add DeltaFnTol option

main.go

@@ -51,6 +51,7 @@ type opt_config struct {
 	LR_sigma    float64
 	Momentum    float64
 	SigmaTol    float64
+	DeltaFnTol  float64
 	Verbose     bool
 	Seed        uint64
 }
@@ -63,6 +64,7 @@ func Config() opt_config {
 	cfg.LR_sigma = 0.15
 	cfg.Momentum = 0.75
 	cfg.SigmaTol = 1e-14
+	cfg.DeltaFnTol = 1e-14
 	cfg.Verbose = false
 	cfg.Seed = 798371291237
 	return cfg
@@ -149,7 +151,10 @@ func Opt(fn func([]float64) float64, mu []float64, sigma []float64, cfg opt_conf
 			mu[j] += v[j]
 			sigma[j] *= math.Exp(cfg.LR_sigma * g_log_sigma[j])
 		}
-		if slices.Max(sigma) < cfg.SigmaTol {
+		if slices.Max(sigma) <= cfg.SigmaTol || math.Abs(pop[0].C-pop[pop_n-1].C) <= cfg.DeltaFnTol {
+			if cfg.Verbose {
+				log.Println("END OPT: Convergence reached.")
+			}
 			break
 		}
 		if cfg.Verbose {

main_test.go

@@ -78,11 +78,16 @@ func TestVerbose(t *testing.T) {
 	log.SetOutput(buf)
 	cfg.Verbose = true
 	cfg.Generations = 10
+	//Run it for few generations with a cost function to test for iteration verbosity
+	Opt(func(f []float64) float64 {
+		return Probability(f[0])
+	}, []float64{0.0, 0.0}, []float64{1.0, 1.0}, cfg)
+	//run it with a constant cost to hit convergence verbosity
 	Opt(func(f []float64) float64 {
 		return 0.0
 	}, []float64{0.0, 0.0}, []float64{1.0, 1.0}, cfg)
 	str := buf.String()
-	if strings.Count(str, "GoES:") != cfg.Generations {
+	if strings.Count(str, "GoES:") != cfg.Generations || strings.Count(str, "END OPT:") != 1 {
 		t.Error(str)
 	}
 }

readme.md

@@ -22,7 +22,8 @@ The **`Config()` function:** returns a struct that allows fine-tuning the optimi
 * `LR_mu`: Learning rate for mean vector update (default: 0.6).
 * `LR_sigma`: Learning rate for standard deviation vector update (default: 0.15).
 * `Momentum`: Momentum coefficient for velocity update (default: 0.93).
-* `SigmaTol`: Tolerance threshold for stopping the optimization (default: 1e-12).
+* `SigmaTol`: Tolerance threshold on parameter variance for stopping the optimization (default: 1e-14).
+* `DeltaFnTol`: Tolerance threshold on cost function variance for stopping the optimization (default: 1e-14).
 * `Seed`: A uint64 value to seed the optimiser rng.
 * `Verbose`: Flag to enable detailed logging of optimization progress during each generation (default: false).