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

@@ -36,7 +36,7 @@ public class CMOptimizationProblem(
         addOptimizationData(InitialGuess(map.toDoubleArray()))
     }
 
-    public fun expression(expression: Expression<Double>): Unit {
+    public override fun expression(expression: Expression<Double>): Unit {
         val objectiveFunction = ObjectiveFunction {
             val args = it.toMap()
             expression(args)
@@ -44,7 +44,7 @@ public class CMOptimizationProblem(
         addOptimizationData(objectiveFunction)
     }
 
-    public fun derivatives(expression: DifferentiableExpression<Double>): Unit {
+    public override fun diffExpression(expression: DifferentiableExpression<Double>): Unit {
         expression(expression)
         val gradientFunction = ObjectiveFunctionGradient {
             val args = it.toMap()
@@ -83,6 +83,10 @@ public class CMOptimizationProblem(
         optimizatorBuilder = block
     }
 
+    override fun update(result: OptimizationResult<Double>) {
+        initialGuess(result.point)
+    }
+
     override fun optimize(): OptimizationResult<Double> {
         val optimizer = optimizatorBuilder?.invoke() ?: error("Optimizer not defined")
         val (point, value) = optimizer.optimize(*optimizationData.values.toTypedArray())

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

@@ -1,17 +1,32 @@
 package kscience.kmath.commons.optimization
 
+import kscience.kmath.expressions.DifferentiableExpression
+import kscience.kmath.expressions.Expression
 import kscience.kmath.expressions.Symbol
-import kotlin.reflect.KClass
 
-public typealias ParameterSpacePoint<T> = Map<Symbol, T>
+public interface OptimizationResultFeature
+
 
 public class OptimizationResult<T>(
-    public val point: ParameterSpacePoint<T>,
+    public val point: Map<Symbol, T>,
     public val value: T,
-    public val extra: Map<KClass<*>, Any> = emptyMap()
+    public val features: Set<OptimizationResultFeature> = emptySet(),
 )
 
+/**
+ * A configuration builder for optimization problem
+ */
 public interface OptimizationProblem<T : Any> {
+    /**
+     * Set an objective function expression
+     */
+    public fun expression(expression: Expression<Double>): Unit
+
+    /**
+     *
+     */
+    public fun diffExpression(expression: DifferentiableExpression<Double>): Unit
+    public fun update(result: OptimizationResult<T>)
     public fun optimize(): OptimizationResult<T>
 }
 

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

@@ -13,7 +13,7 @@ public fun Expression<Double>.optimize(
     configuration: CMOptimizationProblem.() -> Unit,
 ): OptimizationResult<Double> {
     require(symbols.isNotEmpty()) { "Must provide a list of symbols for optimization" }
-    val problem = CMOptimizationProblem(symbols.toList()).apply(configuration).apply(configuration)
+    val problem = CMOptimizationProblem(symbols.toList()).apply(configuration)
     problem.expression(this)
     return problem.optimize()
 }
@@ -26,7 +26,7 @@ public fun DifferentiableExpression<Double>.optimize(
     configuration: CMOptimizationProblem.() -> Unit,
 ): OptimizationResult<Double> {
     require(symbols.isNotEmpty()) { "Must provide a list of symbols for optimization" }
-    val problem = CMOptimizationProblem(symbols.toList()).apply(configuration).apply(configuration)
-    problem.derivatives(this)
+    val problem = CMOptimizationProblem(symbols.toList()).apply(configuration)
+    problem.diffExpression(this)
     return problem.optimize()
 }

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

@@ -9,16 +9,14 @@ internal class OptimizeTest {
     val y by symbol
 
     val normal = DerivativeStructureExpression {
-        val x = bind(x)
-        val y = bind(y)
-        exp(-x.pow(2) / 2) + exp(-y.pow(2) / 2)
+        exp(-bind(x).pow(2) / 2) + exp(- bind(y).pow(2) / 2)
     }
 
     @Test
     fun testOptimization() {
         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
+            //no need to select optimizer. Gradient optimizer is used by default because gradients are provided by function
         }
         println(result.point)
         println(result.value)