// Academic benchmarking suite for research validation
//
// This module provides standardized benchmarks and evaluation protocols
// for comparing optimization algorithms in academic research contexts.

#[allow(unused_imports)]
use crate::error::Result;
use crate::optimizers::*;
use crate::research::experiments::{Experiment, ExperimentResult};
use crate::unified_api::OptimizerConfig;
use chrono::{DateTime, Utc};
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::numeric::Float;
use scirs2_core::random::Rng;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Academic benchmark suite
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AcademicBenchmarkSuite {
    /// Suite identifier
    pub id: String,
    /// Suite name
    pub name: String,
    /// Suite description
    pub description: String,
    /// Benchmark problems
    pub benchmarks: Vec<BenchmarkProblem>,
    /// Evaluation metrics
    pub metrics: Vec<EvaluationMetric>,
    /// Reference results
    pub reference_results: HashMap<String, BenchmarkResults>,
    /// Suite metadata
    pub metadata: BenchmarkSuiteMetadata,
    /// Creation timestamp
    pub created_at: DateTime<Utc>,
}

/// Individual benchmark problem
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkProblem {
    /// Problem identifier
    pub id: String,
    /// Problem name
    pub name: String,
    /// Problem description
    pub description: String,
    /// Problem category
    pub category: ProblemCategory,
    /// Problem difficulty
    pub difficulty: DifficultyLevel,
    /// Problem dimensions
    pub dimensions: Vec<usize>,
    /// Objective function
    pub objective_function: ObjectiveFunction,
    /// Problem constraints
    pub constraints: Vec<Constraint>,
    /// Known optimal solution
    pub optimal_solution: Option<OptimalSolution>,
    /// Problem parameters
    pub parameters: HashMap<String, f64>,
    /// Literature references
    pub references: Vec<String>,
}

/// Problem categories
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum ProblemCategory {
    /// Convex optimization
    Convex,
    /// Non-convex optimization
    NonConvex,
    /// Machine learning
    MachineLearning,
    /// Deep learning
    DeepLearning,
    /// Reinforcement learning
    ReinforcementLearning,
    /// Computer vision
    ComputerVision,
    /// Natural language processing
    NaturalLanguageProcessing,
    /// Numerical optimization
    NumericalOptimization,
    /// Constrained optimization
    ConstrainedOptimization,
    /// Multi-objective optimization
    MultiObjective,
    /// Stochastic optimization
    Stochastic,
    /// Discrete optimization
    Discrete,
    /// Continuous optimization
    Continuous,
    /// Mixed optimization
    Mixed,
}

/// Difficulty levels
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, PartialOrd, Ord)]
pub enum DifficultyLevel {
    /// Easy problems
    Easy,
    /// Medium problems
    Medium,
    /// Hard problems
    Hard,
    /// Very hard problems
    VeryHard,
    /// Extreme problems
    Extreme,
}

/// Objective function definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ObjectiveFunction {
    /// Function name
    pub name: String,
    /// Function type
    pub function_type: FunctionType,
    /// Function properties
    pub properties: FunctionProperties,
    /// Mathematical description
    pub mathematical_form: String,
    /// Implementation notes
    pub implementation_notes: String,
}

/// Function types
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum FunctionType {
    /// Quadratic function
    Quadratic,
    /// Rosenbrock function
    Rosenbrock,
    /// Sphere function
    Sphere,
    /// Rastrigin function
    Rastrigin,
    /// Ackley function
    Ackley,
    /// Griewank function
    Griewank,
    /// Schwefel function
    Schwefel,
    /// Himmelblau function
    Himmelblau,
    /// Booth function
    Booth,
    /// Beale function
    Beale,
    /// Three-hump camel function
    ThreeHumpCamel,
    /// Six-hump camel function
    SixHumpCamel,
    /// Cross-in-tray function
    CrossInTray,
    /// Egg holder function
    EggHolder,
    /// Holder table function
    HolderTable,
    /// McCormick function
    McCormick,
    /// Schaffer function N2
    SchafferN2,
    /// Schaffer function N4
    SchafferN4,
    /// StyblinskiTang function
    StyblinskiTang,
    /// Custom function
    Custom(String),
}

/// Function properties
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FunctionProperties {
    /// Is the function differentiable
    pub differentiable: bool,
    /// Is the function continuous
    pub continuous: bool,
    /// Is the function convex
    pub convex: bool,
    /// Is the function separable
    pub separable: bool,
    /// Is the function multimodal
    pub multimodal: bool,
    /// Function smoothness
    pub smoothness: SmoothnesLevel,
    /// Condition number
    pub condition_number: Option<f64>,
    /// Lipschitz constant
    pub lipschitz_constant: Option<f64>,
}

/// Smoothness levels
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum SmoothnesLevel {
    /// Very smooth
    VerySmooth,
    /// Smooth
    Smooth,
    /// Moderately smooth
    ModeratelySmooth,
    /// Rough
    Rough,
    /// Very rough
    VeryRough,
}

/// Optimization constraint
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Constraint {
    /// Constraint type
    pub constraint_type: ConstraintType,
    /// Constraint description
    pub description: String,
    /// Mathematical form
    pub mathematical_form: String,
    /// Constraint parameters
    pub parameters: HashMap<String, f64>,
}

/// Constraint types
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum ConstraintType {
    /// Equality constraint
    Equality,
    /// Inequality constraint
    Inequality,
    /// Box constraint (bounds)
    Box,
    /// Linear constraint
    Linear,
    /// Nonlinear constraint
    Nonlinear,
    /// Integer constraint
    Integer,
    /// Binary constraint
    Binary,
}

/// Known optimal solution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimalSolution {
    /// Optimal parameter values
    pub parameters: Array1<f64>,
    /// Optimal objective value
    pub objective_value: f64,
    /// Solution properties
    pub properties: SolutionProperties,
    /// Literature reference
    pub reference: Option<String>,
}

/// Solution properties
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SolutionProperties {
    /// Is this a global optimum
    pub global_optimum: bool,
    /// Is this a local optimum
    pub local_optimum: bool,
    /// Solution uniqueness
    pub unique: bool,
    /// Solution stability
    pub stable: bool,
}

/// Evaluation metric for benchmarks
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EvaluationMetric {
    /// Metric name
    pub name: String,
    /// Metric description
    pub description: String,
    /// Metric type
    pub metric_type: MetricType,
    /// Aggregation method
    pub aggregation: AggregationMethod,
    /// Better direction (higher or lower is better)
    pub better_direction: BetterDirection,
    /// Metric weight in overall score
    pub weight: f64,
}

/// Metric types
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum MetricType {
    /// Objective value at convergence
    FinalObjective,
    /// Number of iterations to convergence
    IterationsToConvergence,
    /// Time to convergence
    TimeToConvergence,
    /// Function evaluations to convergence
    FunctionEvaluations,
    /// Gradient evaluations
    GradientEvaluations,
    /// Success rate (percentage of successful runs)
    SuccessRate,
    /// Solution quality
    SolutionQuality,
    /// Convergence rate
    ConvergenceRate,
    /// Robustness measure
    Robustness,
    /// Memory usage
    MemoryUsage,
    /// Energy consumption
    EnergyConsumption,
    /// Custom metric
    Custom(String),
}

/// Aggregation methods for multiple runs
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum AggregationMethod {
    /// Mean value
    Mean,
    /// Median value
    Median,
    /// Best value
    Best,
    /// Worst value
    Worst,
    /// Standard deviation
    StandardDeviation,
    /// Percentile (specify which percentile)
    Percentile(u8),
    /// Success count
    SuccessCount,
    /// Custom aggregation
    Custom(String),
}

/// Better direction for metrics
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum BetterDirection {
    /// Higher values are better
    Higher,
    /// Lower values are better
    Lower,
}

/// Benchmark results for a specific optimizer
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkResults {
    /// Optimizer name
    pub optimizer_name: String,
    /// Results per problem
    pub problem_results: HashMap<String, ProblemResults>,
    /// Overall scores
    pub overall_scores: HashMap<String, f64>,
    /// Statistical significance tests
    pub statistical_tests: Vec<StatisticalTest>,
    /// Performance ranking
    pub ranking: OptimizerRanking,
    /// Execution timestamp
    pub executed_at: DateTime<Utc>,
}

/// Results for a single problem
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProblemResults {
    /// Problem identifier
    pub problem_id: String,
    /// Individual run results
    pub run_results: Vec<RunResult>,
    /// Aggregated metrics
    pub aggregated_metrics: HashMap<String, f64>,
    /// Statistical summaries
    pub statistics: ResultStatistics,
    /// Convergence analysis
    pub convergence_analysis: ConvergenceAnalysis,
}

/// Result for a single run
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RunResult {
    /// Run identifier
    pub run_id: String,
    /// Random seed used
    pub random_seed: u64,
    /// Final objective value
    pub final_objective: f64,
    /// Convergence achieved
    pub converged: bool,
    /// Number of iterations
    pub iterations: usize,
    /// Execution time (seconds)
    pub execution_time: f64,
    /// Function evaluations
    pub function_evaluations: usize,
    /// Gradient evaluations
    pub gradient_evaluations: usize,
    /// Memory usage (bytes)
    pub memory_usage: usize,
    /// Convergence trajectory
    pub trajectory: Vec<f64>,
    /// Error information (if failed)
    pub error_info: Option<String>,
}

/// Statistical summaries
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResultStatistics {
    /// Number of successful runs
    pub successful_runs: usize,
    /// Total number of runs
    pub total_runs: usize,
    /// Success rate
    pub success_rate: f64,
    /// Mean objective value
    pub mean_objective: f64,
    /// Standard deviation of objective values
    pub std_objective: f64,
    /// Best objective value
    pub best_objective: f64,
    /// Worst objective value
    pub worst_objective: f64,
    /// Median objective value
    pub median_objective: f64,
    /// Quartiles
    pub quartiles: (f64, f64, f64), // Q1, Q2, Q3
    /// Confidence intervals
    pub confidence_intervals: HashMap<String, (f64, f64)>,
}

/// Convergence analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConvergenceAnalysis {
    /// Average convergence rate
    pub avg_convergence_rate: f64,
    /// Convergence stability
    pub convergence_stability: f64,
    /// Early convergence indicator
    pub early_convergence: bool,
    /// Plateau detection
    pub plateau_detected: bool,
    /// Convergence pattern
    pub convergence_pattern: ConvergencePattern,
}

/// Convergence patterns
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum ConvergencePattern {
    /// Monotonic decrease
    MonotonicDecrease,
    /// Exponential decay
    ExponentialDecay,
    /// Linear decrease
    LinearDecrease,
    /// Oscillatory convergence
    Oscillatory,
    /// Stepwise convergence
    Stepwise,
    /// Plateau then drop
    PlateauThenDrop,
    /// No clear pattern
    Irregular,
}

/// Statistical significance test
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticalTest {
    /// Test name
    pub test_name: String,
    /// Compared optimizers
    pub optimizers: Vec<String>,
    /// Test statistic
    pub test_statistic: f64,
    /// P-value
    pub p_value: f64,
    /// Significance level
    pub significance_level: f64,
    /// Test result
    pub significant: bool,
    /// Effect size
    pub effect_size: Option<f64>,
}

/// Optimizer ranking
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizerRanking {
    /// Overall rank (1 is best)
    pub overall_rank: usize,
    /// Ranks per category
    pub category_ranks: HashMap<String, usize>,
    /// Ranks per metric
    pub metric_ranks: HashMap<String, usize>,
    /// Ranking score
    pub ranking_score: f64,
    /// Ranking method used
    pub ranking_method: RankingMethod,
}

/// Ranking methods
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum RankingMethod {
    /// Average rank across all metrics
    AverageRank,
    /// Weighted score
    WeightedScore,
    /// Pareto dominance
    ParetoDominance,
    /// Win-loss-tie
    WinLossTie,
    /// Tournament ranking
    Tournament,
    /// Custom ranking method
    Custom(String),
}

/// Benchmark suite metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkSuiteMetadata {
    /// Suite version
    pub version: String,
    /// Suite authors
    pub authors: Vec<String>,
    /// Suite license
    pub license: String,
    /// Literature references
    pub references: Vec<String>,
    /// Target audience
    pub target_audience: Vec<String>,
    /// Keywords
    pub keywords: Vec<String>,
    /// Changelog
    pub changelog: Vec<ChangelogEntry>,
}

/// Changelog entry
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChangelogEntry {
    /// Version number
    pub version: String,
    /// Release date
    pub date: DateTime<Utc>,
    /// Changes description
    pub changes: String,
    /// Author of changes
    pub author: String,
}

/// Benchmark runner for executing benchmark suites
pub struct BenchmarkRunner {
    /// Benchmark suite
    suite: AcademicBenchmarkSuite,
    /// Execution settings
    settings: BenchmarkSettings,
    /// Progress callback
    progress_callback: Option<Box<dyn Fn(f64) + Send + Sync>>,
}

impl std::fmt::Debug for BenchmarkRunner {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("BenchmarkRunner")
            .field("suite", &self.suite)
            .field("settings", &self.settings)
            .field("progress_callback", &self.progress_callback.is_some())
            .finish()
    }
}

/// Benchmark execution settings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkSettings {
    /// Number of independent runs per problem
    pub num_runs: usize,
    /// Random seeds to use
    pub random_seeds: Vec<u64>,
    /// Maximum iterations per run
    pub max_iterations: usize,
    /// Maximum execution time per run (seconds)
    pub max_time_seconds: f64,
    /// Convergence tolerance
    pub convergence_tolerance: f64,
    /// Enable parallel execution
    pub parallel_execution: bool,
    /// Number of parallel threads
    pub num_threads: Option<usize>,
    /// Save detailed results
    pub save_detailed_results: bool,
    /// Output directory
    pub output_directory: Option<String>,
}

impl AcademicBenchmarkSuite {
    /// Create a new benchmark suite
    pub fn new(name: &str) -> Self {
        Self {
            id: uuid::Uuid::new_v4().to_string(),
            name: name.to_string(),
            description: String::new(),
            benchmarks: Vec::new(),
            metrics: Vec::new(),
            reference_results: HashMap::new(),
            metadata: BenchmarkSuiteMetadata::default(),
            created_at: Utc::now(),
        }
    }

    /// Add a benchmark problem
    pub fn add_benchmark(&mut self, benchmark: BenchmarkProblem) {
        self.benchmarks.push(benchmark);
    }

    /// Add an evaluation metric
    pub fn add_metric(&mut self, metric: EvaluationMetric) {
        self.metrics.push(metric);
    }

    /// Create standard ML optimization benchmark suite
    pub fn standard_ml_suite() -> Self {
        let mut suite = Self::new("Standard ML Optimization Benchmark");
        suite.description =
            "Standard benchmark suite for machine learning optimization algorithms".to_string();

        // Add standard problems
        suite.add_benchmark(Self::create_quadratic_problem());
        suite.add_benchmark(Self::create_rosenbrock_problem());
        suite.add_benchmark(Self::create_logistic_regression_problem());
        suite.add_benchmark(Self::create_neural_network_problem());

        // Add standard metrics
        suite.add_metric(Self::create_final_objective_metric());
        suite.add_metric(Self::create_convergence_time_metric());
        suite.add_metric(Self::create_success_rate_metric());

        suite
    }

    fn create_quadratic_problem() -> BenchmarkProblem {
        BenchmarkProblem {
            id: "quadratic_10d".to_string(),
            name: "10D Quadratic Function".to_string(),
            description: "Simple quadratic function in 10 dimensions".to_string(),
            category: ProblemCategory::Convex,
            difficulty: DifficultyLevel::Easy,
            dimensions: vec![10],
            objective_function: ObjectiveFunction {
                name: "Quadratic".to_string(),
                function_type: FunctionType::Quadratic,
                properties: FunctionProperties {
                    differentiable: true,
                    continuous: true,
                    convex: true,
                    separable: true,
                    multimodal: false,
                    smoothness: SmoothnesLevel::VerySmooth,
                    condition_number: Some(1.0),
                    lipschitz_constant: Some(2.0),
                },
                mathematical_form: "f(x) = 0.5 * x^T * x".to_string(),
                implementation_notes: "Simple quadratic function with unit matrix".to_string(),
            },
            constraints: Vec::new(),
            optimal_solution: Some(OptimalSolution {
                parameters: Array1::zeros(10),
                objective_value: 0.0,
                properties: SolutionProperties {
                    global_optimum: true,
                    local_optimum: true,
                    unique: true,
                    stable: true,
                },
                reference: None,
            }),
            parameters: HashMap::new(),
            references: vec!["Standard optimization textbooks".to_string()],
        }
    }

    fn create_rosenbrock_problem() -> BenchmarkProblem {
        BenchmarkProblem {
            id: "rosenbrock_10d".to_string(),
            name: "10D Rosenbrock Function".to_string(),
            description: "Rosenbrock function in 10 dimensions".to_string(),
            category: ProblemCategory::NonConvex,
            difficulty: DifficultyLevel::Medium,
            dimensions: vec![10],
            objective_function: ObjectiveFunction {
                name: "Rosenbrock".to_string(),
                function_type: FunctionType::Rosenbrock,
                properties: FunctionProperties {
                    differentiable: true,
                    continuous: true,
                    convex: false,
                    separable: false,
                    multimodal: false,
                    smoothness: SmoothnesLevel::Smooth,
                    condition_number: None,
                    lipschitz_constant: None},
                mathematical_form: "f(x) = sum(100*(x[i+1] - x[i]^2)^2 + (1 - x[i])^2)".to_string(),
                implementation_notes: "Classic Rosenbrock function, challenging for optimization".to_string()},
            constraints: Vec::new(),
            optimal_solution: Some(OptimalSolution {
                parameters: Array1::ones(10),
                objective_value: 0.0,
                properties: SolutionProperties {
                    global_optimum: true,
                    local_optimum: true,
                    unique: true,
                    stable: true},
                reference: Some("Rosenbrock, H.H. (1960)".to_string())}),
            parameters: HashMap::new(),
            references: vec!["Rosenbrock, H.H. (1960). An automatic method for finding the greatest or least value of a function.".to_string()]}
    }

    fn create_logistic_regression_problem() -> BenchmarkProblem {
        BenchmarkProblem {
            id: "logistic_regression_100d".to_string(),
            name: "Logistic Regression (100D)".to_string(),
            description: "Logistic regression on synthetic dataset".to_string(),
            category: ProblemCategory::MachineLearning,
            difficulty: DifficultyLevel::Medium,
            dimensions: vec![100],
            objective_function: ObjectiveFunction {
                name: "Logistic Loss".to_string(),
                function_type: FunctionType::Custom("LogisticLoss".to_string()),
                properties: FunctionProperties {
                    differentiable: true,
                    continuous: true,
                    convex: true,
                    separable: false,
                    multimodal: false,
                    smoothness: SmoothnesLevel::Smooth,
                    condition_number: None,
                    lipschitz_constant: None,
                },
                mathematical_form: "f(w) = mean(log(1 + exp(-y * X * w))) + lambda * ||w||^2"
                    .to_string(),
                implementation_notes: "Binary classification with L2 regularization".to_string(),
            },
            constraints: Vec::new(),
            optimal_solution: None, // Depends on dataset
            parameters: {
                let mut params = HashMap::new();
                params.insert("lambda".to_string(), 0.01);
                params.insert("num_samples".to_string(), 1000.0);
                params
            },
            references: vec!["Standard machine learning references".to_string()],
        }
    }

    fn create_neural_network_problem() -> BenchmarkProblem {
        BenchmarkProblem {
            id: "neural_network_mnist".to_string(),
            name: "Neural Network MNIST".to_string(),
            description: "Two-layer neural network on MNIST subset".to_string(),
            category: ProblemCategory::DeepLearning,
            difficulty: DifficultyLevel::Hard,
            dimensions: vec![784, 128, 10], // Input, hidden, output
            objective_function: ObjectiveFunction {
                name: "Cross-entropy Loss".to_string(),
                function_type: FunctionType::Custom("CrossEntropyLoss".to_string()),
                properties: FunctionProperties {
                    differentiable: true,
                    continuous: true,
                    convex: false,
                    separable: false,
                    multimodal: true,
                    smoothness: SmoothnesLevel::Smooth,
                    condition_number: None,
                    lipschitz_constant: None,
                },
                mathematical_form: "f(θ) = mean(-log(softmax(NN(x; θ))[y]))".to_string(),
                implementation_notes: "Two-layer ReLU network with softmax output".to_string(),
            },
            constraints: Vec::new(),
            optimal_solution: None, // Unknown for neural networks
            parameters: {
                let mut params = HashMap::new();
                params.insert("num_samples".to_string(), 10000.0);
                params.insert("batch_size".to_string(), 64.0);
                params
            },
            references: vec![
                "LeCun et al. (1998). Gradient-based learning applied to document recognition."
                    .to_string(),
            ],
        }
    }

    fn create_final_objective_metric() -> EvaluationMetric {
        EvaluationMetric {
            name: "Final Objective Value".to_string(),
            description: "Final objective function value achieved".to_string(),
            metric_type: MetricType::FinalObjective,
            aggregation: AggregationMethod::Mean,
            better_direction: BetterDirection::Lower,
            weight: 1.0,
        }
    }

    fn create_convergence_time_metric() -> EvaluationMetric {
        EvaluationMetric {
            name: "Time to Convergence".to_string(),
            description: "Time required to reach convergence tolerance".to_string(),
            metric_type: MetricType::TimeToConvergence,
            aggregation: AggregationMethod::Median,
            better_direction: BetterDirection::Lower,
            weight: 0.5,
        }
    }

    fn create_success_rate_metric() -> EvaluationMetric {
        EvaluationMetric {
            name: "Success Rate".to_string(),
            description: "Percentage of runs that converged successfully".to_string(),
            metric_type: MetricType::SuccessRate,
            aggregation: AggregationMethod::Mean,
            better_direction: BetterDirection::Higher,
            weight: 0.8,
        }
    }
}

impl BenchmarkRunner {
    /// Create a new benchmark runner
    pub fn new(suite: AcademicBenchmarkSuite, settings: BenchmarkSettings) -> Self {
        Self {
            suite,
            settings,
            progress_callback: None,
        }
    }

    /// Set progress callback
    pub fn set_progress_callback<F>(&mut self, callback: F)
    where
        F: Fn(f64) + Send + Sync + 'static,
    {
        self.progress_callback = Some(Box::new(callback));
    }

    /// Run benchmark suite on multiple optimizers
    pub fn run_benchmarks<
        A: Float + std::fmt::Debug + Send + Sync + scirs2_core::ndarray::ScalarOperand + 'static,
    >(
        &self,
        optimizers: &[(&str, OptimizerConfig<A>)],
    ) -> Result<HashMap<String, BenchmarkResults>> {
        let mut all_results = HashMap::new();

        let total_work = optimizers.len() * self.suite.benchmarks.len() * self.settings.num_runs;
        let mut completed_work = 0;

        for (optimizer_name, optimizer_config) in optimizers {
            let mut optimizer_results = BenchmarkResults {
                optimizer_name: optimizer_name.to_string(),
                problem_results: HashMap::new(),
                overall_scores: HashMap::new(),
                statistical_tests: Vec::new(),
                ranking: OptimizerRanking {
                    overall_rank: 0,
                    category_ranks: HashMap::new(),
                    metric_ranks: HashMap::new(),
                    ranking_score: 0.0,
                    ranking_method: RankingMethod::WeightedScore,
                },
                executed_at: Utc::now(),
            };

            for benchmark in &self.suite.benchmarks {
                let problem_results = self.run_single_problem::<A>(benchmark, optimizer_config)?;
                optimizer_results
                    .problem_results
                    .insert(benchmark.id.clone(), problem_results);

                completed_work += self.settings.num_runs;
                if let Some(ref callback) = self.progress_callback {
                    callback(completed_work as f64 / total_work as f64);
                }
            }

            // Calculate overall scores
            self.calculate_overall_scores(&mut optimizer_results);

            all_results.insert(optimizer_name.to_string(), optimizer_results);
        }

        // Calculate rankings and statistical tests
        self.calculate_rankings_and_tests(&mut all_results);

        Ok(all_results)
    }

    fn run_single_problem<
        A: Float + std::fmt::Debug + Send + Sync + scirs2_core::ndarray::ScalarOperand + 'static,
    >(
        &self,
        benchmark: &BenchmarkProblem,
        optimizer_config: &OptimizerConfig<A>,
    ) -> Result<ProblemResults> {
        let mut run_results = Vec::new();

        for run_idx in 0..self.settings.num_runs {
            let seed = if run_idx < self.settings.random_seeds.len() {
                self.settings.random_seeds[run_idx]
            } else {
                42 + run_idx as u64
            };

            let run_result = self.run_single_instance::<A>(benchmark, optimizer_config, seed)?;
            run_results.push(run_result);
        }

        // Calculate aggregated metrics and statistics
        let aggregated_metrics = self.calculate_aggregated_metrics(&run_results);
        let statistics = self.calculate_statistics(&run_results);
        let convergence_analysis = self.analyze_convergence(&run_results);

        Ok(ProblemResults {
            problem_id: benchmark.id.clone(),
            run_results,
            aggregated_metrics,
            statistics,
            convergence_analysis,
        })
    }

    fn run_single_instance<
        A: Float + std::fmt::Debug + Send + Sync + scirs2_core::ndarray::ScalarOperand + 'static,
    >(
        &self,
        benchmark: &BenchmarkProblem,
        optimizer_config: &OptimizerConfig<A>,
        seed: u64,
    ) -> Result<RunResult> {
        // This is a simplified implementation
        // In practice, you'd implement the actual optimization problems

        let run_id = uuid::Uuid::new_v4().to_string();
        let start_time = std::time::Instant::now();

        // Simulate optimization run
        let final_objective = match benchmark.objective_function.function_type {
            FunctionType::Quadratic => self.simulate_quadratic_optimization(seed),
            FunctionType::Rosenbrock => self.simulate_rosenbrock_optimization(seed),
            _ => self.simulate_generic_optimization(seed),
        };

        let execution_time = start_time.elapsed().as_secs_f64();
        let iterations = std::cmp::min(1000, self.settings.max_iterations);
        let converged = final_objective < self.settings.convergence_tolerance;

        // Generate synthetic trajectory
        let trajectory = self.generate_synthetic_trajectory(final_objective, iterations);

        Ok(RunResult {
            run_id,
            random_seed: seed,
            final_objective,
            converged,
            iterations,
            execution_time,
            function_evaluations: iterations,
            gradient_evaluations: iterations,
            memory_usage: 1024 * 1024, // 1MB default
            trajectory,
            error_info: None,
        })
    }

    fn simulate_quadratic_optimization(&self, seed: u64) -> f64 {
        use scirs2_core::random::{Random, Rng};

        let mut rng = Random::default();
        rng.gen_range(1e-8..1e-4) // Simulate good convergence for quadratic
    }

    fn simulate_rosenbrock_optimization(&self, seed: u64) -> f64 {
        use scirs2_core::random::{Random, Rng};

        let mut rng = Random::default();
        rng.gen_range(1e-6..1e-2) // Simulate moderate convergence for Rosenbrock
    }

    fn simulate_generic_optimization(&self, seed: u64) -> f64 {
        use scirs2_core::random::{Random, Rng};

        let mut rng = Random::default();
        rng.gen_range(1e-5..1e-1) // Generic optimization results
    }

    fn generate_synthetic_trajectory(&self, final_value: f64, iterations: usize) -> Vec<f64> {
        let mut trajectory = Vec::with_capacity(iterations);
        let initial_value = final_value * 1000.0; // Start 1000x higher

        for i in 0..iterations {
            let progress = i as f64 / iterations as f64;
            let _value = initial_value * (1.0 - progress).powi(2) + final_value * progress;
            trajectory.push(_value);
        }

        trajectory
    }

    fn calculate_aggregated_metrics(&self, run_results: &[RunResult]) -> HashMap<String, f64> {
        let mut metrics = HashMap::new();

        if !run_results.is_empty() {
            // Final objective metrics
            let final_objectives: Vec<f64> =
                run_results.iter().map(|r| r.final_objective).collect();
            metrics.insert(
                "mean_final_objective".to_string(),
                final_objectives.iter().sum::<f64>() / final_objectives.len() as f64,
            );

            let mut sorted_objectives = final_objectives.clone();
            sorted_objectives.sort_by(|a, b| a.partial_cmp(b).unwrap());
            metrics.insert(
                "median_final_objective".to_string(),
                sorted_objectives[sorted_objectives.len() / 2],
            );
            metrics.insert("best_final_objective".to_string(), sorted_objectives[0]);

            // Time metrics
            let execution_times: Vec<f64> = run_results.iter().map(|r| r.execution_time).collect();
            metrics.insert(
                "mean_execution_time".to_string(),
                execution_times.iter().sum::<f64>() / execution_times.len() as f64,
            );

            // Success rate
            let successful_runs = run_results.iter().filter(|r| r.converged).count();
            metrics.insert(
                "success_rate".to_string(),
                successful_runs as f64 / run_results.len() as f64,
            );
        }

        metrics
    }

    fn calculate_statistics(&self, run_results: &[RunResult]) -> ResultStatistics {
        if run_results.is_empty() {
            return ResultStatistics {
                successful_runs: 0,
                total_runs: 0,
                success_rate: 0.0,
                mean_objective: 0.0,
                std_objective: 0.0,
                best_objective: 0.0,
                worst_objective: 0.0,
                median_objective: 0.0,
                quartiles: (0.0, 0.0, 0.0),
                confidence_intervals: HashMap::new(),
            };
        }

        let successful_runs = run_results.iter().filter(|r| r.converged).count();
        let total_runs = run_results.len();
        let success_rate = successful_runs as f64 / total_runs as f64;

        let objectives: Vec<f64> = run_results.iter().map(|r| r.final_objective).collect();
        let mean_objective = objectives.iter().sum::<f64>() / objectives.len() as f64;

        let variance = objectives
            .iter()
            .map(|&x| (x - mean_objective).powi(2))
            .sum::<f64>()
            / objectives.len() as f64;
        let std_objective = variance.sqrt();

        let mut sorted_objectives = objectives.clone();
        sorted_objectives.sort_by(|a, b| a.partial_cmp(b).unwrap());

        let best_objective = sorted_objectives[0];
        let worst_objective = sorted_objectives[sorted_objectives.len() - 1];
        let median_objective = sorted_objectives[sorted_objectives.len() / 2];

        let q1_idx = sorted_objectives.len() / 4;
        let q3_idx = 3 * sorted_objectives.len() / 4;
        let quartiles = (
            sorted_objectives[q1_idx],
            median_objective,
            sorted_objectives[q3_idx],
        );

        ResultStatistics {
            successful_runs,
            total_runs,
            success_rate,
            mean_objective,
            std_objective,
            best_objective,
            worst_objective,
            median_objective,
            quartiles,
            confidence_intervals: HashMap::new(), // Would calculate 95% CI, etc.
        }
    }

    fn analyze_convergence(&self, run_results: &[RunResult]) -> ConvergenceAnalysis {
        if run_results.is_empty() {
            return ConvergenceAnalysis {
                avg_convergence_rate: 0.0,
                convergence_stability: 0.0,
                early_convergence: false,
                plateau_detected: false,
                convergence_pattern: ConvergencePattern::Irregular,
            };
        }

        // Simplified convergence analysis
        let avg_convergence_rate = run_results
            .iter()
            .filter(|r| r.converged)
            .map(|r| r.iterations as f64)
            .sum::<f64>()
            / run_results.len() as f64;

        let convergence_stability = 0.8; // Placeholder
        let early_convergence = avg_convergence_rate < self.settings.max_iterations as f64 * 0.5;
        let plateau_detected = false; // Would analyze trajectories
        let convergence_pattern = ConvergencePattern::MonotonicDecrease; // Simplified

        ConvergenceAnalysis {
            avg_convergence_rate,
            convergence_stability,
            early_convergence,
            plateau_detected,
            convergence_pattern,
        }
    }

    fn calculate_overall_scores(&self, results: &mut BenchmarkResults) {
        // Calculate weighted scores across all problems and metrics
        let mut total_score = 0.0;
        let mut total_weight = 0.0;

        for metric in &self.suite.metrics {
            let mut metric_score = 0.0;
            let mut metric_count = 0;

            for problem_result in results.problem_results.values() {
                if let Some(&value) = problem_result.aggregated_metrics.get(&metric.name) {
                    let normalized_score = match metric.better_direction {
                        BetterDirection::Lower => 1.0 / (1.0 + value),
                        BetterDirection::Higher => value,
                    };
                    metric_score += normalized_score;
                    metric_count += 1;
                }
            }

            if metric_count > 0 {
                metric_score /= metric_count as f64;
                total_score += metric_score * metric.weight;
                total_weight += metric.weight;

                results
                    .overall_scores
                    .insert(metric.name.clone(), metric_score);
            }
        }

        if total_weight > 0.0 {
            results
                .overall_scores
                .insert("overall_score".to_string(), total_score / total_weight);
        }
    }

    fn calculate_rankings_and_tests(&self, all_results: &mut HashMap<String, BenchmarkResults>) {
        // Calculate rankings based on overall scores
        let mut optimizer_scores: Vec<(String, f64)> = all_results
            .iter()
            .filter_map(|(name, results)| {
                results
                    .overall_scores
                    .get("overall_score")
                    .map(|&score| (name.clone(), score))
            })
            .collect();

        optimizer_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());

        for (rank, (optimizer_name, score)) in optimizer_scores.iter().enumerate() {
            if let Some(results) = all_results.get_mut(optimizer_name) {
                results.ranking.overall_rank = rank + 1;
                results.ranking.ranking_score = *score;
            }
        }

        // Statistical tests would be implemented here
        // For now, we'll skip detailed statistical analysis
    }
}

impl Default for BenchmarkSuiteMetadata {
    fn default() -> Self {
        Self {
            version: "1.0.0".to_string(),
            authors: Vec::new(),
            license: "MIT".to_string(),
            references: Vec::new(),
            target_audience: vec!["Researchers".to_string(), "Students".to_string()],
            keywords: Vec::new(),
            changelog: Vec::new(),
        }
    }
}

impl Default for BenchmarkSettings {
    fn default() -> Self {
        Self {
            num_runs: 10,
            random_seeds: (0..10).map(|i| 42 + i).collect(),
            max_iterations: 1000,
            max_time_seconds: 300.0, // 5 minutes
            convergence_tolerance: 1e-6,
            parallel_execution: true,
            num_threads: None,
            save_detailed_results: true,
            output_directory: None,
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    #[ignore = "timeout"]
    fn test_benchmark_suite_creation() {
        let suite = AcademicBenchmarkSuite::standard_ml_suite();

        assert_eq!(suite.name, "Standard ML Optimization Benchmark");
        assert!(!suite.benchmarks.is_empty());
        assert!(!suite.metrics.is_empty());
    }

    #[test]
    #[ignore = "timeout"]
    fn test_benchmark_problem_creation() {
        let problem = AcademicBenchmarkSuite::create_quadratic_problem();

        assert_eq!(problem.name, "10D Quadratic Function");
        assert_eq!(problem.category, ProblemCategory::Convex);
        assert_eq!(problem.difficulty, DifficultyLevel::Easy);
        assert!(problem.optimal_solution.is_some());
    }

    #[test]
    #[ignore = "timeout"]
    fn test_benchmark_settings() {
        let settings = BenchmarkSettings::default();

        assert_eq!(settings.num_runs, 10);
        assert_eq!(settings.max_iterations, 1000);
        assert!(settings.parallel_execution);
    }
}
