG+kKAfMf38QETlgnZ9Ovgl1oUQmLWpHsM8C0P3NVxqvUOZmngNO41Wdka2XkNuZufn55t7ZjI2ODiIkpISiMViFz5Tm6D5qgQ7tm7FVrpjh4PtKH0Haku34b1lb2H+3FAETyVAEAR+RMxDQRudVayIbeexYbc9h1Iy6qHTakAuYDTqMsTzBVhyUG2pa7TQ6emxmY0sWIojlBFoEHUbnwcxaxMlhaOK5zkZoddpH+NXoyyeD8GSg1CT/DQaaHV62hcT9z7FYj4PC4o7bJ2NI+AAPI/dPqFDIx8+fHjoDpJcdnY2YmNj0dPTMxSnLxjR8WkyIsPDzV8DyS+ClCPytzjUTtPwxk58tiwYxJOhiFu3A4VlR3Hun624VZ4AgZtGHj12GyNDx7e4qKbB6e4cmb8If6ZMi/SQpTwL4tXttmROSqQZXOdkgq6lHmdOytDYQT9BoKZzY448cBRJgQSi86gjkOleG5TfdUI3bOCx5nAPO2D64Xs0/P0Maq7dpX+ZrA8ednbZyCqVCmlpaZBKpcNu92zR1FMKCZ9A5M4rsOkxiMY/vAx/Igb5rTSmooEwUrjRYDd0nYP05zPxuzN6+wzuGnnydKz8QmvrwfprsHE2gedSbNM2+yTUiDucfqiW4nXJFhSX5iE5RoKPr9jUpD7VWnPDyEYV8n7Bx9TYMtwaWrIY0b5nIQICY3GQZkvSHezor8eHCxdBWrwf6YtF2Hh+aLFkBUt7dmjksrIyPHr0yHwUFhYiMTERnZ2dtA/ySHDgHNaE+GNq1GZUfnMVzU01qNiZgBefIOBPROAjpw1iQUEKNybsurPYumIT0ldEI4XOyO7uI0/iIWCmGOmH5ag7V470X4WAFyRCwX8euiyb65yMaDtZiM+vkuY1ov1PYsQe6La9RLQZ3dlHNqH7eDJmEM9A+H4pZPV1qCp5H8JneHhh7Vloh8xtS+Q6dnK934gT51thgAm95YlYkN08rFOzPXNkidHIKSkpSE1NhVKpNP/fxYYNG7Bly5aR93u4bkJPTQ6SIn6MJwgCgqdnQbjiI5ysK8AbAU9h6V/uuJTPM9gf4uL21+mN7BIKy0WWOfJi5Bz5AOLZQeDzn8LzMatQ/I3GibmoSUbDyaRR4ANRPPa1uDaSUTM6qg3g2l9TEf9aMJ7kEZgy/RXEpR3HdYZNaLexm7SQpYvwwk8k2KdyNppYcDIaWa1WIykpaWhVu2rVKnR1dTliN2F+8wx2zxjZU6K4y+nhf/+GTeI38eF5z36tGw0fd7FbchhxW7YWUcs+A2V5wQCA0cgM1/8fhSeWkd0R3nDzc7wrSkLxRdfml+4829vXGpT7sC63wbyNZ1LvwyLxbjjZdTVD4ozM2DJsNXIfTr07HUEvzTNvlYrFb0H6xfj3yowyj/xBfwl7EkWI//06/Ea8CGlne1yagnFGHikkV58ACgzirloNrZ5m5ciAjjMygzBcmF0KcEZmV3txaBkU4IzMIAwXZpcCnJHZ1V4cWgYFOCMzCMOF2aUAZ2R2tReHlkEBzsgMwnBhdinAGZld7cWhZVDgf9o2bGMMGoe2AAAAAElFTkSuQmCC)
Com os valores reais em a = 2; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 2; b = 2 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = 1 e c = 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 1; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = -1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Os vetores v1, v2 e v3 são LI porque o determinante é igual a -2, ou seja, diferente de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual a -1, ou seja, diferente de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual a 1, ou seja, diferente de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual de zero
Os vetores v1, v2 e v3 são LI porque o determinante é igual a 2, ou seja, diferente de zero
![](data:image/png;base64,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)
I, II e III
II e III apenas
I apenas
I e II apenas
I e III apenas
Seja T : IR2
IR 2, um operador linear dado por T (x, y) = ( 2y, x) .
O polinômio minimal é dado por:
p(x)= ( - x +
) . ( - x +
)= p (λ)
p(x)= (x -
) . (x -
)= p (λ)
p(x)= (x -
) . (x +
)= p (λ)
p(x)= (x2 -
) . (x2 -
)= p (λ)
p(x)= (x2 +
) . (x2 +
)= p (λ)
Dada a matriz A de uma transformação linear T: V
V .Determinando o autovetor de A =
, ao utilizar o autovalor λ 1= 12, teremos como solução:
V = (5/2, 1)
V = ( - 5/2, 1)
V = (- 5/2, - 1)
V = (2/5, 1)
V = (3/2, - 1)
![](data:image/png;base64,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)
6 e -3
2 e -9
-2 e -9
-2 e 9
2 e 9
Seja o operador linear de IR2 definido por T(x, y) = (3x - 2y, 5x + 6y), indique a alternativa que determina as coordenadas do vetor
IR2 tal que T (
) = (- 12, 8).
X= - 2 e y = - 3.
X= -3 e y = 2.
X= 3 e y = - 2.
X= 3 e y = 4.
X= - 2 e y = 3.
Existem funções que relacionam espaços vetoriais ou subespaços vetoriais a outros espaços vetoriais chamadas de transformações lineares.
Assim sabendo que T: IR2
IR3 associa vetores pertencentes a IR2 do tipo (x, y) = (- 4, 1), definido pela imagem f(x, y) = (2x, x - y, - y); tem-se uma transformação linear, se:
f(- 4, 1) = (- 8, 5, - 1)
f(- 4, 1) = (8, 5, - 1)
f(- 4, 1) = (8, 5, 1)
f(- 4, 1) = (- 8, - 5, 1)
f(- 4, 1) = (- 8, - 5, - 1)
Verifique se o vetor dado v = (1, 2, 4) pertence ou não ao subespaço vetorial com v1= (1, 2, 1) ; v2 = (1, 0, 2) e v3 = (1, 1, 0) assinale a alternativa que indica os valores reais de a, b e c; na combinação linear definida por
![](data:image/png;base64,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)
Com os valores reais em a = 2; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 2; b = 2 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = 1 e c = 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 1; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = -1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
I, II e III
II e III apenas
I apenas
I e II apenas
I e III apenas
Seja T : IR2
IR 2, um operador linear dado por T (x, y) = ( 2y, x) .
O polinômio minimal é dado por:
p(x)= ( - x +
) . ( - x +
)= p (λ)
p(x)= (x -
) . (x -
)= p (λ)
p(x)= (x -
) . (x +
)= p (λ)
p(x)= (x2 -
) . (x2 -
)= p (λ)
p(x)= (x2 +
) . (x2 +
)= p (λ)
Dada a matriz A de uma transformação linear T: V
V .Determinando o autovetor de A =
, ao utilizar o autovalor λ 1= 12, teremos como solução:
V = (5/2, 1)
V = ( - 5/2, 1)
V = (- 5/2, - 1)
V = (2/5, 1)
V = (3/2, - 1)
![](data:image/png;base64,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)
6 e -3
2 e -9
-2 e -9
-2 e 9
2 e 9
Seja o operador linear de IR2 definido por T(x, y) = (3x - 2y, 5x + 6y), indique a alternativa que determina as coordenadas do vetor
IR2 tal que T (
) = (- 12, 8).
X= - 2 e y = - 3.
X= -3 e y = 2.
X= 3 e y = - 2.
X= 3 e y = 4.
X= - 2 e y = 3.
Existem funções que relacionam espaços vetoriais ou subespaços vetoriais a outros espaços vetoriais chamadas de transformações lineares.
Assim sabendo que T: IR2
IR3 associa vetores pertencentes a IR2 do tipo (x, y) = (- 4, 1), definido pela imagem f(x, y) = (2x, x - y, - y); tem-se uma transformação linear, se:
f(- 4, 1) = (- 8, 5, - 1)
f(- 4, 1) = (8, 5, - 1)
f(- 4, 1) = (8, 5, 1)
f(- 4, 1) = (- 8, - 5, 1)
f(- 4, 1) = (- 8, - 5, - 1)
Verifique se o vetor dado v = (1, 2, 4) pertence ou não ao subespaço vetorial com v1= (1, 2, 1) ; v2 = (1, 0, 2) e v3 = (1, 1, 0) assinale a alternativa que indica os valores reais de a, b e c; na combinação linear definida por
![](data:image/png;base64,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)
Com os valores reais em a = 2; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 2; b = 2 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = 1 e c = 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 1; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = -1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
p(x)= ( - x +) . ( - x +
)= p (λ)
p(x)= (x - ) . (x -
)= p (λ)
p(x)= (x - ) . (x +
)= p (λ)
p(x)= (x2 -) . (x2 -
)= p (λ)
p(x)= (x2 +) . (x2 +
)= p (λ)
Dada a matriz A de uma transformação linear T: V
V .Determinando o autovetor de A =
, ao utilizar o autovalor λ 1= 12, teremos como solução:
V = (5/2, 1)
V = ( - 5/2, 1)
V = (- 5/2, - 1)
V = (2/5, 1)
V = (3/2, - 1)
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlcAAABBCAYAAAD8K4ErAAAciElEQVR4Ae2di5HcNrNGlYJjcArOwSE4BqfgDJyBMlAEisAJOAFn4Bz019lb5+pTGyDBGc7OYxtVNEignx8ajR7OWPvpW7dGoBFoBBqBRqARaAQagdMQ+HSapBbUCDQCd0Pg33///fbTTz/dTX8rbgQagUagEfiOQBdX37HouxdH4K+//vr2+++/f/v555+/ffr06a0Y4ZnC5Jnar7/++mY/PuTFeLdGoBFoBBqB+yPQxdX916AteAcEKKwoRLKY+vz589vYsxUl2PtsNr/DEreKRqARaAQeBoEurh5mKdqQVQTybQ33f/755y6rxVV9S+VbrF0BD0SwUlyBScXpgVxoUxqBRqAReGkEurh66eV9TecsGs7w7pdffnm63yqtFFeJzZl4pdy+bwQagUagERgj0MXVGJcefWAEzioWfJvF14PP1Lq4eqbValsbgUbgIyLQxdVHXPUn9/na4uqff/55+yqRrwSfrbBi6SiusJ0eLPi/BPO3ZHV5r8WryuvnRqARaAQagW0EurjaxqdnHxCBa4qF/C0SXwk+Y3GFD1lM7f0w/xq8HnD526RGoBFoBB4egS6uHn6J2sCKwBnFAm+vKFCQRf/eLYs8/dnq9+zTl7///vs/pMr9z0QPNAKNQCPQCNwEgacorjgc+AqkWyMAAmcWC7y9Qh7F1jM3izV+R1bbWXj5GzV0dWsEHgGBVzsb/Kr/EbBtG65D4HBxZRI3YdPzmw+C4suXL9dZM+F+tQ00cfOUYTfn6JC9RgFr65qP3o5cI/sor3Yc5RvRG89n4zXSdcuxLT/OwusVi6tb7ZdbrnXL/o7Aq50NxuN3D1/3Tl/Pzr2+xb/3X6y4uLjCARI612+//fZWYBHovAk4+/B9tQ20ul0uCb4//vjjrdA9ew1YYw9pdNyzaccRG4hTfgRem37Vf/+q0j3K88wPE8roDdwleI38vbS4ko94frR2q/3yaH6eZQ/xRzzRP0LDlkeMq0uxMedfyn8vPu0+Uijdau9RVJnzvn79ei9Ivl1cXFUQOZzceBxiZx5Wr7aBVlf7koBdlX2EjrVkDShEWNtRkXJE3rW0bpwjcoxNihBj0x+C37tYvLUfl+A1sski6ejBKt8rHYIjfD7CmPvoaAzcCptXOxvM+bfC61ZytbvWBbfSN5NLMUVMkNPpyff3aqcVVzrg5jvzwHq1DSRWe/2jBKxfCdIbtGe/GdvDIueJB64jzX9+wd9Ywc/9rb7KPmLbEVr8YA2O+HEJXiObLJKOHqzydXE1QvW5xszvR2PgVl4S268UV+b8W+F1K7nafe/iKt/gkyPv+dXgsRPq27f/fzs1A9G3HOkUbzzyMGBDMFYPaHjZtLwZgYbezZwbaFXeLJCobpGXrw/VNePJcQ8LbOPth/bioz4xp3x0OY6cFf0GqwejPbrFhPvUj2z5tFe+UT9bQ3ntwRtfWB/8QNaZxbN6Vnt9WaX/6HSX4EURR6IyholtC2viLxvPub+JQWOLXv3ZQ2NzPziPLOLapgz0cK8u7t0LyMA+7SVmiVcuEy7yuWfMVvdLyuPevU2Pjtqg0R7kp++VdvSMD9iq77M8lLaoZ+WDgdiKC7wjHRUHbRWPxFpb7aGhgWu1E9/MfcxjB/pHjXHmacjRJvWAc10D5qDLtoJp+lVzKLKw2byHDmwjvjJ2pNE+aIiv0VfzaZ/3rJ+xg9/w+izNKg7S1x4ssA856Sd6XBfmjA+wdBxZK/FT10k8Mma4T/3Ilk+b5Rv18K80/DAe0IesGjMrcs6gOb24wihBc5FwmEBlEblMgoxnIBpY8EtHwAKQgCF/Vd4MIGSjCzu0SZsZ22sGLHZgG7K03Q2mzdpPb1vRz8aTl02nneDFvXrVg36afqQueenxD56KvfS1J5lAjw027Ep/HJ/14oWcvWsmI8eVkWN9P0fgKF7EmLFt7NE7RhzZLAzc38SXccu6IwtebGDcWLQwMAESv865l9Rj/DhOT5wzDg2ytY1x9WMTtMwxLk3G8mi/VHnqZTzz1Z7vYjTrwUBd5iJ1pY3ih634C62+zGQ7Dj0ylc+zPjNmc8xne/EFay7ptIV5xskT2u4cdmMnl2eBvsCTjXmw0CZkKAcdXK5rHpbwQGdbxVS/0mbuadiGzegTN9daXdirb9Iwhz3I3mvwQJt7QlsYtyGTSwxmOEhfe3xBHrbSo0M96HY90CG+9Db0Qa+PPOsnY7RHOauIC3w0t7BXecbHe7Tvq7ioHXAxuG6OZBf8LRqBQB7NTeFzykMfMrdalbdFO5sz6Gbzjhuw0Js0mDNQGc8kLB5Jq6zsq375Ko6uARs+9SBLnpSb99qYCSrn673rkvQeiHv+KAsbsXnlkmerJx64uq0hcBQvY6TGnXHvHjU2TGZa4xsK96x8Pku3lfwyjuVnf1Sbci/kWwUPCnxxXLs4aGyphzHlMZ57y5g/6rt6aq8t2Kl90niQu+dYP3zPBg++XdpmuabKEw9x91kcpHccnLKRI9J+n6vtFhtbOQWfkZVxlM9HMNXeUQ5lTcCnros8YOG9a6TPzFUMnLM37tMP52o8Op79CIecz3vsEf/E1j2Onxnn6k/alOf9LH6ME+nEaYSzuqStvTZWjCudz9LnuqE397u079EfPqEEq4KYxgpa0pCAcZ45q2gWHXk0eRIYZeYGcmxPnnSznoBCN3oNFPRw7TUDVtulNwHXzTXCbEW/mCSO6BrJ0wZ5fM7ew9BPHDk3ux8Fp8nhiJyZ/EvGV9fpEtmvyHMUL+jZE7XVuCfW2MuM18s9hQz5oM9mHBOXld9E6Tg21f2GLGVAl424haceEnV/1OeZPOQgTx/oV3xPm/KeA2Pmk7rAgAYdB34egilr7/5IrqmyKh4+02fL9c5x7l0L7ceXeuDxXGOONSXHgDU84JBrgOx8PoKpftS44RmZ6OU+L/M7vPJXHKrvo2d5R0VDjUf4sWEPh5EeefGn2qkvr3RWEUPEWjbPvBHWSXeL+/1Komg1MFjwWTNASBIUS248nGeOpGHydNEJgLq5lJ8baFWevKNewNWJTdjhBh7x5Bi+jwJ2Nl4xW9UvjhXrKi9tkyfHuGctRgms0uUzWOPn7AKvezTtuYfuZ9R5BC9jeFQ4O5d7VtmzHrzkIzazGaszXsbhlV+9KWO2F2bj6lRGfZ7xQY89+rBls3PqGPXqmSX91MVaKJM9xwFCDhl9EK26juaayq+drAHNZ/ps2DfL3/Iow4Md22gWRT4z5vkgDvisHNcAOue5d34FU2m1CX6a4+I96qGhUCSfOo9NrBN5dq8Zc6P1c04ZqzhIX/vZ3pmN67+4HI0f+bSjynOcvvrq3CVnlTHketQeHN+7nV5ceSATeDTBxbkMprq4gDHbnMyxEEfkvREP/oMN2MZVA2G22FVMtd352bgYMH9Ev/ZUO1Oeuu3l8ZkenWCLz356zPnZvRuLtUNnXn4aXUkm4lIDfvQ8syXH5cuxvp8jcAQv12q1uJrt2bRGme5h54zVzAvOZS8/8VfbbC/MxtWpnPo844MeHPWB+xXf1VN79awUAvCCAWuiverfwu6SXDOzE/007abPpj055r08ysAu6H3LQH4hN+mL6w2+NV/B5xogP5/Vs4KptNpUbZ3JkI4eeykU8cMP5tiTRWLSe+8a6q/j9M5xfwSHlJH3yqjrNRtPXLBv9azU7hmedbz6qs3ovOSsIobAHvvrpTx1vFd/enHl4tDTBL0GUl1cAACcSoeM3ECr8mYAqnd0eCh7xuu4MvRxb1xM4JN3Rb8FDDzZUl6Ocz/ywcBbSRgpT/055j1FFesy8kMaexIkNq9c8mz16OXqtobAEbzYf9CzH2szdllH2taeTV5jhdjMRuygay8uq96UMdsLs/G6P+rzjA+d2KoPq76nrXmPz8gb7R/xYt+OGmvk3qy5IenFbaSj+l2flVPx8K0T49m28NDWLJTMSSNf1TmKi1wD9OfzEUzVUfHbkpH+ju7F2xgZ0TC2Ffe5Dtq4gsNMlzbV9ZqNq5N5aVbixzWGJ1vKy3Hu01fnjIuRz9KMeorAGe7G7FGZIz1Hxg6fUFtgOcdGs0gSrAq64/DQDLi6kMoUOPn25M1AYIPnhpSOTc4CMbfXDDptl342rg/MH9GffOqgn40zVwPWwKq4przRvYcsm2bW+LR2j68GWaOVdZrZ/dHGj+LlQZnJKA904944ZE/WRpxnzGGDe1ha90vmC+fQp1zp1CsNvTZAk202XvdHfZ7xITt9kE4bU3f1Pee8xz/fCnCfzYMK/PELHCuN+qvfKedIrlFnyuPeWHCcHhzQn0176rjFE3Ky4RtylA+dzTfmVZbjGUe5JquYokd79UvdKSNtcp58Cg/rXnmhSXvkqb2+40euK+N5BunvCg5Vh8+z9ZqNJy5H4if51E0/G2eu7r1LzyrxhH/U9CP3KnuKtcqxEe81Y/uVRJEuWBjFPRcb06Bgs+CMzUVkHoeg50CWnmcaPI4xD/A8O+aGWpWn/lHvotKjH18AWl0jnhzTBm13bjYOHfKZp63qd3OBh3Yio8pTf8rm3qBCNzz1ynVKGdyrexaw0IjbKAlVeWc+4w9XtzUEKl7GTyb2lGSygo/9bLy6P+C3eTgSo+5vD2r4bMwjz7xBTzPJITvn1AXNbF8xpy/urTehG+P6Il19nsmDHvvTp1Xf1VV79xi+ip0yxUffKz6uzWwN1aV/9PiGXHgTX2hzzaHVDunE15yizdhNjsAOeVIXdFyjHGFM0GdDlnqNCWQ6xr2trskKpvBurXNiQSxDi5/aCxbY4Bowz+U8Nuw1scInZMmrj/AfwWGmz/jBvmyzcejwy/XWT3rmZvEj7vghHTKqvLRB2YwZV+iGp15bZ5U2jWJMfdgFtjbx1k/Hz+wPn1CCBQheBsgsqAhWnRF8Axh5NsAxMSMb0ASdhbCtypO+9gStC4IedAJyLnblyWdo4UvbmZ+Ni5kLeUQ/vG44sAOjKi9tSx+0x3WqvfYkv/euw1ZQu6FIPO/Z9ONWOsGF+AB3+ns34oW1zz1wxKaKl/GzlYzYY3kAwDPas9jB4SoturAV3DK+0GVsQpMxg1zjjTlw59l8YhxjQ236krqgmY1rg3Lq84wPemyra7Diu7pGPXan7+bHpAWHpDEuiYu9diTX4Av6XUP0jvDI/IstrhO6oFcGcrB7FmfKRm9tGS/6OzoLRmuygqm6a9xoB/rNAehQD7biJ7ZkwcU8ewBsVprrgm/KxpYaj6s4zHQiE/n4m202XnHRTmRwsZ4jO5ENr/4QA9he5aUN6av2qKf2zM8aOtG31VhLZLI+xhHrdct2uLi6pTEtuxFYQcCNt0J7lIaEyWblwNgqLI/KvYYem/C5HuyrMiteJsFV/qZrBBqBRuBVECC3kxP9UHArv7q4uhWyLfdmCNRi4SxFfLrhExCf1h6l8YkNmyisziqu+MRGgdWtEWgEGoGPhoBvJG/tdxdXt0a45Z+OwC2KKz/NbL1+Pt2RHYEUeRRW2HRWcYUskku3RqARaAQ+IgJ8M/EeHy67uPqI0fXkPt+iuPLt0CNBw9eBFkJnFVeP5F/b0gg0Ao3AqyLQxdWrruwL+3V2ccUPL5HJpxkuCi2e6W/9vfxsmXjDxCcsv6Lkq7yzvhac6ezxRqARaAQagXMQ6OLqHBxbyjsicHZxRUFlMeX/7UNR4/+hdY+vCimmsrDDvi6u3jHIWlUj0Ag0Alcg0MXVFeA1630QmBVXFknO7/VaL18tovxfdimy3rNhTy2kLK4o+o7+L8Ti8J4+tK5GoBFoBD4yAl1cfeTVf1Lfzy4WZsUV8FjUvCdU+rfVH7FHOUd4mrYRaAQagUbgcgS6uLocu+a8EwJnFwuPVlyNYMXn+jZrRDcaOxuvkY4eawQagUagEfiOQBdX37HouydBwGLBnuLomuYP2vm/87LNxpPmve6PFlcWjGJE360RaAQagUbgfRDojPs+OLeWB0eA31Xxf+f5uyt+b8Vvm/L/2LuXC/zOiuLo6G+t7mVv620EGoFG4KMj0MXVR4+A9v8NAQoY//QNhQxFFf/G1CP8CRz/aQjseu8f13d4NAKNQCPQCBxHoIur45g1RyPQCDQCjUAj0Ag0AlMEuriaQtMTjUAj0Ag0Ao1AI9AIHEegi6vjmDVHI9AINAKNQCPQCDQCUwS6uJpC0xONQCPQCDQCjUAj0AgcR6CLq+OYNUcj0Ag0Ao1AI9AINAJTBLq4mkLTE41AI9AINAKNQCPQCBxHoIur45hdzcH/Un/pv7Z9tfIWcAoC/HtYrOO1/4DpKca0kEagEWgEGoGHQqCLq5OWw38Re+Ww7eLqJNDvKOZViyuKfuLTf0z1jhC36kagEWgEnhaBLq5OWrovX768vY2i32tdXO0h9Pjzo+KKdeV6hjazlX9IlQKLP/3T7UcEjnyA+pGznxqBRuCjIfAcJ8GLrUoXV8+/oK9aXD3/ytzOgy6ubodtS24EXg2BLq7usKJdXN0B9JNVdnF1MqBPIK6LqydYpDaxEXgQBC4qrvjKwD90S6HA3z7j6wT+PpvNAoJDiD84K51fm339+vWNz3GeVxoJLv/WGl9hKFN+ZPnbEeSj//Pnz06/9diaspBp8oTXph8+2yvfZ3nx17aqAyzFCH1cjK18NZOHPD6KDfLkxzb+Vp6+OI6dzOmLuuGt6yFN/g2+xIk1SB/4u3zaIh7K8Nl+hl3KwH4wWflbf6s+qb/2K+uRuMMvdrVX9pE9g4/YgCx8SV2JM5gwn23F92qjz8jJNUIWc3V/QccYc8Qc9sLnejOObaxf5oS0s95DN1rv3E/pOzqOxik82FnxxZaZfuNNLJCRF+Nn+F/x6OdGoBF4bgQOF1ckOxIniZSCiuTiQUDispGAoKOH1qTNs4mK5JgHsolMGbUn+cKPLGTkH9qVlmQPDXKh4VIH9zbtSVkeDozZ1Oezvfw+IxvaPAzUu6cDnMBQe/WL8T1MPHjFGp3qxZ/ETP/obdimfeqXLgss/UU2unhGNg178R0+ZUjPuM0xn+0rdhx0yEIPOpin55lr78BGD5e20I98Un/t0bG3HuKObBo9vnKlXuaO7Bn9pscH4lld2IV81kAsec7iZ8X3LVuVi93EHvIZq809z1pgH/a6VsjnXlsrb31Gj76Ju/zq9hk9yCfmuB/5r15kwg8vjXF4GKdnDnxX4g0foUcGvWvM+LX+Vzz6uRFoBJ4fge8n36IvJCWSez3gTNgkGhpJiCSWB7SfdhmXDlp5SXRbzaSZNNhh8vQw8DnpTIyMaceIzuQpb312POUxpg/6pQ7Ga5vJTDpwg27En3Tog441yTdS+OZ4FmjanbQpj3swrTbKhz259mLOfG3yOF6fHa/YYTsxUm3U1z1MlJv9yKec37uv6zGyBcy4aju6Z7JYQpa6kMO9DXzqOjmX/cj3ma11jSyiMoaUx9xWs+hOm0f0xmr1G53I0P+qDzvABF+4p2l/jVPmoCOuqp7VeDNOV+Nv1f8RJj3WCDQCz43Af0+CDX9MciY8nr18Y2TiGSV9D4OaJJUr78wEZJJMM9EnrcmP5Kld9iZwnk3AJuSUUe2uz9Iqw2d1I5/m/IoO6LEZG+HjAEAv1x4m6BvRuR61YK12ohsZrCm6Payq3/rz5lz8R3lZRDtdeeqzdMoQO3XzXC/n5J31Kz7NeBnfWw/kV9x55som3aV7BlnKGMWCsVJ17q3nyFZk1DUCB2gzjoytXHPinHH2tjLUgf1bDTo+HMwavkAzkqMt2ElT90gWMpivzXHk18s5eIzT0Tqs+I9s5GlrtaOfG4FG4HUQ+PEk2PHL5EKCmF0mnkxKKXY0btKRN+nz3iSLDIoAEjmJygLGxDqzjXF1wT9q1b76LI+6fBYb5NPgmx0YKRPboWOMgxK5FFkWg3uY6E+lm41XO9WjTWAqTR5E1V/9dtw1cJzeOcfqs+Pqw2btxp7ZhZytturTSMbqemhn4q69KVffnBv1ymBu5NtIlzoqpqu+a4dy7Ks8xonL3C/Eaz7zockij3FkWNyhB/tnTd+we9a0aRRj8ouhtCNZI3zlF49Rj0yaa6kudaz6r64urkSu+0bgdRG4qLjKT6wzaEaJDNrRuEmnJq2RbGgzcSOPZE/iNbGOknDKkifHvK/21Wfp1OWziRf7aKs65ONwSbtXMZnRzcbVx7w04FffBla/q7/67XjlZ965Suuz/cgm1viSdsSnkXxt2VsP9UBvAzOubMq7Zs+MdKkjMZZuZT1HtiIz5anDDzUUEawzvOm3PPl2C159x65Z0+Z7F1cr8aY/6Tt+XeP/DJcebwQagedG4MeTYMcXDggS60oigo6kU9to3ARbk1blrc8UI7xpQSYyPAT2DjIOH3hGrdpXn+Uxofps4vUgUUcWTNKmTOVUulVMZnSz8bTT+xFeaSN2a6c+2G9hXnnqszK0A5vBAd3gd0lT1opPI/nauLceI3x9e5Nyz9gzI13q0F6ej/g+shUZKU8dFlQUQK53FtOz9dIe7J+1lfVW52hNr/1acEW/tqsLv7Jd43/K6ftGoBF4HQTGFcbEPxIRSZmLT7G1kXxMpPVwlnY0vnV4yAcNSbYeepnAlcPBXOl49tMxPXbU1/Pyc8DY9DflwVcPp7QDXg8E+mzSqUNb0J3N8ZrIk4Z7ba50s3H1M48f4FB5HddG9IwOXcYtHirmyK8YZSGsH9BZiHKfuur6MAdNfUOiLHptX/Ep+bwXd22p48plvmI3woi4MYYu3TMjXdqVOo/4nnzKop+Ns0b4wdd+rGM2xpjLPcJ9XdfkyXt11vWmgGP/6D902dCBbtZB3cpKOu+hqzKYk6fqZw7dxpt2GAPKvdZ/5XTfCDQCr4PAoeIKtz1MSVT+PocEaJIjAdFmiWw0PktaCbM0JHEOQBKcB2Ee7BY1lY5ndNNI2j4ry4O/2pfySML6Kb82Yg+8+p864IEXHvlM8ukXupBjskZeTeTqs5e/0s3G004OJO0RB+2sODDO2Kh5iCILOjGCPnkydqBLPuhG2EGDzVzSV1/TpiM+JZ/34oYvW+shXdpiPOIb4/S09PuSPTPSpb3oEOMjvs9sTXnqoLdwQ1ctQvCVcdade3ADP2PLdU15eZ+/WRIf7DOeoNXe1GGcpT0z+5GBja5J6s+9uhVv0CHD2MBPCq9V/13HtDft6PtGoBF4HQTGp+WOfyRDkx/JxqRFovETpGNV1GjcpEOS2mokpSyCSHLYoU55OcwqHc+Z1PDBRIxNzDNW7UO2h4Vz2CuvOk2weZAgL+3AVhN0Jnns9aDw8PBA3sNkht1svNqZOIjnyMbqr37Tg1HGAwcUWI94iJH0FbpqkzLBXVqwRy5j2LfVVn2ayVhZjxG+4oCt2qsObEqMjKWVPTPSpdyK8arvM1urPPVAT3xwjRpryJx+z9Z1xMsYa5r4IIu9g+82ZFpgq4e1yjazHxoxT3rv8W8l3jI2sBGbaCv+u47yqLv7RqAReD0ELiquXg+G//OIBEsCJsl3ux6BrYPueuktoRFoBBqBRqAReEwEuriKdeEtAsVVf7IMUK647eLqCvCatRFoBBqBRuBpEfiwxRVfN/G1A6/zufz6jq8dup2DQBdX5+DYUhqBRqARaASeC4EPW1xx8OfvebinyOKrwW7nINDF1Tk4tpRGoBFoBBqB50LgwxZXz7VMbW0j0Ag0Ao1AI9AIPAsCXVw9y0q1nY1AI9AINAKNQCPwFAh0cfUUy9RGNgKNQCPQCDQCjcCzIPA/AYBZ6+1YdaAAAAAASUVORK5CYII=)
6 e -3
2 e -9
-2 e -9
-2 e 9
2 e 9
Seja o operador linear de IR2 definido por T(x, y) = (3x - 2y, 5x + 6y), indique a alternativa que determina as coordenadas do vetor
IR2 tal que T (
) = (- 12, 8).
X= - 2 e y = - 3.
X= -3 e y = 2.
X= 3 e y = - 2.
X= 3 e y = 4.
X= - 2 e y = 3.
Existem funções que relacionam espaços vetoriais ou subespaços vetoriais a outros espaços vetoriais chamadas de transformações lineares.
Assim sabendo que T: IR2
IR3 associa vetores pertencentes a IR2 do tipo (x, y) = (- 4, 1), definido pela imagem f(x, y) = (2x, x - y, - y); tem-se uma transformação linear, se:
f(- 4, 1) = (- 8, 5, - 1)
f(- 4, 1) = (8, 5, - 1)
f(- 4, 1) = (8, 5, 1)
f(- 4, 1) = (- 8, - 5, 1)
f(- 4, 1) = (- 8, - 5, - 1)
Verifique se o vetor dado v = (1, 2, 4) pertence ou não ao subespaço vetorial com v1= (1, 2, 1) ; v2 = (1, 0, 2) e v3 = (1, 1, 0) assinale a alternativa que indica os valores reais de a, b e c; na combinação linear definida por
![](data:image/png;base64,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)
Com os valores reais em a = 2; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 2; b = 2 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = 1 e c = 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 1; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = -1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
V = (5/2, 1)
V = ( - 5/2, 1)
V = (- 5/2, - 1)
V = (2/5, 1)
V = (3/2, - 1)
![](data:image/png;base64,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)
6 e -3
2 e -9
-2 e -9
-2 e 9
2 e 9
Seja o operador linear de IR2 definido por T(x, y) = (3x - 2y, 5x + 6y), indique a alternativa que determina as coordenadas do vetor
IR2 tal que T (
) = (- 12, 8).
X= - 2 e y = - 3.
X= -3 e y = 2.
X= 3 e y = - 2.
X= 3 e y = 4.
X= - 2 e y = 3.
Existem funções que relacionam espaços vetoriais ou subespaços vetoriais a outros espaços vetoriais chamadas de transformações lineares.
Assim sabendo que T: IR2
IR3 associa vetores pertencentes a IR2 do tipo (x, y) = (- 4, 1), definido pela imagem f(x, y) = (2x, x - y, - y); tem-se uma transformação linear, se:
f(- 4, 1) = (- 8, 5, - 1)
f(- 4, 1) = (8, 5, - 1)
f(- 4, 1) = (8, 5, 1)
f(- 4, 1) = (- 8, - 5, 1)
f(- 4, 1) = (- 8, - 5, - 1)
Verifique se o vetor dado v = (1, 2, 4) pertence ou não ao subespaço vetorial com v1= (1, 2, 1) ; v2 = (1, 0, 2) e v3 = (1, 1, 0) assinale a alternativa que indica os valores reais de a, b e c; na combinação linear definida por
![](data:image/png;base64,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)
Com os valores reais em a = 2; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 2; b = 2 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = 1 e c = 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 1; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = -1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
6 e -3
2 e -9
-2 e -9
-2 e 9
2 e 9
Seja o operador linear de IR2 definido por T(x, y) = (3x - 2y, 5x + 6y), indique a alternativa que determina as coordenadas do vetor
IR2 tal que T (
) = (- 12, 8).
X= - 2 e y = - 3.
X= -3 e y = 2.
X= 3 e y = - 2.
X= 3 e y = 4.
X= - 2 e y = 3.
Existem funções que relacionam espaços vetoriais ou subespaços vetoriais a outros espaços vetoriais chamadas de transformações lineares.
Assim sabendo que T: IR2
IR3 associa vetores pertencentes a IR2 do tipo (x, y) = (- 4, 1), definido pela imagem f(x, y) = (2x, x - y, - y); tem-se uma transformação linear, se:
f(- 4, 1) = (- 8, 5, - 1)
f(- 4, 1) = (8, 5, - 1)
f(- 4, 1) = (8, 5, 1)
f(- 4, 1) = (- 8, - 5, 1)
f(- 4, 1) = (- 8, - 5, - 1)
Verifique se o vetor dado v = (1, 2, 4) pertence ou não ao subespaço vetorial com v1= (1, 2, 1) ; v2 = (1, 0, 2) e v3 = (1, 1, 0) assinale a alternativa que indica os valores reais de a, b e c; na combinação linear definida por
![](data:image/png;base64,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)
Com os valores reais em a = 2; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 2; b = 2 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = 1 e c = 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 1; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = -1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
X= - 2 e y = - 3.
X= -3 e y = 2.
X= 3 e y = - 2.
X= 3 e y = 4.
X= - 2 e y = 3.
Existem funções que relacionam espaços vetoriais ou subespaços vetoriais a outros espaços vetoriais chamadas de transformações lineares.
Assim sabendo que T: IR2
IR3 associa vetores pertencentes a IR2 do tipo (x, y) = (- 4, 1), definido pela imagem f(x, y) = (2x, x - y, - y); tem-se uma transformação linear, se:
f(- 4, 1) = (- 8, 5, - 1)
f(- 4, 1) = (8, 5, - 1)
f(- 4, 1) = (8, 5, 1)
f(- 4, 1) = (- 8, - 5, 1)
f(- 4, 1) = (- 8, - 5, - 1)
Verifique se o vetor dado v = (1, 2, 4) pertence ou não ao subespaço vetorial com v1= (1, 2, 1) ; v2 = (1, 0, 2) e v3 = (1, 1, 0) assinale a alternativa que indica os valores reais de a, b e c; na combinação linear definida por
![](data:image/png;base64,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)
Com os valores reais em a = 2; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 2; b = 2 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = 1 e c = 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 1; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = -1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
f(- 4, 1) = (- 8, 5, - 1)
f(- 4, 1) = (8, 5, - 1)
f(- 4, 1) = (8, 5, 1)
f(- 4, 1) = (- 8, - 5, 1)
f(- 4, 1) = (- 8, - 5, - 1)
Verifique se o vetor dado v = (1, 2, 4) pertence ou não ao subespaço vetorial com v1= (1, 2, 1) ; v2 = (1, 0, 2) e v3 = (1, 1, 0) assinale a alternativa que indica os valores reais de a, b e c; na combinação linear definida por
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Com os valores reais em a = 2; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 2; b = 2 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = 1 e c = 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 1; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = -1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 2; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 2; b = 2 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = 1 e c = 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = 1; b = 1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.
Com os valores reais em a = - 2; b = -1 e c = - 2, teremos uma combinação linear pertencente ao subespaço vetorial dado.