Advanced configuration API for cm-optimization

.space.kts

@@ -1 +1,3 @@
-job("Build") { gradlew("openjdk:11", "build") }
+job("Build") {
+    gradlew("openjdk:11", "build")
+}

kmath-commons/src/main/kotlin/kscience/kmath/commons/expressions/DerivativeStructureExpression.kt

@@ -106,7 +106,7 @@ public class DerivativeStructureExpression(
     /**
      * Get the derivative expression with given orders
      */
-    public override fun derivative(orders: Map<Symbol, Int>): Expression<Double> = Expression { arguments ->
+    public override fun derivativeOrNull(orders: Map<Symbol, Int>): Expression<Double> = Expression { arguments ->
         with(DerivativeStructureField(orders.values.maxOrNull() ?: 0, arguments)) { function().derivative(orders) }
     }
 }

kmath-commons/src/main/kotlin/kscience/kmath/commons/optimization/CMOptimizationProblem.kt

@@ -0,0 +1,100 @@
+package kscience.kmath.commons.optimization
+
+import kscience.kmath.expressions.*
+import org.apache.commons.math3.optim.*
+import org.apache.commons.math3.optim.nonlinear.scalar.GoalType
+import org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer
+import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction
+import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunctionGradient
+import org.apache.commons.math3.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer
+import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.AbstractSimplex
+import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.NelderMeadSimplex
+import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer
+import kotlin.reflect.KClass
+
+public operator fun PointValuePair.component1(): DoubleArray = point
+public operator fun PointValuePair.component2(): Double = value
+
+public class CMOptimizationProblem(
+    override val symbols: List<Symbol>,
+) : OptimizationProblem<Double>, SymbolIndexer {
+    protected val optimizationData: HashMap<KClass<out OptimizationData>, OptimizationData> = HashMap()
+    private var optimizatorBuilder: (() -> MultivariateOptimizer)? = null
+
+    public var convergenceChecker: ConvergenceChecker<PointValuePair> = SimpleValueChecker(DEFAULT_RELATIVE_TOLERANCE,
+        DEFAULT_ABSOLUTE_TOLERANCE, DEFAULT_MAX_ITER)
+
+    private fun addOptimizationData(data: OptimizationData) {
+        optimizationData[data::class] = data
+    }
+
+    init {
+        addOptimizationData(MaxEval.unlimited())
+    }
+
+    public fun initialGuess(map: Map<Symbol, Double>): Unit {
+        addOptimizationData(InitialGuess(map.toDoubleArray()))
+    }
+
+    public fun expression(expression: Expression<Double>): Unit {
+        val objectiveFunction = ObjectiveFunction {
+            val args = it.toMap()
+            expression(args)
+        }
+        addOptimizationData(objectiveFunction)
+    }
+
+    public fun derivatives(expression: DifferentiableExpression<Double>): Unit {
+        expression(expression)
+        val gradientFunction = ObjectiveFunctionGradient {
+            val args = it.toMap()
+            DoubleArray(symbols.size) { index ->
+                expression.derivative(symbols[index])(args)
+            }
+        }
+        addOptimizationData(gradientFunction)
+        if (optimizatorBuilder == null) {
+            optimizatorBuilder = {
+                NonLinearConjugateGradientOptimizer(
+                    NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES,
+                    convergenceChecker
+                )
+            }
+        }
+    }
+
+    public fun simplex(simplex: AbstractSimplex) {
+        addOptimizationData(simplex)
+        //Set optimization builder to simplex if it is not present
+        if (optimizatorBuilder == null) {
+            optimizatorBuilder = { SimplexOptimizer(convergenceChecker) }
+        }
+    }
+
+    public fun simplexSteps(steps: Map<Symbol, Double>) {
+        simplex(NelderMeadSimplex(steps.toDoubleArray()))
+    }
+
+    public fun goal(goalType: GoalType) {
+        addOptimizationData(goalType)
+    }
+
+    public fun optimizer(block: () -> MultivariateOptimizer) {
+        optimizatorBuilder = block
+    }
+
+    override fun optimize(): OptimizationResult<Double> {
+        val optimizer = optimizatorBuilder?.invoke() ?: error("Optimizer not defined")
+        val (point, value) = optimizer.optimize(*optimizationData.values.toTypedArray())
+        return OptimizationResult(point.toMap(), value)
+    }
+
+    public companion object {
+        public const val DEFAULT_RELATIVE_TOLERANCE: Double = 1e-4
+        public const val DEFAULT_ABSOLUTE_TOLERANCE: Double = 1e-4
+        public const val DEFAULT_MAX_ITER: Int = 1000
+    }
+}
+
+public fun CMOptimizationProblem.initialGuess(vararg pairs: Pair<Symbol, Double>): Unit = initialGuess(pairs.toMap())
+public fun CMOptimizationProblem.simplexSteps(vararg pairs: Pair<Symbol, Double>): Unit = simplexSteps(pairs.toMap())

kmath-commons/src/main/kotlin/kscience/kmath/commons/optimization/OptimizationProblem.kt

@@ -0,0 +1,17 @@
+package kscience.kmath.commons.optimization
+
+import kscience.kmath.expressions.Symbol
+import kotlin.reflect.KClass
+
+public typealias ParameterSpacePoint<T> = Map<Symbol, T>
+
+public class OptimizationResult<T>(
+    public val point: ParameterSpacePoint<T>,
+    public val value: T,
+    public val extra: Map<KClass<*>, Any> = emptyMap()
+)
+
+public interface OptimizationProblem<T : Any> {
+    public fun optimize(): OptimizationResult<T>
+}
+

kmath-commons/src/main/kotlin/kscience/kmath/commons/optimization/optimize.kt

@@ -1,103 +1,32 @@
 package kscience.kmath.commons.optimization
 
-import kscience.kmath.expressions.*
+import kscience.kmath.expressions.DifferentiableExpression
-import org.apache.commons.math3.optim.*
+import kscience.kmath.expressions.Expression
-import org.apache.commons.math3.optim.nonlinear.scalar.GoalType
+import kscience.kmath.expressions.Symbol
-import org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer
-import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction
-import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunctionGradient
-import org.apache.commons.math3.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer
-import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.NelderMeadSimplex
-import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer
 
-public typealias ParameterSpacePoint = Map<Symbol, Double>
-
-public class OptimizationResult(public val point: ParameterSpacePoint, public val value: Double)
-
-public operator fun PointValuePair.component1(): DoubleArray = point
-public operator fun PointValuePair.component2(): Double = value
-
-public object Optimization {
-    public const val DEFAULT_RELATIVE_TOLERANCE: Double = 1e-4
-    public const val DEFAULT_ABSOLUTE_TOLERANCE: Double = 1e-4
-    public const val DEFAULT_MAX_ITER: Int = 1000
-}
-
-
-private fun SymbolIndexer.objectiveFunction(expression: Expression<Double>) = ObjectiveFunction {
-    val args = it.toMap()
-    expression(args)
-}
-
-private fun SymbolIndexer.objectiveFunctionGradient(
-    expression: DifferentiableExpression<Double>,
-) = ObjectiveFunctionGradient {
-    val args = it.toMap()
-    DoubleArray(symbols.size) { index ->
-        expression.derivative(symbols[index])(args)
-    }
-}
-
-private fun SymbolIndexer.initialGuess(point: ParameterSpacePoint) = InitialGuess(point.toArray())
 
 /**
  * Optimize expression without derivatives
  */
 public fun Expression<Double>.optimize(
-    startingPoint: ParameterSpacePoint,
+    vararg symbols: Symbol,
-    goalType: GoalType = GoalType.MAXIMIZE,
+    configuration: CMOptimizationProblem.() -> Unit,
-    vararg additionalArguments: OptimizationData,
+): OptimizationResult<Double> {
-    optimizerBuilder: () -> MultivariateOptimizer = {
+    require(symbols.isNotEmpty()) { "Must provide a list of symbols for optimization" }
-        SimplexOptimizer(
+    val problem = CMOptimizationProblem(symbols.toList()).apply(configuration).apply(configuration)
-            SimpleValueChecker(
+    problem.expression(this)
-                Optimization.DEFAULT_RELATIVE_TOLERANCE,
+    return problem.optimize()
-                Optimization.DEFAULT_ABSOLUTE_TOLERANCE,
-                Optimization.DEFAULT_MAX_ITER
-            )
-        )
-    },
-): OptimizationResult = withSymbols(startingPoint.keys) {
-    val optimizer = optimizerBuilder()
-    val objectiveFunction = objectiveFunction(this@optimize)
-    val (point, value) = optimizer.optimize(
-        objectiveFunction,
-        initialGuess(startingPoint),
-        goalType,
-        MaxEval.unlimited(),
-        NelderMeadSimplex(symbols.size, 1.0),
-        *additionalArguments
-    )
-    OptimizationResult(point.toMap(), value)
 }
 
 /**
  * Optimize differentiable expression
  */
 public fun DifferentiableExpression<Double>.optimize(
-    startingPoint: ParameterSpacePoint,
+    vararg symbols: Symbol,
-    goalType: GoalType = GoalType.MAXIMIZE,
+    configuration: CMOptimizationProblem.() -> Unit,
-    vararg additionalArguments: OptimizationData,
+): OptimizationResult<Double> {
-    optimizerBuilder: () -> NonLinearConjugateGradientOptimizer = {
+    require(symbols.isNotEmpty()) { "Must provide a list of symbols for optimization" }
-        NonLinearConjugateGradientOptimizer(
+    val problem = CMOptimizationProblem(symbols.toList()).apply(configuration).apply(configuration)
-            NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES,
+    problem.derivatives(this)
-            SimpleValueChecker(
+    return problem.optimize()
-                Optimization.DEFAULT_RELATIVE_TOLERANCE,
-                Optimization.DEFAULT_ABSOLUTE_TOLERANCE,
-                Optimization.DEFAULT_MAX_ITER
-            )
-        )
-    },
-): OptimizationResult = withSymbols(startingPoint.keys) {
-    val optimizer = optimizerBuilder()
-    val objectiveFunction = objectiveFunction(this@optimize)
-    val objectiveGradient = objectiveFunctionGradient(this@optimize)
-    val (point, value) = optimizer.optimize(
-        objectiveFunction,
-        objectiveGradient,
-        initialGuess(startingPoint),
-        goalType,
-        MaxEval.unlimited(),
-        *additionalArguments
-    )
-    OptimizationResult(point.toMap(), value)
 }

kmath-commons/src/test/kotlin/kscience/kmath/commons/optimization/OptimizeTest.kt

@@ -1,10 +1,7 @@
 package kscience.kmath.commons.optimization
 
 import kscience.kmath.commons.expressions.DerivativeStructureExpression
-import kscience.kmath.expressions.Expression
-import kscience.kmath.expressions.Symbol
 import kscience.kmath.expressions.symbol
-import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer
 import org.junit.jupiter.api.Test
 
 internal class OptimizeTest {
@@ -14,22 +11,25 @@ internal class OptimizeTest {
     val normal = DerivativeStructureExpression {
         val x = bind(x)
         val y = bind(y)
-        exp(-x.pow(2)/2) + exp(-y.pow(2)/2)
+        exp(-x.pow(2) / 2) + exp(-y.pow(2) / 2)
     }
 
-    val startingPoint: Map<Symbol, Double> = mapOf(x to 1.0, y to 1.0)
-
     @Test
     fun testOptimization() {
-        val result = normal.optimize(startingPoint)
+        val result = normal.optimize(x, y) {
+            initialGuess(x to 1.0, y to 1.0)
+            //no need to select optimizer. Gradient optimizer is used by default
+        }
         println(result.point)
         println(result.value)
     }
 
     @Test
     fun testSimplexOptimization() {
-        val result = (normal as Expression<Double>).optimize(startingPoint){
+        val result = normal.optimize(x, y) {
-            SimplexOptimizer(1e-4,1e-4)
+            initialGuess(x to 1.0, y to 1.0)
+            simplexSteps(x to 2.0, y to 0.5)
+            //this sets simplex optimizer
         }
         println(result.point)
         println(result.value)

kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/DifferentiableExpression.kt

@@ -4,9 +4,15 @@ package kscience.kmath.expressions
  * And object that could be differentiated
  */
 public interface Differentiable<T> {
-    public fun derivative(orders: Map<Symbol, Int>): T
+    public fun derivativeOrNull(orders: Map<Symbol, Int>): T?
 }
 
+public fun <T> Differentiable<T>.derivative(orders: Map<Symbol, Int>): T =
+    derivativeOrNull(orders) ?: error("Derivative with orders $orders not provided")
+
+/**
+ * An expression that provid
+ */
 public interface DifferentiableExpression<T> : Differentiable<Expression<T>>, Expression<T>
 
 public fun <T> DifferentiableExpression<T>.derivative(vararg orders: Pair<Symbol, Int>): Expression<T> =
@@ -14,8 +20,19 @@ public fun <T> DifferentiableExpression<T>.derivative(vararg orders: Pair<Symbol
 
 public fun <T> DifferentiableExpression<T>.derivative(symbol: Symbol): Expression<T> = derivative(symbol to 1)
 
-public fun <T> DifferentiableExpression<T>.derivative(name: String): Expression<T> = derivative(StringSymbol(name) to 1)
+public fun <T> DifferentiableExpression<T>.derivative(name: String): Expression<T> =
+    derivative(StringSymbol(name) to 1)
 
 //public interface DifferentiableExpressionBuilder<T, E, A : ExpressionAlgebra<T, E>>: ExpressionBuilder<T,E,A> {
 //    public override fun expression(block: A.() -> E): DifferentiableExpression<T>
 //}
+
+public abstract class FirstDerivativeExpression<T> : DifferentiableExpression<T> {
+
+    public abstract fun derivativeOrNull(symbol: Symbol): Expression<T>?
+
+    public override fun derivativeOrNull(orders: Map<Symbol, Int>): Expression<T>? {
+        val dSymbol = orders.entries.singleOrNull { it.value == 1 }?.key ?: return null
+        return derivativeOrNull(dSymbol)
+    }
+}

kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/SimpleAutoDiff.kt

@@ -221,23 +221,16 @@ private class AutoDiffContext<T : Any, F : Field<T>>(
 public class SimpleAutoDiffExpression<T : Any, F : Field<T>>(
     public val field: F,
     public val function: AutoDiffField<T, F>.() -> AutoDiffValue<T>,
-) : DifferentiableExpression<T> {
+) : FirstDerivativeExpression<T>() {
     public override operator fun invoke(arguments: Map<Symbol, T>): T {
         //val bindings = arguments.entries.map { it.key.bind(it.value) }
         return AutoDiffContext(field, arguments).function().value
     }
 
-    /**
+    override fun derivativeOrNull(symbol: Symbol): Expression<T> = Expression { arguments ->
-     * Get the derivative expression with given orders
+        //val bindings = arguments.entries.map { it.key.bind(it.value) }
-     */
+        val derivationResult = AutoDiffContext(field, arguments).derivate(function)
-    public override fun derivative(orders: Map<Symbol, Int>): Expression<T> {
+        derivationResult.derivative(symbol)
-        val dSymbol = orders.entries.singleOrNull { it.value == 1 }
-            ?: error("SimpleAutoDiff supports only first order derivatives")
-        return Expression { arguments ->
-            //val bindings = arguments.entries.map { it.key.bind(it.value) }
-            val derivationResult = AutoDiffContext(field, arguments).derivate(function)
-            derivationResult.derivative(dSymbol.key)
-        }
     }
 }
 

kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/SymbolIndexer.kt

@@ -1,7 +1,12 @@
 package kscience.kmath.expressions
 
+import kscience.kmath.linear.Point
+import kscience.kmath.structures.BufferFactory
+import kscience.kmath.structures.Structure2D
+
 /**
  * An environment to easy transform indexed variables to symbols and back.
+ * TODO requires multi-receivers to be beutiful
  */
 public interface SymbolIndexer {
     public val symbols: List<Symbol>
@@ -22,15 +27,26 @@ public interface SymbolIndexer {
         return get(this@SymbolIndexer.indexOf(symbol))
     }
 
+    public operator fun <T> Point<T>.get(symbol: Symbol): T {
+        require(size == symbols.size) { "The input buffer size for indexer should be ${symbols.size} but $size found" }
+        return get(this@SymbolIndexer.indexOf(symbol))
+    }
+
     public fun DoubleArray.toMap(): Map<Symbol, Double> {
         require(size == symbols.size) { "The input array size for indexer should be ${symbols.size} but $size found" }
         return symbols.indices.associate { symbols[it] to get(it) }
     }
 
+    public operator fun <T> Structure2D<T>.get(rowSymbol: Symbol, columnSymbol: Symbol): T =
+        get(indexOf(rowSymbol), indexOf(columnSymbol))
+
 
     public fun <T> Map<Symbol, T>.toList(): List<T> = symbols.map { getValue(it) }
 
-    public fun Map<Symbol, Double>.toArray(): DoubleArray = DoubleArray(symbols.size) { getValue(symbols[it]) }
+    public fun <T> Map<Symbol, T>.toPoint(bufferFactory: BufferFactory<T>): Point<T> =
+        bufferFactory(symbols.size) { getValue(symbols[it]) }
+
+    public fun Map<Symbol, Double>.toDoubleArray(): DoubleArray = DoubleArray(symbols.size) { getValue(symbols[it]) }
 }
 
 public inline class SimpleSymbolIndexer(override val symbols: List<Symbol>) : SymbolIndexer