# findindex

## 構文

``[numindex1,numindex2,...,numindexk] = findindex(var,strindex1,strindex2,...,strindexk)``
``numindex = findindex(var,strindex1,strindex2,...,strindexk)``

## 説明

``[numindex1,numindex2,...,numindexk] = findindex(var,strindex1,strindex2,...,strindexk)` は、最適化変数 `var` 内の名前付きインデックス変数と等価な数値インデックスを検索します。`

``numindex = findindex(var,strindex1,strindex2,...,strindexk)` は、名前付きインデックス変数と等価な線形インデックスを検索します。`

## 例

すべて折りたたむ

`colors = optimvar('colors',["black","white","red","green","blue"],["cyan","magenta","yellow","black"]);`

`[idxadd,idxsub] = findindex(colors,{'red','black'},{'black'})`
```idxadd = 1×2 3 1 ```
```idxsub = 4 ```

`colors = optimvar('colors',["black","white","red","green","blue"],["cyan","magenta","yellow","black"]);`

`["white","black"]``["red","cyan"]``["green","magenta"]`、および `["blue","yellow"]` の組み合わせと等価な線形インデックスを検索します。

`idx = findindex(colors,["white","red","green","blue"],["black","cyan","magenta","yellow"])`
```idx = 1×4 17 3 9 15 ```

```rng(0) % For reproducibility p = optimproblem('ObjectiveSense', 'maximize'); flow = optimvar('flow', ... {'apples', 'oranges', 'bananas', 'berries'}, {'NYC', 'BOS', 'LAX'}, ... 'LowerBound',0,'Type','integer'); p.Objective = sum(sum(rand(4,3).*flow)); p.Constraints.NYC = rand(1,4)*flow(:,'NYC') <= 10; p.Constraints.BOS = rand(1,4)*flow(:,'BOS') <= 12; p.Constraints.LAX = rand(1,4)*flow(:,'LAX') <= 35; sol = solve(p);```
```Solving problem using intlinprog. Running HiGHS 1.6.0: Copyright (c) 2023 HiGHS under MIT licence terms Presolving model 3 rows, 12 cols, 12 nonzeros 3 rows, 12 cols, 12 nonzeros Solving MIP model with: 3 rows 12 cols (0 binary, 12 integer, 0 implied int., 0 continuous) 12 nonzeros Nodes | B&B Tree | Objective Bounds | Dynamic Constraints | Work Proc. InQueue | Leaves Expl. | BestBound BestSol Gap | Cuts InLp Confl. | LpIters Time 0 0 0 0.00% 1160.150059 -inf inf 0 0 0 0 0.0s S 0 0 0 0.00% 1160.150059 1027.233133 12.94% 0 0 0 0 0.0s Solving report Status Optimal Primal bound 1027.23313332 Dual bound 1027.23313332 Gap 0% (tolerance: 0.01%) Solution status feasible 1027.23313332 (objective) 0 (bound viol.) 0 (int. viol.) 0 (row viol.) Timing 0.01 (total) 0.00 (presolve) 0.00 (postsolve) Nodes 1 LP iterations 3 (total) 0 (strong br.) 0 (separation) 0 (heuristics) Optimal solution found. Intlinprog stopped at the root node because the objective value is within a gap tolerance of the optimal value, options.AbsoluteGapTolerance = 1e-06. The intcon variables are integer within tolerance, options.ConstraintTolerance = 1e-06. ```

ニューヨークとロサンゼルスへのオレンジとベリーの最適フローを求めます。

`[idxFruit,idxAirports] = findindex(flow, {'oranges','berries'}, {'NYC', 'LAX'})`
```idxFruit = 1×2 2 4 ```
```idxAirports = 1×2 1 3 ```
`orangeBerries = sol.flow(idxFruit, idxAirports)`
```orangeBerries = 2×2 0 980 70 0 ```

この表示は、`NYC` 向けのオレンジは 0、70 のベリーが `NYC` 向け、980 のオレンジが `LAX` 向けで、`LAX` 向けのベリーは 0 であることを示しています。

`Fruit Airports`

` ----- --------`

` Berries NYC`

` Apples BOS`

` Oranges LAX`

`idx = findindex(flow, {'berries', 'apples', 'oranges'}, {'NYC', 'BOS', 'LAX'})`
```idx = 1×3 4 5 10 ```
`optimalFlow = sol.flow(idx)`
```optimalFlow = 1×3 70 28 980 ```

この表示は、70 のベリーが `NYC` 向け、28 のアップルが `BOS` 向けで、980 のオレンジが `LAX` 向けであることを示しています。

さまざまな地表タイプ、候補となる作物、耕起方法を伴う問題の名前付きインデックス変数を作成します。

```land = ["irr-good","irr-poor","dry-good","dry-poor"]; crops = ["wheat-lentil","wheat-corn","barley-chickpea","barley-lentil","wheat-onion","barley-onion"]; plow = ["tradition","mechanized"]; xcrop = optimvar('xcrop',land,crops,plow,'LowerBound',0);```

`x0.xcrop = zeros(size(xcrop));`

```[idxLand, idxCrop, idxPlough] = findindex(xcrop, ["dry-good","dry-poor"], ... ["wheat-onion","wheat-lentil"],"tradition"); x0.xcrop(idxLand,idxCrop,idxPlough) = 3000;```

```Land Crops Method Value dry-good wheat-corn mechanized 2000 irr-poor barley-onion tradition 5000 irr-good barley-chickpea mechanized 3500 ```
```idx = findindex(xcrop,... ["dry-good","irr-poor","irr-good"],... ["wheat-corn","barley-onion","barley-chickpea"],... ["mechanized","tradition","mechanized"]); x0.xcrop(idx) = [2000,5000,3500];```

## 入力引数

すべて折りたたむ

データ型: `double` | `char` | `string` | `cell`

## 出力引数

すべて折りたたむ

• `var` の次元の数。各出力ベクトル `numindexj` は、対応する入力引数 `strindexj` に等価な数値です。

• 1。この場合、各入力 `strindex``j` のサイズはすべての `j` で同じでなければなりません。また、出力は線形インデックス付け条件を満たします。

`var(numindex(j)) = var(strindex1(j),...,strindexk(j))` (すべての `j` について)

R2018a で導入