第 14559 題：內積誘導範數

原題與已知條件

向量與已認證內積綁定同一實空間。操作=innernorm-parallelogram；資料={"matrix":[[10,2],[2,5]],"x":[-2,1],"y":[3,4]}。請依 [x,y,lhs,rhs,equal] 欄序回答。

• G001 — 向量與已認證內積綁定同一實空間。操作=innernorm-parallelogram；資料={"matrix":[[10,2],[2,5]],"x":[-2,1],"y":[3,4]}。請依 [x,y,lhs,rhs,equal] 欄序回答。

定義與適用範圍

沿用原題明示的論域、假設與來源 MKO 定義。本題已完成第二對話數理與中英語義覆核，並由原始參數逐題獨立重算模型、答案與每個算術步驟；原題仍為 candidate。

• mko-inner-product-induced-norm

逐步推導

inner parent diagonal product := mul(10, 5)

inner parent diagonal product = 50

inner parent off-diagonal product := mul(2, 2)

inner parent off-diagonal product = 4

inner parent determinant := sub(50, 4)

inner parent determinant = 46

same real R2 inner-product candidate matrix=[[10, 2], [2, 5]]

symmetric=是;leading=10;determinant=46

inner parent completed-square slope := div(2, 10)

inner parent completed-square slope = 1/5

inner parent completed-square remainder := div(46, 10)

inner parent completed-square remainder = 23/5

positive leading=10;slope=1/5;positive remainder=23/5

q(s,t)=(10)*(s+(1/5)*t)^2+(23/5)*t^2;positive iff(s,t)!=0

same certified real inner product;arbitrary real x,y

if y=0 then inner(x,y)=0;otherwise yy>0 and q(x-a*y)=xx-2*a*xy+a^2*yy>=0

norm(x)=nonnegative sqrt(inner(x,x));inner product positive definite and bilinear

norm(x)>=0;norm(x)=0 iff x=0;norm(a*x)=abs(a)*norm(x)

Cauchy-Schwarz holds for arbitrary x,y in this same inner product

norm(x+y)^2=xx+2*xy+yy<=(norm(x)+norm(y))^2;nonnegative roots give triangle inequality

x self-inner-product:same matrix=[[10, 2], [2, 5]];first vector=[-2, 1];second vector=[-2, 1]

inner product=first^T*(matrix*second)

x self-inner-product matrix term0,0 := mul(10, -2)

x self-inner-product matrix term0,0 = -20

x self-inner-product matrix term0,1 := mul(2, 1)

x self-inner-product matrix term0,1 = 2

x self-inner-product image coordinate0 := sum(-20, 2)

x self-inner-product image coordinate0 = -18

x self-inner-product matrix term1,0 := mul(2, -2)

x self-inner-product matrix term1,0 = -4

x self-inner-product matrix term1,1 := mul(5, 1)

x self-inner-product matrix term1,1 = 5

x self-inner-product image coordinate1 := sum(-4, 5)

x self-inner-product image coordinate1 = 1

x self-inner-product first-slot term0 := mul(-2, -18)

x self-inner-product first-slot term0 = 36

x self-inner-product first-slot term1 := mul(1, 1)

x self-inner-product first-slot term1 = 1

x self-inner-product inner sum := sum(36, 1)

x self-inner-product inner sum = 37

y self-inner-product:same matrix=[[10, 2], [2, 5]];first vector=[3, 4];second vector=[3, 4]

inner product=first^T*(matrix*second)

y self-inner-product matrix term0,0 := mul(10, 3)

y self-inner-product matrix term0,0 = 30

y self-inner-product matrix term0,1 := mul(2, 4)

y self-inner-product matrix term0,1 = 8

y self-inner-product image coordinate0 := sum(30, 8)

y self-inner-product image coordinate0 = 38

y self-inner-product matrix term1,0 := mul(2, 3)

y self-inner-product matrix term1,0 = 6

y self-inner-product matrix term1,1 := mul(5, 4)

y self-inner-product matrix term1,1 = 20

y self-inner-product image coordinate1 := sum(6, 20)

y self-inner-product image coordinate1 = 26

y self-inner-product first-slot term0 := mul(3, 38)

y self-inner-product first-slot term0 = 114

y self-inner-product first-slot term1 := mul(4, 26)

y self-inner-product first-slot term1 = 104

y self-inner-product inner sum := sum(114, 104)

y self-inner-product inner sum = 218

parallelogram sum coordinate0 := sum(-2, 3)

parallelogram sum coordinate0 = 1

parallelogram sum coordinate1 := sum(1, 4)

parallelogram sum coordinate1 = 5

parallelogram difference coordinate0 := sub(-2, 3)

parallelogram difference coordinate0 = -5

parallelogram difference coordinate1 := sub(1, 4)

parallelogram difference coordinate1 = -3

sum self-inner-product:same matrix=[[10, 2], [2, 5]];first vector=[1, 5];second vector=[1, 5]

inner product=first^T*(matrix*second)

sum self-inner-product matrix term0,0 := mul(10, 1)

sum self-inner-product matrix term0,0 = 10

sum self-inner-product matrix term0,1 := mul(2, 5)

sum self-inner-product matrix term0,1 = 10

sum self-inner-product image coordinate0 := sum(10, 10)

sum self-inner-product image coordinate0 = 20

sum self-inner-product matrix term1,0 := mul(2, 1)

sum self-inner-product matrix term1,0 = 2

sum self-inner-product matrix term1,1 := mul(5, 5)

sum self-inner-product matrix term1,1 = 25

sum self-inner-product image coordinate1 := sum(2, 25)

sum self-inner-product image coordinate1 = 27

sum self-inner-product first-slot term0 := mul(1, 20)

sum self-inner-product first-slot term0 = 20

sum self-inner-product first-slot term1 := mul(5, 27)

sum self-inner-product first-slot term1 = 135

sum self-inner-product inner sum := sum(20, 135)

sum self-inner-product inner sum = 155

difference self-inner-product:same matrix=[[10, 2], [2, 5]];first vector=[-5, -3];second vector=[-5, -3]

inner product=first^T*(matrix*second)

difference self-inner-product matrix term0,0 := mul(10, -5)

difference self-inner-product matrix term0,0 = -50

difference self-inner-product matrix term0,1 := mul(2, -3)

difference self-inner-product matrix term0,1 = -6

difference self-inner-product image coordinate0 := sum(-50, -6)

difference self-inner-product image coordinate0 = -56

difference self-inner-product matrix term1,0 := mul(2, -5)

difference self-inner-product matrix term1,0 = -10

difference self-inner-product matrix term1,1 := mul(5, -3)

difference self-inner-product matrix term1,1 = -15

difference self-inner-product image coordinate1 := sum(-10, -15)

difference self-inner-product image coordinate1 = -25

difference self-inner-product first-slot term0 := mul(-5, -56)

difference self-inner-product first-slot term0 = 280

difference self-inner-product first-slot term1 := mul(-3, -25)

difference self-inner-product first-slot term1 = 75

difference self-inner-product inner sum := sum(280, 75)

difference self-inner-product inner sum = 355

parallelogram left sum := sum(155, 355)

parallelogram left sum = 510

twice x self-inner-product := mul(2, 37)

twice x self-inner-product = 74

twice y self-inner-product := mul(2, 218)

twice y self-inner-product = 436

parallelogram right sum := sum(74, 436)

parallelogram right sum = 510

same inner product;left=510;right=510

bilinear expansions have opposite cross terms which cancel;identity=是

逐步反向核對

• S059 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S058 · premise_and_result_audit — 以精確有理數重算 parallelogram right sum，不以小數近似替代。
• S057 · premise_and_result_audit — 以精確有理數重算 twice y self-inner-product，不以小數近似替代。
• S056 · premise_and_result_audit — 以精確有理數重算 twice x self-inner-product，不以小數近似替代。
• S055 · premise_and_result_audit — 以精確有理數重算 parallelogram left sum，不以小數近似替代。
• S054 · premise_and_result_audit — 以精確有理數重算 difference self-inner-product inner sum，不以小數近似替代。
• S053 · premise_and_result_audit — 以精確有理數重算 difference self-inner-product first-slot term1，不以小數近似替代。
• S052 · premise_and_result_audit — 以精確有理數重算 difference self-inner-product first-slot term0，不以小數近似替代。
• S051 · premise_and_result_audit — 以精確有理數重算 difference self-inner-product image coordinate1，不以小數近似替代。
• S050 · premise_and_result_audit — 以精確有理數重算 difference self-inner-product matrix term1,1，不以小數近似替代。
• S049 · premise_and_result_audit — 以精確有理數重算 difference self-inner-product matrix term1,0，不以小數近似替代。
• S048 · premise_and_result_audit — 以精確有理數重算 difference self-inner-product image coordinate0，不以小數近似替代。
• S047 · premise_and_result_audit — 以精確有理數重算 difference self-inner-product matrix term0,1，不以小數近似替代。
• S046 · premise_and_result_audit — 以精確有理數重算 difference self-inner-product matrix term0,0，不以小數近似替代。
• S045 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S044 · premise_and_result_audit — 以精確有理數重算 sum self-inner-product inner sum，不以小數近似替代。
• S043 · premise_and_result_audit — 以精確有理數重算 sum self-inner-product first-slot term1，不以小數近似替代。
• S042 · premise_and_result_audit — 以精確有理數重算 sum self-inner-product first-slot term0，不以小數近似替代。
• S041 · premise_and_result_audit — 以精確有理數重算 sum self-inner-product image coordinate1，不以小數近似替代。
• S040 · premise_and_result_audit — 以精確有理數重算 sum self-inner-product matrix term1,1，不以小數近似替代。
• S039 · premise_and_result_audit — 以精確有理數重算 sum self-inner-product matrix term1,0，不以小數近似替代。
• S038 · premise_and_result_audit — 以精確有理數重算 sum self-inner-product image coordinate0，不以小數近似替代。
• S037 · premise_and_result_audit — 以精確有理數重算 sum self-inner-product matrix term0,1，不以小數近似替代。
• S036 · premise_and_result_audit — 以精確有理數重算 sum self-inner-product matrix term0,0，不以小數近似替代。
• S035 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S034 · premise_and_result_audit — 以精確有理數重算 parallelogram difference coordinate1，不以小數近似替代。
• S033 · premise_and_result_audit — 以精確有理數重算 parallelogram difference coordinate0，不以小數近似替代。
• S032 · premise_and_result_audit — 以精確有理數重算 parallelogram sum coordinate1，不以小數近似替代。
• S031 · premise_and_result_audit — 以精確有理數重算 parallelogram sum coordinate0，不以小數近似替代。
• S030 · premise_and_result_audit — 以精確有理數重算 y self-inner-product inner sum，不以小數近似替代。
• S029 · premise_and_result_audit — 以精確有理數重算 y self-inner-product first-slot term1，不以小數近似替代。
• S028 · premise_and_result_audit — 以精確有理數重算 y self-inner-product first-slot term0，不以小數近似替代。
• S027 · premise_and_result_audit — 以精確有理數重算 y self-inner-product image coordinate1，不以小數近似替代。
• S026 · premise_and_result_audit — 以精確有理數重算 y self-inner-product matrix term1,1，不以小數近似替代。
• S025 · premise_and_result_audit — 以精確有理數重算 y self-inner-product matrix term1,0，不以小數近似替代。
• S024 · premise_and_result_audit — 以精確有理數重算 y self-inner-product image coordinate0，不以小數近似替代。
• S023 · premise_and_result_audit — 以精確有理數重算 y self-inner-product matrix term0,1，不以小數近似替代。
• S022 · premise_and_result_audit — 以精確有理數重算 y self-inner-product matrix term0,0，不以小數近似替代。
• S021 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S020 · premise_and_result_audit — 以精確有理數重算 x self-inner-product inner sum，不以小數近似替代。
• S019 · premise_and_result_audit — 以精確有理數重算 x self-inner-product first-slot term1，不以小數近似替代。
• S018 · premise_and_result_audit — 以精確有理數重算 x self-inner-product first-slot term0，不以小數近似替代。
• S017 · premise_and_result_audit — 以精確有理數重算 x self-inner-product image coordinate1，不以小數近似替代。
• S016 · premise_and_result_audit — 以精確有理數重算 x self-inner-product matrix term1,1，不以小數近似替代。
• S015 · premise_and_result_audit — 以精確有理數重算 x self-inner-product matrix term1,0，不以小數近似替代。
• S014 · premise_and_result_audit — 以精確有理數重算 x self-inner-product image coordinate0，不以小數近似替代。
• S013 · premise_and_result_audit — 以精確有理數重算 x self-inner-product matrix term0,1，不以小數近似替代。
• S012 · premise_and_result_audit — 以精確有理數重算 x self-inner-product matrix term0,0，不以小數近似替代。
• S011 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S010 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S009 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S008 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S007 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S006 · premise_and_result_audit — 以精確有理數重算 inner parent completed-square remainder，不以小數近似替代。
• S005 · premise_and_result_audit — 以精確有理數重算 inner parent completed-square slope，不以小數近似替代。
• S004 · premise_and_result_audit — 回到原始資料與本步條件核對，不把單向推論誤稱等價。
• S003 · premise_and_result_audit — 以精確有理數重算 inner parent determinant，不以小數近似替代。
• S002 · premise_and_result_audit — 以精確有理數重算 inner parent off-diagonal product，不以小數近似替代。
• S001 · premise_and_result_audit — 以精確有理數重算 inner parent diagonal product，不以小數近似替代。

結論

([-2, 1], [3, 4], 510, 510, 是)

依上列完整代值及條件核對得到原題答案；原題編號、格式與狀態保持不變。

本題覆核與來源綁定

覆核日期：2026-10-26

原題、答案、步驟表達式、數值 AST 與既有歷史保持原樣；原來的候選狀態屬於來源歷史，不覆寫為形式化證明。